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USDA Ultraviolet Radiation Monitoring and Research Program - Strategic Plan – June 2016 page 1 of 36

USDA Ultraviolet Radiation Monitoring and Research Program

Strategic Plan

(May 1, 2016)

Introduction The U.S. Department of Agriculture (USDA) has long been concerned with the impacts of ultraviolet radiation (UV-B) and climate change on agriculture. High levels of UV-B radiation from the Sun are known to have harmful effects on agricultural crops, forest ecosystems, livestock, and people. The stratospheric ozone layer that blocks most of the harmful UV-B radiation before it reaches the Earth’s surface, has suffered considerable erosion due to man-made chemicals such as chlorofluorocarbons (CFCs) and methyl bromide. Though recovering, the ozone layer still remains susceptible to depletion like those observed over the Arctic in 2011 and again over the Antarctic in 2015. Furthermore, changes in climate and weather patterns can lead to unanticipated variations in the UV-B radiation reaching Earth’s surface, directly and indirectly affecting agriculture. Thus in 1992, the USDA initiated the UV-B Monitoring and Research Program (UVMRP) through legislative authority available to the National Institute for Food and Agriculture (NIFA) [formerly Cooperative State Research Education and Extension Service (CSREES)]. UVMRP is guided by NIFA's mission "To invest in and advance agricultural research, education, and extension to solve societal challenges” and vision to “Catalyze transformative discoveries, education, and engagement to address agricultural challenges". Specifically, UVMRP addresses NIFA Strategic Plan Goal 1.2 to “Advance the development and delivery of science for agricultural, forest, and range systems adapted to climate variability and to mitigate climate impacts”, and The National Global Change Research Plan 2012-2021 to provide a “comprehensive and integrated United States research program to assist the Nation and the world to understand, assess, predict, and respond to human-induced and natural processes of global change.” UVMRP fulfills the above mandates by establishing three program areas.

1. Monitor UV-B radiation at the Earth’s surface. Our monitoring network is designed to provide measurements to characterize UV-B radiation over the entire nation for a long term. In fact, UVMRP is the only source of nationwide surface UV measurements, with data dating back to 1996. The UVMRP data are used to determine the impact of UV-B radiation and photosynthetically active radiation (PAR) on crops, animals, forests, rangeland, and their cumulative effects on the environment and U.S. agriculture.

2. Study the effects of UV-B radiation on agriculture. With collaborators in Colorado State University, Mississippi State University and elsewhere, UVMRP studies the isolated effects of UV-B radiation on agricultural crops, trees, and rangelands, and assesses the combined effects of UV-B radiation with other


climate stress factors such as moisture (drought), temperature, ozone, soil nutrients and CO2.

3. Develop the Climate-Agroecosystem-UV Interactions and Economic (CAIE) system. The agricultural community and decision makers require tools to reliably predict crop yield, to assess optimal management practices and economic impacts. With collaborators from University of Maryland and Colorado State University, UVMRP develops CAIE system that will couple an advanced regional climate model with models of crops and ecosystems and economics. The system will integrate the data from the monitoring network, the results of the effects studies, and satellite observations. This system will allow studies on how climate and crop production interact and how the interaction impacts management practices and agricultural economics.

UVMRP publishes studies on UV-B radiation and related subjects in peer-reviewed publications (128 refereed articles, 119 proceedings articles) and promotes graduate education (eight graduate students and five post-doctoral associates). A list of publications authored and co-authored by the UVMRP is in Appendix B. UVMRP has provided leadership for two national UV workshops and many international conferences (on UV Measurements, Modeling, and Effects; on Ecosystems Dynamics, Agricultural Remote Sensing and Site-Specific Agricultures conferences; and on Remote Sensing and Modeling of Ecosystems for Sustainability). UVMRP scientists also served as co-editors for 37 scientific proceedings, eight book chapters, four books, and four peer-reviewed UV special issues in Journal of Agricultural and Forest Meteorology, Journal of Optical Engineering, and Journal of Photochemistry and Photobiology. This UVMRP Strategic Plan describes the objectives and future activities of the three main program areas for the next five years. A complete background of UVMRP may be found at http://uvb.nrel.colostate.edu.

Objectives

Program Area 1: UV-B Monitoring Network The primary goal of this program area is to maintain and improve the existing network operations. More specifically, the objectives of this program area are as follows. x Maintain existing instrumentation so that data remain high quality and uninterrupted. x Provide high quality data within the UV and visible light spectral bands. x Employ comprehensive calibration and quality control procedures/algorithms. x Provide secondary products derived from the primary radiative measurements. x Establish a UV climatology based on long-term monitoring. x Provide the basic meteorological information as an ancillary dataset. x Detect spatial and temporal (e.g. diurnal and seasonal) patterns in global horizontal UV

irradiance. x Provide open-access datasets for research (e.g. agricultural, atmospheric, biological,

ecological, human health, and material sciences). x Validate radiative transfer models and/or satellite derived UV irradiance at Earth’s surface.


x Provide general UV-related information and public interest products (e.g. UV index). x Explore upgrades to data logging hardware that will allow improved Internet-based polling of

measurements such as same day data monitoring. x Investigate modern instrumentation that is field-hardened for outdoor use which measures

solar radiation from UV to visible to near infrared (290 nm to 1100 nm). Program Area 2: Effects of UV-B on Agriculture The main goal of this program area is to evaluate the response of crops, trees, and rangelands to UV-B radiation and other climate stress factors. This project area has the following objectives. x Evaluate (quantify) the isolated effects of elevated UV-B radiation on agricultural crops,

trees, and rangelands and assess the combined effects of UV-B radiation with other climate stress factors such as moisture (drought), temperature, ozone, soil nutrients and CO2.

x Develop mechanistic algorithms of effects of UV-B, to be incorporated into crop/ecosystem models.

x Make portable instrument clusters available to researchers to enable short-term, site-specific investigations.

Program Area 3: Integrated Assessment System The primary goal of this project area is to develop Climate-Agroecosystem-UV Interactions and Economic (CAIE) system. x Establish the conceptual framework of the CAIE system. x Couple a regional Climate-Weather Research and Forecasting model (CWRF) to crop/tree

models. x Develop an UV module in an ecosystem biogeochemical model DayCent and couple with the

CWRF model. x Develop the economic assessment model to be coupled with CWRF and crop/ecosystem

models.

Future Activities (for FY2016-2020) Program Area 1: UV-B Monitoring Network After decades of considerable erosion, the ozone layer is now protected by the Montreal Protocol on Substances that Deplete the Ozone Layer that reduced ozone depleting substances since the mid-1990s (Mäder et al., 2010; Shepherd and Jonsson, 2008). However, questions remain whether the ozone layer has stabilized or is recovering (WMO, 2015). The present recovery period is still relatively short for a statistically significant trend to emerge (Vyushin et al., 2010), and the year-to-year variability of the ozone layer is still quite large. Indeed, the ozone layer became depleted over the Arctic in March 2011, and again in 2015 over Antarctica. The Antarctic ozone hole in 2015 was the fourth-largest area measured since the start of the satellite


record in 1979, exposing an area of 10.9 million square miles, equivalent to the size of the continent of North America (NASA/Goddard Space Flight Center). Instrumentation From inception in 1992, UVMRP continually monitors UV and visible light in a nationwide network of instruments across 40 sites (36 climatologic and 4 long-term research; Figure 1). The primary goal of this program area is to maintain and improve the existing network operations. The sites are in 27 states, one Canadian province, and the south island of New Zealand. The Network covers 20 ecoregions and span an elevation gradient from 18 meters below sea level to 3400 meters above. The network covers a wide variety of land types: 44 % of the network sites are in active farmland, 17% in rural, 14% in forest, 6% in grassland, 5% in scrubland, 3% above timberline, and 11% in urban areas.

Each of the 36 climatological sites has four primary instruments (Figure 2): an ultraviolet multi-filter rotating shadowband radiometer (UV-MFRSR), a visible MFRSR, a UVB-1 broadband meter, and a PAR (Photosynthetically Active Radiation) sensor. The UV-MFRSR records measurements of 3-minute averages of the total horizontal, direct normal, and diffuse horizontal irradiance at seven selected wavelengths in the

UV-B and UV-A spectral ranges (nominal 300, 305, 311, 317, 325, 332 and 368 nm). Each site also records 3-miniute average measurements of all three solar radiation components (total, direct and diffuse) at six wavelengths in the visible spectral range (nominal 415, 500, 615, 673, 870, and 940 nm) with a VIS-MFRSR to determine solar attenuation due to clouds and aerosols. We continue to use the very stable broadband UVB-1 instrument (280 to 320 nm) because much of the historical record is based on broadband meters, and they are still widely used by researchers. In addition, we measure PAR (400 to 700 nm) with the same sensor that is widely used by agricultural researchers. Temperature, humidity and surface reflectance are also measured at all sites. UVMRP also maintains 4 research sites that have only the instrumentation necessary for the specific long-duration research that is ongoing.

Figure 1. Site locations of the UVMRP monitoring network.

Figure 2. Instrument cluster at a typical site.


These instruments take measurements continuously every 15 seconds to 20 seconds and average them to a three-minute datum. The data are downloaded every day through telephone or Internet to the UVMRP database servers. Any sites that experience connectivity issues are addressed by our staff on the next business day. After the download processes are completed, numerous automated scripts begin applying quality control and calibration procedures to ensure that the data are of the highest standard. Data are then made available on the UVMRP website http://uvb.nrel.colostate.edu/UVB/index.jsf within 36 hours after collection. To preserve data continuity, UVMRP works with the site operators at the institutions hosting the instruments to address problems with connectivity and instrumentation within hours to days. The instruments are aging, and UVMRP will fix failing components in house. We have experienced increased instrument failure in recent years and we have become proficient at refurbishing and replacing instrument components. We maintain a reserve of 6-10 of each of the primary instruments (UV-MFRSR, VIS-MFRSR, UVB-1 and PAR) and a few each of ancillary instruments (temperature/humidity probe, barometer and albedo photometer) to allow quick replacement of damaged or faulty instruments, to minimize downtime and lost data. We also perform preventative maintenance such as upgrading MFRSR radiometer diffusers, filters, and photodiodes, and replacing cables and connectors. Upgrading of the VIS-MFRSR instruments throughout the network is underway, and is expected to be completed over the next three years. Since all these instruments are aging, we are evaluating the feasibility of purchasing and testing new state-of the-art instruments to replace the old. We are investigating alternative sensors for the aging primary shadowband instruments (both UV and VIS), but since there appear to be no suitable substitutes, we will be repairing and upgrading these shadowband instruments for the duration of this program. For the UVB-1 broadband instrument, we have identified an equivalent instrument and plan to test it in a long-term comparison with our UVB-1’s at MLO for at least the next year. Many of the instruments are connected to rural telephone systems, and the telephone-polled systems increasingly lose data from lengthy outages, and thus, we are actively converting to Internet-based communications systems. Each conversion is different due to the proximity (or lack thereof) of an Internet hub at the host facility and their associated cyber-security requirements and connection protocols, and thus each installation requires a site visit by our staff. We have converted most of the network sites with ready access to a hard-wired Internet hub. We developed a method for communicating with our dataloggers using Wi-Fi technology, and converted a few sites to this method. Sites with neither hard-wire nor Wi-Fi connectivity will need cellular technology, which we are investigating for adaptation to our network. Most, if not all, sites should be transitioned from telephone to Internet connectivity within the next three years. Calibration and Quality Control The raw measurements from the instruments must be transformed to usable data using a calibration factor for each instrument. In the past, UVMRP obtained the calibration factor similarly to the procedures set by the National Institute of Standards and Technology (NIST), but due to the declined funding to UVMRP, we have developed an alternate in-house procedure. Before the funding decline, our instruments were regularly calibrated by either the manufacturer, or at the Central UV Calibration Facility at the NOAA Earth System Research Laboratory, recognized as the premier UV calibration lab by the World Meteorological Organization. Our


instruments are now calibrated by these services periodically, and we track the calibration factor through the in-house procedure. The procedure is based on Langley techniques, refined with a new robust cloud screening algorithm, and augmented with the cycling of the instruments through an observation platform at NOAA's Mauna Loa Observatory (MLO) that is used by several other atmospheric monitoring organizations for instrument calibration. In this procedure, the instruments are first placed at MLO to obtain a calibration factor. We began using the procedure in mid-December 2009, and we have cycled through MLO 94% of the UV-MFRSR, 87% of the UVB-1, and 13% of the VIS-MFRSR instruments, many now with repeat placements. We anticipate cycling up to 30 instruments per year through MLO. After being calibrated at MLO, the instruments receive at least monthly updates of the calibration factor using the new Langley procedure with the cloud screening algorithm that better selects measurements for use in calibration. The cloud screening procedure algorithm takes the new day’s data from each site, scans them for cloud free periods, and uses the MODTRAN5 radiative transfer model to calculate clear sky, filter function weighted irradiances (Chen et al 2014). The quality of the Langley process is then confirmed by a quality control technique that compares the spectral instruments (MFRSR) with broadband instruments. The technique uses the synthetic spectrum algorithm applied to the UV-MFRSR measurements to obtain erythemal irradiances averaged over each day of the calibration deployment period (usually about a month) and compares these averages with the UVB-1 broadband instruments’ measurements averaged over the same period. In an ideal world all the mean values should agree, and any disagreements serve as an additional quality control measure for our data collection network. The procedure is difficult to apply to the 940nm channel of the VIS-MFRSR instrument, and we will develop and test an updated method to address this issue. The 940nm channel measures water vapor that is an important greenhouse gas in the atmosphere. The channel is difficult to calibrate in-situ because unlike aerosols, water vapor has non-linear relationship between its amount and its optical depth. We propose a new method that substitutes the water vapor optical depth term with one derived from GPS zenith wet delay retrieval. With concurrent meteorological measurements, the method can infer reasonable reference data for calibration of the channel in-situ. We also plan to monitor the instrument calibration factors using an in-house calibration laboratory that is under development at the UVMRP headquarters. Although not intended to meet the standards of the Central UV Calibration facility, our laboratory will be able to detect significant changes in the instruments. We are designing and testing the accuracy of several calibration procedures (monochromator, lamp stability, dark signal, repeatability, cosine response characterization etc.). Eventually, this facility is expected to be able to monitor the calibration of the network’s UV- and VIS-MFRSRs, and the UVB-1 and PAR radiometers. We also plan to revise the calibration of our UVB-1 radiometers, to address the cosine and azimuthal errors that are not well characterized. With this in-house facility, we will investigate how such errors affect the accuracy of UVB-1 measurements. These calibration factors will also be monitored through additional in-situ processes. One is to use data from analogous instrumentation placed at MLO by other organizations (e.g. NOAA, NASA) as an additional check of the accuracy of our calibration procedures. Another is to use satellite and ground-based measurements of the organizations incorporated into our radiative transfer routines as references


to ensure the data quality. A comprehensive analysis of the procedures and calibration protocols used by UVMRP is presented in Gao et al. 2009. UVMRP applies quality control (QC) procedures to ensure the data are of the highest standard. The QC procedures consist first of a daily automated detection and visual inspection by our staff. Then the new Langley calibrations are performed for all subsequent suitable days. Further examination of the data occurs monthly, when an outlier detection program is run to smooth the Langley intercept voltages and to generate updated in-situ calibrated irradiances. Our staff then examines the Langley voltage intercepts to determine if the data should be flagged with QC codes or if any instrument should be recalled for examination in the calibration lab. The QC codes have three levels to communicate how serious the problem is to the users of our data. We will develop procedures to estimate uncertainties of our data to be made available on the UVMRP website. All primary data and derived products are processed and stored on servers with appropriate specifications. Power back up systems, multiple database copies, supporting tools, and experienced personnel assure the uninterrupted flow of the data stream. Data Products We make all data products available to anyone visiting our web page. The data products are divided into six major categories: Irradiance Data, Derived Products, UV Climatology, Ancillary Instruments, Instrument Characteristics, and Quality Control. The Irradiance Data includes in-situ, MLO, and historical lamp calibrated UV-MFRSR and visible-MFRSR calibrated irradiances and erythemally weighted and PAR irradiances. Derived Products include UV Index; synthetic spectra of UV-A, UV-B, erythemal, Flint, Caldwell and vitamin D weighted irradiances; Instantaneous Optical Depth; Average Optical Depth; and a U.S. UV irradiance estimator. UV Climatology includes Hourly or Daily Sums of UV-A, UV-B, erythemal, Flint, Caldwell and vitamin-D weighted irradiances; Sums Contour Maps over the continental U.S., and U.S. Irradiance Statistics. The Ancillary Measurements provide instrument head internal temperatures, air temperature, humidity, surface reflected irradiance, barometric pressure, electronic offset (bias), cosine corrected voltages, and uncalibrated UV-A and UVB-1 signals in volts. The Instrument Characteristics include UV-MFRSR and visible-MFRSR filter function responses, angular cosine responses, Langley voltage offsets, and instrument deployment history. The Quality Control includes Data Corrections, Quality Control Abbreviations (the definitions of QC flags), Data Processing Procedures, and Quality Control Procedures. The UV Climatology data products were recently developed and made available on the UVMRP web page. The web page has some numerical and graphic displays definable by the user, and we will further develop this interactive feature to allow a more direct path for the user to obtain numerical and graphical display of various data products tailored for their needs. Some examples would include aerosol optical depth, spectral irradiance statistics, and statistics of other products. We will develop additional data products useful as inputs to agricultural and ecosystem models, with flexibility to suit the user’s needs. The data products for models will include time series of UV-B radiation, Flint and Caldwell biological weighting functions, and photosynthetically active radiation (PAR). Time series will be made available at sub daily, daily and weekly time steps, for any specified location or as grids covering the USA. The data products flexible for user needs


will accommodate specific requests by the user. For example, the user will be able to select starting and ending wavelengths to permit spectrum integration over selected spectral ranges. The users studying plant disease for instance may focus on the UV-B spectral range while those studying material/chemical compound degradation may require spectral irradiances at multiple activation wavelengths and/or ranges. We will also develop a spatial UV radiation data product that combines the UV observations from different sources using data fusion/assimilation methodologies (Sun et al 2015). Satellite observations provide daily global UV radiation, while UVMRP provides 3-minute UV ground observations at all available sites. By optimally combining both sources of data and utilizing their advantages, we are obtaining better UV data for the U.S. In addition to use in calibration checks, the combined data may improve the satellite observations in areas near the ground sites by using ground observations (Xu et al. 2010). Our long term goal is to provide the measurement of column ozone, but this requires precise spectral UV irradiance measurements. We will continue to examine various retrieval methods using data measured at MLO and assess the quality of the retrieval. The UVMRP data are used by over 280 national and international organizations (Appendix A), and this success of the program speaks to the effectiveness of the instrumentation, calibration and quality control procedures, and data products. We will continue to develop and refine our monitoring network to ensure that the data remain uninterrupted. Program Area 2: Effects of UV-B on Agriculture UV-B radiation affects agricultural crops in complex interactions with the changing climate. Increasing UV-B radiation appears to cause damage in about 2/3 of over 680 plant cultivars tested. The damaging effects include heritable mutations in DNA, lipid and protein denaturation, and changes to several physiological and growth processes (Lidon et al. 2012). UV-B radiation causes positive effects as well, including improved nutrition, greater hardiness to drought, and increased resistance to oxidative damage from excess light (Wargent and Jordan 2013). The effects of UV-B radiation vary among crop species and even cultivars (Searles et al. 2001, Sullivan et al. 2007). Though much less well understood, UV-B radiation interacts with other environmental factors to affect plant growth in complex ways (Paul and Gwynn-Jones 2003). In several studies, UV-B radiation harmed well-watered plants more than those under drought or high temperature, while it harmed plants fertilized in excess more than those adequately fertilized (Bornman et al 2015). Climate change is forcing trees to move their distribution higher in elevation (Harsch et al 2009), where they face greater UV-B radiation dosages that may affect their ability to survive and establish. UV-B radiation as well as visible light accelerate litter decomposition, possibly stimulating carbon and nutrient cycling to enhance plant production in rangelands of the western U.S. that receive high doses of solar radiation (King et al 2012). The recent discovery of plant UV-B receptor UVR8 and its interaction with other regulatory pathways suggests that UV-B radiation is intricately involved in regulating a plant’s response to its environment, such as defense against pests, disease and hardiness under other stresses (Ballare 2014).


Despite the progress in understanding how crops respond to UV-B radiation, considerable gaps in knowledge remain (Kakani et al. 2003). The effects of UV-B radiation were at times tested under unrealistic light (PAR<300 µmol m-2 s-1) that are now known to produce results that differ from those in the field (Searles et al. 2001). The effects of UV-B radiation combined with other environmental factors remain not well understood, even though crops in the field face many factors combined such as UV-B radiation, heat, drought, changing nutrient availability, and rising CO2 concentration in the atmosphere. Part of this knowledge gap is belowground, where the effects of UV-B radiation on roots are not well understood. Root growth and architecture are critical to modeling crops since they determine how efficiently the crops acquire soil water and nutrients (Lynch 2005). For most crops, these gaps in knowledge prevent the development of functional algorithms for modeling how crops respond to UV-B radiation and other stress factors (Reddy et al. 2003). Though the effects of UV-B radiation are complex, they can be mediated or even exploited toward developing sustainable agriculture under climate change. What is needed are the accurate characterization of the effects of UV-B radiation in isolation for use in modeling, and an understanding of the complex interaction between UV-B radiation and other environmental factors. The goal of this program area is to quantify the effects of UV-B radiation on crops to facilitate the development of quantitative algorithms that can be incorporated into climate-crop simulation models for the Climate-Agroecosystem-UV Interactions and Economic (CAIE) system. With collaborators, UVMRP has examined the effects of UV-B radiation on various aspects of agriculture, from individual crops to rangelands and forests, and from individual organisms to ecosystems as a whole. The effects of UV-B on the growth and nutrition of various crops were examined on barley (Sullivan et al 2002, Sullivan et al 2007), cotton (Gao et al 2003, Kakani et al 2003, Reddy et al 2003, Kakani et al 2004, Reddy et al 2004, Zhao et al 2004, Kakani et al 2005, Koti et al 2006, Lokhande and Reddy 2014, Lokhande and Reddy 2015), trees of southern U.S. (Qi et al 2002, Heisler et al 2003, Grant et al 2006), rangeland plants (Thines et al 2007), nematode (Koti et al 2006), soil arthropod (Beresford et al 2013), and the decomposition of leaf litter (Smith et al 2010). To better serve the needs of researchers, UVMRP continues to provide instruments, data and technical guidance. UVMRP is examining the effects of UV-B radiation and other environmental factors on corn, soybean, and rice, the crops that rank among the top five economically important crops in the U.S. and the world. Working with collaborators in Mississippi State University, the studies on corn are under way, and those on soybean and rice are planned. As the effects of UV-B radiation differs among crop species, each species must be characterized separately for accurate representation in crop models. These studies are designed to produce algorithms to be incorporated into crop models: they identify the physiological processes responsible for the effects of UV-B radiation on yield and biomass, and quantify how the processes respond to various levels of UV-B radiation. The studies use plant growth chambers known as the Soil-Plant-Atmosphere-Research (SPAR) facility at MSU that can control the environment inside over a wide range for atmospheric CO2 concentration, temperature, humidity, and UV-B radiation. They are particularly suited for studying climate change effects and acquiring the necessary functional data that can be used to develop crop models. The chambers are sunlit and provide natural light similar to what the plants


experience in the field (Figure 3). The chambers can be programmed to control the environmental conditions for the duration of a study or for short periods to induce acute responses to environmental factors during critical stages of crop development. Each chamber can independently maintain atmospheric CO2 at both sub-ambient and super-ambient levels. Chambers are also unique in that both canopy and root system are enclosed, allowing a continuous measurement of photosynthesis, respiration and transpiration, and thus a calculation of complete CO2 and water budgets. The root enclosures allow for continuous monitoring of soil environment including root growth and depth profiles. Each chamber excludes UV-B radiation with transparent plastic filters and supplements UV-B radiation in a controlled manner with lamps. More than 500 pieces of information are recorded from these chambers electronically every 15 minutes, 24 hours per day, for the duration of experiments using more than 200 sensors and instruments. Using the SPAR facility, we will expose several cultivars of each crop to two general sets of experiments. The first experiment will quantify the isolated effects of UV-B radiation, by exposing the plants to five levels of UV-B (0, 4, 8, 12, and 16 kJ m-2 day-1) to generate UV-B dose response curves during the critical periods of crop life cycle. This experiment will first identify the physiological processes affected by UV-B radiation, such as leaf expansion or timing of flowering, that are represented in most crop models. The experiment will then develop indices to quantify the effects of UV-B radiation on each process. The indices are commonly used to incorporate experimental results into crop models. To develop the indices, we will first define potential process rates or amounts under optimal growth conditions without UV-B radiation. The levels of UV-B radiation will be increased and the plant tracked for responses. The plant responses in critical processes will then be normalized as indices ranging from 0 to 1 as a function of UV-B radiation, from completely limiting to unchanged. The resulting dose response curve will be fitted with a simple linear or curvilinear model with parameters specific to each process. The indices can then be incorporated into crop models for each process to reduce their potential rates and amounts before environmental stressors take effect in the model. The second experiment will examine the effects of UV-B radiation combined with stress factors of temperature, drought, and CO2 at specific growth stages, using a factorial design with two to three levels for each factor. The plants will be monitored for responses in physiology, growth, and chemistry. For physiology, measurements will include photosynthesis parameters and fluorescence. For growth, measurements will include plant height, dry weight, numbers of leaves, and leaf area. Leaf anatomical features will also be studied using light and scanning electron microscopy. Roots will be measured for architectural variables including cumulative length, surface area, average diameter, length per volume, and branching. For chemistry, measurements will include total and component chlorophyll and UV-B radiation screening compounds such as phenolics. The experiments cannot be run concurrently because the space in SPAR facility cannot accommodate adequate replicates for each treatment for each cultivar, and we will examine each crop sequentially.

Figure 3. SPAR facility at MSU.


We are currently examining the responses of corn to UV-B radiation in isolation and in combination with other stress factors. For isolated effects, the results show that corn is sensitive to UV-B radiation even at ambient levels, with some cultivars more so than others. The results also show that corn has lower biomass and yield under UV-B, primarily from reduced leaf area and photosynthesis rate per leaf area. These results are currently being used to develop an algorithm for incorporation into a crop model DSSAT (Decision Support System for Agrotechnology Transfer). For the combined effects, the corn cultivars were exposed to hot and cold temperatures, elevated CO2, and drought. The results are being analyzed. UVMRP will also examine the impacts of UV-B radiation on trees, with a focus on economically valuable subalpine trees of western U.S. We will examine how subalpine tree species respond to UV-B radiation and if the responses are predictable from the species traits. Climate change is forcing tree distribution to move upward in elevation, and once there the trees must contend with greater dose of UV-B radiation. The trees can respond by investing in UV-B resistance, with strategies that include adjustments in leaf thickness, leaf chlorophyll content and concentration of UV absorbing compounds. In responding, the trees must divert the necessary resources away from other processes such as growth and reproduction. Some tree species will likely respond more and some will not, and this species difference in the responses may be predictable from their traits, those that reflect the species differences in fundamental strategies for resource investment in nutrients and energy. By examining the relationship between tree species traits and their responses to UV-B radiation, we aim to generate relationships generalizable to be incorporated into models of forest ecosystems. The study will be carried out in a transplant experiment in the Rocky Mountains, where native tree species will be planted at three elevations, with and without UV-B blocking screens to test their responses to UV-B radiation. The trees will be tracked for physiological responses, including photosynthetic capacity, growth and morphological characters, tissue chemistry, and leaf optical properties. We will study the effects of UV-B radiation on rangelands as well. The rangelands of western U.S. receive high doses of UV-B radiation that can affect how nutrients cycle in the system. UV-B radiation can change leaf morphology and nutrient concentration of rangeland grasses, and this in turn can affect the chemistry of litter once the grasses senesce and become litter. How the litter degrades and releases nutrients is controlled by the litter chemistry, and is an important process represented in models of grassland ecosystems. We will use UV-B blocking filters on rangeland fields to study this process, and incorporate the results into grassland models. Program Area 3: Integrated Assessment System U.S. agriculture is under a challenge to develop sustainably in the face of changing climate, population, and policy. Agriculture in U.S. Corn and Cotton Belts is already being altered by climate change, and even more severe consequences in the future are projected by USDA and the U.S. Global Change Research Program. Climate change is also expected to make extreme weather more frequent, and extreme weather such as drought and storms already cause agricultural damage in the tens of billions of dollars. U.S. agriculture must also face a growing population with changing preferences for diet that accompanies economic growth, and agriculture must meet the increasing demand for food and energy while competing for increasingly scarce water. Policy is dramatically changing U.S. agriculture as well, as


agriculture is being transformed by the pressure to reduce U.S. fuel imports and U.S. carbon footprint, transferring almost 40% of the U.S. corn crop to the energy sector. These trends make clear that developing sustainable agriculture depends on environmental constraints, economic growth, and policy. It is thus necessary to develop a fully coupled climate-crop growth modeling system that simultaneously integrates the interactions between weather, soil, plant processes, agricultural practices, and economics. With collaborators from the University of Maryland and Colorado State University, UVMRP is developing the Climate-Agroecosystem-UV Interactions and Economic (CAIE) system to predict the interactions of climate, crop, and economics. CAIE is an integrated agricultural impact assessment system that fully couples the models for climate, UV radiation, crop growth, and economics, while assimilating satellite and in-situ observations. Its advanced model infrastructure will be able to quantify the responses of crop yield and quality to the impacts of important environmental stressors including temperature, moisture (drought), nutrients, UV radiation, CO2 concentration, aerosols and other air pollutants. It will be capable of simulating not only the growth and yield of economically significant crops (cotton, corn, soybean, wheat and rice), but also forests, grasslands, grazing lands, deserts, and rangelands. CAIE will be coupled to an economic assessment model that predicts total factor productivity change (TPFC) based on key climate indices. This system will be able to provide improved prediction of agricultural productivity that leads to better understanding of sustainability in the face of climate change. The system will have the ability to do policy analysis, as well as economic evaluation of land use and management practices. Ultimately, the system will provide the science support not only for U.S. policy makers to establish necessary incentives and safety nets for producers, but also for decision makers to measure potential risks, determine optimal practices, design effective policies, and identify mitigation and adaptation strategies.

Figure 4. Climate-Agroecosystem-UV Interactions and Economic (CAIE) system


The integrated CAIE system takes advantage of the existing capabilities of a regional Climate-Weather Research and Forecasting model (CWRF) and couples it with crop models, an ecosystem model, and an economic model, assimilating data from solar radiation transfer models (Figure 4). The CAIE system is centered on CWRF, a state of the art model that comprehensively simulates the processes behind regional climate and weather (Liang et al 2012). It runs on horizontal grids and is capable of simulating terrestrial hydrology, precipitation, and radiation, all of which are critical for crop growth. CWRF will be coupled to other models through Conjunctive Surface-Subsurface Process model (CSSP) that simulates canopy effects, soil temperature/moisture distributions, terrestrial hydrology variations, and land-atmosphere exchanges of water, heat, and moment fluxes. Several models will be coupled with CWRF: x GOSSYM (a shortened scientific name for the genus of cotton, Gossypium) is a mechanistic

model developed by USDA to simulate cotton growth given soil, weather, and management practices (McKinion et al. 1989).

x DSSAT (Decision Support System for Agrotechnology Transfer) is a crop model capable of simulating many species individually, based on the genetic characteristics of each species (Jones et al 2003).

x DayCent-UV (Daily version of Century model with UV module) is a modified version of a widely used terrestrial ecosystem biogeochemistry model DayCent, which simulates photosynthesis, plant production, carbon allocation, autotrophic and heterotrophic respiration, decomposition, evaporation, transpiration, phenology, disturbances such as fire and grazing, and management practices such as fertilizer use and irrigation (Del Grosso et al 2001).

x TUV (Tropospheric Ultraviolet and Visible radiation model), is a well-tested radiation transfer model developed by National Center for Atmospheric Research (Mandronich 1993).

x CUV (3-D Canopy UV radiation transfer model) is a canopy level radiation transfer model that predicts UV-B within and below a canopy.

x FASOMGHG (Forest and Agricultural Sector Optimization Model – GreenHouse Gasses version) is an economic model used by EPA that simulates land allocation and the economic impacts of changing land allocation and production practices.

CAIE will incorporate the ongoing NASA Land Data Assimilation System (LDAS) capabilities to improve observational data integration in initialization and subsequent prediction of climate-crop interactions. UVMRP has coupled CWRF with the cotton growth model GOSSYM, with results compared against reported yields at the regional scale (Xu et al. 2005). The coupled model also incorporated the results from studies in UVMRP Program Area 2 on the effects of UV-B on cotton growth (Reddy et al 2003). The coupled model thus has the capability of simulating stress factors such as temperature, precipitation, CO2, and solar UV-B radiation on plant growth and productivity. The simulation results of the coupled model showed that the model reproduces well the observed temporal variations and spatial distributions of the U.S. cotton yields. We are in a process of coupling CWRF with a version of DSSAT for corn, and plan to couple a version of DSSAT for soybean, rice and wheat. The coupling requires that we modify DSSAT and to be compatible with CWRF: DSSAT must be able to simulate the effects of UV-B on crops, to run on a gridded system, to accommodate modularization, and to pass information usable to and from CWRF. We will take the study results from Program Area 2 for corn, specifically the indices developed from dose response curves, and incorporate them into the corn module of


DSSAT. DSSAT simulates each crop as separate modules, and we will repeat this process for soybean as results become available. As DSSAT runs at a single site and is not suitable for calculations in CWRF that occurs over geographic grids, we have modified DSSAT to run on the grids. We will further modify DSSAT to pass information to and from CWRF, by allowing DSSAT to take inputs from CWRF, simulate certain process in a manner compatible, and to return outputs usable to CWRF. DSSAT is also coded in FORTRAN 77, not compatible with the CWRF’s modular design, and we will re-code DSSAT in FORTRAN 90. The coupling procedures will be repeated in recoding DSSAT modules for corn, soybean, rice, and wheat. Although the modules share some common features, they differ in the growth, phenology, and root development parts and require to be recoded independently. The coupled system will be tested by retrospectively simulating crop yields over various U.S. regions with historical climate data. We will compare the simulations with both observational data and model simulations from other studies to attribute crop yields to key environmental factors and stressors, including temperature, precipitation, soil moisture, UV-B radiation, and CO2 fertilization. UVMRP will develop an UV version of DayCent. DayCent is a well-tested model that simulates variety of ecosystems including grasslands, forests, and cultivations for energy crops such as switchgrass, Miscanthus, and sugarcane. We will develop an UV-B sub-module in the DayCent model to incorporate the mechanisms related to UV-B photodegradation and microbial inhibition (King et al 2012), and the processes responsible to the effects of UV-B radiation on plant morphology and chemistry (Paul and Gwynn-Jones 2003). The DayCent-UV model will be calibrated and validated against the experimental data from the Program Area 2, and 10-year litter decay data, and other observed ecosystem variables at multiple western U.S. sites. Model simulations have uncertainties, and we will investigate their source using DayCent-UV. Simulations at regional scale have uncertainties because available data do not contain enough information to constrain the large number of parameters that control model simulations. The uncertainties can be lowered by reducing the number of parameters to be constrained, and the number of parameters can be reduced by examining how parameters are related to each other and how they vary across space. We will examine parameter relatedness and variation using an optimization procedure that assimilates multiple types of data. The procedure minimizes an objective function over iterative model runs, using various types of data, including gas fluxes and elemental pool sizes. It tunes the model parameters to those that best fit the data and analyzes the sensitivities of and the relationships between the parameters. We are currently applying this procedure to parameterize DayCent at site level to simulate ecosystem dynamics of several western U.S. sites. This will allow us to examine how parameters are related to each other, and what observations will best constrain them. We will extend this procedure over space to examine how the uncertainties will change across different geographies. We will also couple DayCent-UV with CWRF to examine the effects of UV-B radiation on ecosystems and how they in turn impact climate. The coupled system will manage the exchange of weather information produced from CWRF and agricultural output of the DayCent-UV model at daily time steps. The procedure here will be similar to those for DSSAT. Like DSSAT, DayCent is a site model and is written in a language incompatible with CWRF. We will modify the DayCent-UV to accept regional scale weather data (e.g. temperature, precipitation, wind speed, vapor pressure deficit, and UV-B radiation). We will also develop a method to incorporate management strategies from national inventories.


These coupled models must accurately simulate UV-B radiation that the plants experience, and we will incorporate into CWRF the models that simulate radiation transfer through the atmosphere (TUV) and through plant canopies (CUV). TUV simulates how UV-B radiation passes through the atmosphere using observed meteorology and aerosol information from historical reanalysis and the satellite datasets. Because TUV only predicts UV radiation above canopy, TUV will be further integrated with the CUV model to predict horizontal UV irradiances at any height located within or under vegetation canopies. Once integrated, we will test the model against UVMRP data through a retrospective simulation over decade time scales. Moreover, TUV and CUV incorporated into CWRF will integrate near-real time meteorological conditions and NASA satellite assimilated data to retrieve UV-B radiation covering the entire U.S. from 1979 onward. Decision making in agriculture must evaluate the economic consequence at the regional or national level from the predicted responses of agricultural production to climate variability/change. We will thus incorporate an economic model FASOMGHG into CAIE system. FASOMGHG simulates the biophysical and economic processes that determine technical, economic, and environmental implications of bioenergy production, climate change and policy intervention. It is used by EPA to study climate change and biofuel issues, and has predicted the developments that occurred with the implementation of the renewable fuel standard. We will expand it to reflect municipal and energy sector demands for water, considering aquifer depth, pumping cost, wind energy, and solar power. The economic dynamics also reflect the changes in resource allocation and energy and land use at relatively longer time and spatial scales than represented in a regional model. We will thus develop a feasible approach that links CWRF-CROP predicted climate and crop yield distributions at the county level to the statistical economy model of aggregate agricultural productivity at the national level. We will look for the correlation between total factor productivity change (TFPC) and key climate indices and gain the physical understanding of their relationships. We will then develop a multivariate model for TFPC and climate indices to predict TFP growth, and apply this regression model to regional climate changes projected by the CWRF-CROP model to predict the potential trends of future U.S. agricultural TFP. The outcome of this research will lead to an interdisciplinary approach for developing an integrated system model infrastructure CAIE to achieve a credible and quantitative assessment of key stress factors, climate feedbacks and economic impacts for U.S. agriculture, and consequently predict the likely changes in agricultural productivity in a changing climate. For the economic model, we are making correlation analyses of U.S. agricultural TFPC, daily precipitation and temperature from 1979 to 2005. The current results show precipitation and temperature have significant impacts on U.S. agricultural TFPC. The CAIE system by design incorporates models for crop growth and other vegetation life cycle processes, as well as their living environments. Thus, the system is a unique tool that is capable of, and readily applicable to, providing fundamental scientific bases for effective planning and control of the manageable and unmanageable factors of ecosystems. The CAIE system, when fully developed, will facilitate addressing numerous scientific issues, including, but not limited to, the following:


x How and to what extent do weather fluctuation, climate variability, elevated CO2, pollutants and UV radiation affect crop yields and other ecosystem components?

x What are the roles for agricultural practices to help regulate and redistribute greenhouse gas emissions?

x What are the ranges of uncertainty for predicting climate change impacts on agricultural production, by which policymakers can make realistic decisions for sustainable agricultural development?

x How does climate change interact with management practices to affect degradation of soil and water resources, a major challenge for sustainable agriculture, and will such resource degradation significantly impact the risks faced by agricultural producers?

x How can we develop institutional capacities, based on this modeling effort, to address agricultural issues that cross state and national boundaries?


Appendix A

Sampling of UVMRP Data Users

purple italic text indicates user where UVMRP monitoring site is located (31)

K-12 Educational Institutions (6) Binks Elementary School, Wellington, FL Dakota Ridge High School, Littleton, CO Geronimo Creek Observatory (GCO), Seguin, TX Seguin High School, TX St. Theresa School, Austin, TX Wiscasset High School, ME

National Educational Institutions (134)

UV-B Project, Colorado State University Bloomsburg University, PA Boston University, MA Brazosport College, Lake Jackson, TX City University of New York (CUNY), Graduate Center Claremont McKenna College, CA Clarkson University, Potsdam, NY Colorado School of Mines Colorado State University Colorado State University, Atmospheric Sciences Colorado State University, High Energy Physics Group, Auger Project Colorado State University, Natural Resource Ecology Laboratory (NREL) Cornell Agricultural Experimental Station Cornell University, Ithaca, NY Creighton University, Omaha, NE Dept. Atmospheric & Planetary Sciences, Hampton University, Hampton, VA Desert Research Institute (DRI), Reno, NV Desert Research Institute, Storm Peak Laboratory, CO Drexel University, Philadelphia, PA Duke University, Durham, NC Elon University, NC Fontbonne University, St. Louis, MO Georgia Institute Of Technology, Atlanta, GA H.B. Owens Science Center, Lonham-Seabrook, MD Hampton University, VA Harvard University, Cambridge, MA Howard University, Washington, DC Humboldt State University, Arcata, CA Illinois State Water Survey, Champaign/Urbana, IL Institute for Earth Science Research and Education (ISERE), PA Iowa State University Jackson State University, MS Louisiana State University Louisiana State University, Agricultural Center Loyola University, New Orleans, Louisiana Massachusetts Institute of Technology (MIT) Miami University, OH


Michigan State University Michigan Technological University (MTU) Middlebury College, Geography Department, VT Mississippi State University Montana State University, Billings National Center for Atmospheric Research (NCAR), Boulder, CO New Mexico State University New York University (NYU) Normandale Community College, Bloomington, MN North Carolina State University (NCSU) North Dakota Agricultural Weather Network (NDAWN), Fargo North Dakota State University (NDSU) Oregon Health and Science University (OHSU), Portland Oregon Health and Science University, Public Health Department Oregon State University Pennsylvania State University Plant Research Laboratory, Michigan State University (DoE) Purdue University, IN Sacramento State, CA Siena College, Loudonville, NY Southern Illinois University, Carbondale Southern University and A&M College, Baton Rouge, LA Stanford University, CA State University of New York (SUNY), Albany, Atmospheric Sciences Research Center State University of New York (SUNY), Buffalo Texas A&M University, Walden (IE) Texas Lutheran University Texas Southern University Texas State University Texas Tech University Texas Tech University, The Institute of Environmental and Human Health (TIEHH) Trinity University, San Antonio, TX University Corporation for Atmospheric Research University of Alabama Huntsville University of Alaska University of Alaska Fairbanks University of Alaska Fairbanks, Geophysical Institute University of Arkansas University of California University of California at San Diego University of California Berkeley University of California Davis University of California Davis, Bodega Marine Laboratory University of California Davis, Climate Center University of California Davis, DBS Greenhouses University of California Los Angeles (UCLA) University of California San Diego University of California, Desert Research and Extension Service University of California, San Diego University of Colorado Boulder University of Florida University of Florida Gainesville


University of Georgia University of Hawaii, Manoa University of Houston, Clear Lake, TX University of Houston, Houston, Texas University of Idaho University of Idaho BAE University of Illinois University of Illinois at Chicago University of Illinois Urbana-Champaign University of Illinois, Biological Sciences University of Kansas, Environmental Laboratory University of Louisville, KY University of Maine, Cooperative Extension University of Maryland University of Michigan University of Michigan - College of Engineering University of Michigan Biological Station (UMBS) University of Michigan, Air Quality Lab University of Minnesota University of Nebraska University of Nebraska Lincoln University of New Hampshire University of New Mexico University of North Carolina, Chapel Hill University of North Carolina, Geography Department University of Phoenix, AZ University of Rhode Island, Institute for Archaeological Oceanography (IAO) University of South Florida University of Texas at El Paso (UTEP) University of Texas at El Paso Environmental Science and Engineering University of Texas at El Paso, Physics Department University of Utah University of Vermont University of Washington University of Wisconsin University of Wisconsin Madison University of Wisconsin Milwaukee University of Wisconsin, ESS 205 University of Wisconsin, Marathon County Utah State Climate Center Utah State University Logan, UT Utah State University Washington State University Washington University, St. Louis, MO Wye Research and Education Center, MD Yale University

International Educational Institutions (18)

Dublin Institute of Technology Javeriana University, Colombia Kurchatov Institute of Atomic Energy (KIAE), Russia Laboratory of Atmospheric Physics, Thessaloniki, Greece Lomonosov Moscow State University


Moscow State University, Meteorological Observatory (IMP KIAE), Russia Universidad Autonoma de Baja California, Mexico Universidad Mayor de San Andres, La Paz, Bolivia University of Calgary, Canada University of Catania, Italy University of Central Las Villas, Cuba University of Otago, New Zealand University of Rome "La Sapienza", Italy University of Saskatchewan University of Sheffield, England University of Southern Queensland, Australia University of Stellenbosch, South Africa University of Victoria, British Columbia, Canada

National Governmental Agencies (51)

ARS USDA CUCF - Central UV Calibration Facility, NOAA, Boulder, CO EPA Fermilab, Batavia, IL Florida Department of Transportation Fort Peck Tribes, MT Grand Canyon National Park, AZ Hamel, Minnesota Illinois Natural History Survey, Champaign, IL NASA NASA Ames Research Center NASA GISS - Goddard Institute for Space Studies at Columbia University, NY NASA GSFC - Goddard Space Flight Center, Greenbelt, MD NASA Langley Research Center, Analytical Services and Materials, Hampton, VA NASA Langley Research Center, Hampton, VA NASA, Yorktown, Virginia National Park Service New York State Department of Health, Wadsworth Center NHRC - Naval Health Research Center, San Diego, CA NOAA NOAA GMD NOAA, Boulder, Colorado NOAA/ARL/SRRB NOAA/National Weather Service NOAA/NWS/NCEP/Climate Prediction Center NRC Associate EPA-ERD Athens, GA NRL - Naval Research Lab, Washington, DC PNNL - Pacific Northwest National Laboratory, Richland, WA SERC - Smithsonian Environmental Research Center, Edgewater, MD South Florida Water Management District Southern Arizona Veterans Administration Health Care System SRNL - Savannah River National Laboratory, DoE SSC - Stennis Space Center, MS TCEQ - Texas Commission on Environmental Quality US Air Force US Army US Department of Defense


USDA USDA Agricultural Research Service USDA Animal and Plant Health Inspection Service USDA ARS Air Quality Research Unit, Raleigh, NC USDA ARS, Beltsville, MD USDA ARS, El Reno, OK USDA ARS, University of California, Davis USDA Forest Service USDA Forest Service, Syracuse, NY USDA Fort Collins USDA, APHIS, MWS, Billings, MT USDA, APHIS, PPQ, CPHST USGS Vermont Department of Environmental Conservation

International Governmental Agencies (15)

Alexandra, New Zealand Australian Radiation Protection and Nuclear Safety CNR-IBIMET - Institute of Biometeorology – National Research Council, Rome, Italy CSIRO - Australia Commonwealth Scientific and Industrial Research Organisation Environment Canada Finnish Meteorological Institute German Meteorological Service [Deutscher Wetterdienst] INRNE - Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences Kiev, Ukraine Meteorologia Nafarroa - Pamplona, Spain MSC - Meteorological Service of Canada NRPA - Norwegian Radiation Protection Authority Solar Simulator, Finland Tokyo, Japan World Ozone and Ultraviolet Data Center (WOUDC), Toronto, Canada

National Commercial Enterprises (42)

3M - Saint Paul, MN Air Resource Specialists, Inc. Alternative Energy Andersen Corporation APA Optics, Inc. Apogee Instruments ArborAmerica, Inc. BASF Corporation, Florham Park, New Jersey Blue Ridge Growers, Stevensburg, VA Cooperative Educational Services, Poughkeepsie, New York Dragonfly Beach [private swimming area in northeast Wisconsin] ENSCO Inc. Glasshouse Specialties Inc., Bella Vista, AR [greenhouse builder] Guardian Industries HR Textron Huntsman Cancer Institute HyMetCo [WA consulting firm in water-related projects in western US] Lullhaven Corporation Metronet, Indiana


Moss Landing Marine Labs Mount Sinai Hospital New Day Farms, Bethesda, MD NIRRI - Non-Ionizing Radiation Research Institute, Tucson, AZ Northrop Grumman Corp., Unmanned Systems Northrop Grumman Corporation Northrop Grumman Space Technology (TRW) Parson Consulting, Chicago, IL [finance and business support functions for Global 1000 companies] Parsons Consulting Group, Somerville, MA [integrated planning for schools, colleges and universities] PPG Industries, Inc. RainWise Inc. S&K Technologies Sequoia Health and Fitness, Inc. SMT Inc. Solar Light Co. Inc. Spectral Sciences Inc. Spottswood Companies, Marathon, Florida StellarNet The Dow Chemical Company TRJ Environmental uvscience.com Wattminder, Sunnyvale, CA WCHOB - Women and Children's Hospital of Buffalo

International Commercial Enterprises (16)

Bosch Security System Inc. C.Venosa, engineer Elven Elettronica S.A.S., Italy GmbH Hej - Sweden ABB Asea Brown Boveri Ltd. IMP - Mexican Petroleum Institute, Mexico City Information Technology Company, Iran Kipp & Zonen Main Radio Meteorological Centre, Moscow, Russia MPI-Meteorology Bundesstrasse 53 20146 Hamburg Research and production firm IMT-Center, Moscow Russia Swisscom, Grenchen, Switzerland Transport Research Laboratory, Vodafone Libertel B.V., Netherlands Tyler Research Corporation Unilever Research Zeus International


Appendix B

UVMRP Publications

Program Area 1: UV-B Monitoring Network 2016 Bai, K., N. Chang, and W. Gao. 2016. Quantification of relative contribution of Antarctic ozone

depletion to increased austral extratropical precipitation during 1979-2013. Journal of Geophysical Research: Atmospheres, 121, 1459-1474. doi: 10.1002/2015JD024247

2015 Chen, M., J. Davis, Z. Sun, and W. Gao. 2015. Two-stage reference channel calibration for

collocated UV and VIS Multi-Filter Rotating Shadowband Radiometers. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability XII, 9610, 96100L. doi: 10.1117/12.2185500

Sun, Z., J. Davis, and W. Gao. 2015. Combined UV Irradiance from TOMS-OMI satellite and UVMRP ground measurements across the continental US. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability XII, 9610, 961004. doi: 10.1117/12.2188760

2014 Chen, M., J. Davis, and W. Gao. 2014. A new cloud screening algorithm for ground-based

direct-beam solar radiation. Journal of Atmosphere and Oceanic Technology, 31(12), 2591-2605. doi: 10.1175/JTECH-D-14-00095.1

Liu, C., M. Chen, R. Shi, and W. Gao. 2014. Retrievals of aerosol optical depth and total column ozone from Ultraviolet Multifilter Rotating Shadowband Radiometer measurements based on an optimal estimation technique. Frontiers of Earth Science, 8(4), 610-624. doi: 10.1007/s11707-014-0455-6

2013 Tang, H., M. Chen, J. Davis, and W. Gao. 2013. Comparison of aerosol optical depth of UV-B

monitoring and research program (UVMRP), AERONET and MODIS over continental United States. Frontiers of Earth Science, 7(2), 129-140. doi:10.1007/s11707-013-0376-9

2012 Chen, M., J. Davis, H. Tang, Z. Gao, and W. Gao. 2012. A Multi-Channel Calibration Method

for Multi-Filter Rotating Shadow-band Radiometer. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability IX, 8513, 851305. doi: 10.1117/12.929454

Gao, Z., W. Gao, and N. Chang. 2012. Spatial Statistical Analyses of Global Trends of Ultraviolet B Fluxes in the Continental United States. GIScience & Remote Sensing, 49(5), 735-754. doi: 10.2747/1548-1603.49.5.735

2011 Bai, K., C. Liu, R. Shi, and W. Gao. 2011. Comparison of aerosol optical depth over northern

China derived from ground-based measurements and MODIS. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability VIII, 8156, 81560I. doi: 10.1117/12.891045

Chen, Y., R. Shi, C. Liu, Y. Chen, and W. Gao. 2011. The evaluation of the applicability of MODIS AOD product in the lower and middle reaches of Yangtze River. Proceedings of


SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability VIII, 8156, 81560K. doi: 10.1117/12.8929367

Gao, Z., X. Xie, W. Gao, and N. Chang. 2011. Spatial analysis of terrain-impacted photosynthetic active radiation (PAR) using MODIS data. GIScience & Remote Sensing, 48(4), 501-521. doi: 10.2747/1548-1603.48.4.501

Liu, C., Y. Li, W. Gao, R. Shi, and K. Bai. 2011. Retrieval of columnar water vapor using multispectral radiometer measurements over northern China. Journal of Applied Remote Sensing, 5(1), 053558. doi: 10.1117/1.3647483

Zhong, H., R. Shi, C. Liu, and W. Gao. 2011. The modification of the abnormal remote sensing data from the DVB-S system based on MODIS. Remote Sensing for Land & Resources, 22(1), 73-76. doi: 10.6046/gtzyyg.2011.01.14

2010 Gao, Z., W. Gao, and N. Chang. 2010. Comparative analyses of the ultraviolet-B flux over the

continental United State based on the NASA total ozone mapping spectrometer data and USDA ground-based measurements. Journal of Applied Remote Sensing, 4(1), 043547. doi: 10.1117/1.3507249

Gao, Z., W. Gao, and N. Chang. 2010. Detection of Multidecadal Changes in UVB and Total Ozone Concentrations over the Continental US with NASA TOMS Data and USDA Ground-based Measurements. Remote Sensing, 2(1), 262-277. doi: 10.390/rs2010262

Gao, Z., and W. Gao. 2010. Comparative analysis of UVB exposure between Nimbus 7/TOMS satellite estimates and ground-based measurements. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability VII, 7809, 78090Q. doi: 10.1117/12.858452

Zhang, H., H. Huang, A. Lim, R. Holz, S. Dutcher, F. Nagle, L. Gumley, J. Wang, R. Shi, and W. Gao. 2010. Analysis and characterization of the synergistic AIRS and MODIS cloud-cleared radiances. Frontiers of Earth Science in China, 4(3), 363-373. doi: 10.1007/s11707-010-0023-7

2009 Corr, C. A., N. Krotkov, S. Madronich, J. R. Slusser, B. Holben, W. Gao, J. Flynn, B. Lefer, and

S. M. Kreidenweis. 2009. Retrieval of aerosol single scattering albedo at ultraviolet wavelengths at the T1 site during MILAGRO. Atmospheric Chemistry and Physics, 9(15), 5813-5827. doi: 10.5194/acp-9-5813-2009

Flint, S. D., R. J. Ryel, T. J. Hudelson, and M. M. Caldwell. 2009. Serious complications in experiments in which UV doses are effected by using different lamp heights. Journal of Photochemistry and Photobiology B: Biology, 97(1), 48-53. doi: 10.1016/j.jphotobiol.2009.07.010

Krotkov, N., G. Labow, J. Herman, J. Slusser, R. Tree, G. Janson, B. Durham, T. Eck, and B. Holben. 2009. Aerosol column absorption measurements using co-located UV-MFRSR and AERONET CIMEL instruments. Proceedings of SPIE, Ultraviolet and Visible Ground- and Space-based Measurements, Trace Gases, Aerosols and Effects VI, 7462, 746205. doi: 10.1117/12.826880

Liu, C., W. Gao, Z. Gao, and R. Shi. 2009. Atmospheric correction model of Landsat images. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability VI, 7454, 745412. doi: 10.1117/12.824230

Sarkissian, A., and J. Slusser. 2009. Water vapor total column measurements using the Elodie Archive at Observatoire de Haute Provence from 1994 to 2004. Atmospheric Measurement Techniques, 2(2), 319-326. doi: 10.5194/amt-2-319-2009


Smith, W. K., W. Gao, and H. Steltzer. 2009. Current and future impacts of ultraviolet radiation on the terrestrial carbon balance. Frontiers of Earth Science, 3(1), 34-41.

2008 Hicke, J. A., J. Slusser, K. Lantz, and F. G. Pascual. 2008. Trends and interannual variability in

surface UVB radiation over 8 to 11 years observed across the United States. Journal of Geophysical Research: Atmospheres, 113(D21), D21302. doi: 10.1029/2008JD009826

Lantz, K., P. Disterhoft, J. Slusser, W. Gao, J. Berndt, G. Bernhard, R. Booth, J. Ehramjian, L. Harrison, G. Janson, P. Johnston, P. Kiedron, R. McKenzie, M. Kimlin, P. Neale, M. O'Neill, V. V. Quang, G. Seckmeyer, T. Taylor, S. Wuttke, and J. Michalsky. 2008. The 2003 north American interagency intercomparison of ultraviolet spectroradiometers, part A: scanning and spectrograph instruments. Journal of Applied Remote Sensing, 2(1), 023547.

Taylor, T. E., T. S. L'Ecuyer, J. R. Slusser, G. L. Stephens, and C. D. Goering. 2008. An operational retrieval algorithm for determining aerosol optical properties in the ultraviolet. Journal of Geophysical Research: Atmospheres, 113(D3), D03201. doi: 10.1029/2007JD008661

Xie, X., W. Gao, and Z. Gao. 2008. A Method for Estimating the Incident PAR on Inclined Surfaces. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability V, 7083, 70830P. doi: 10.1117/12.791695

Xie, X., W. Gao, and Z. Gao. 2008. Estimation of Land Photosynthetically Active Radiation in Clear Sky Using MODIS Atmosphere and Land Products. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability V, 7083, 70830O. doi: 10.1117/12.791693

2007 Adler-Golden, S. M., and J. R. Slusser. 2007. Comparison of Plotting Methods for Solar

Radiometer Calibration. Journal of Atmospheric and Oceanic Technology, 24(5), 935-938. doi: 10.1175/JTECH2012.1

Kerr, J. B., and J. M. Davis. 2007. New methodology applied to deriving total ozone and other atmospheric variables from global irradiance spectra. Journal of Geophysical Research: Atmospheres, 112(D21), D21301. doi: 10.1029/2007JD008708

Wang, X., W. Gao, J. Davis, B. Olson, G. Janson, and J. Slusser. 2007. Dependence of erythemally weighted UV radiation on geographical parameters in the United States. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability IV, 6679, 667903. doi: 10.1117/12.735284

Zou, L., W. Gao, T. Wu, and X. Xu. 2007. A method to compute solar radiation at surface in any time interval based on NCEP re-analysis. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability IV, 6679, 66790R. doi: 10.1117/12.730626

2006 Gao, W., Q. Lu, Z. Gao, W. Wu, B. Du, and J. Slusser. 2006. Analysis of temporal variations of

albedo from MODIS. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability III, 6298, 62981G. doi: 10.1117/12.676197

Johnsen, B., B. Kjeldstad, T. N. Aalerud, L. T. Nilsen, J. Schreder, M. Blumthaler, G. Bernhard, A. Bagheri, B. Bhattarai, C. Topaloglou, G. Zablocki, O. Meinander, B. A. Høiskar, R. Haugen, W. S. Durham, G. Janson, A. R. Marrero, A. Dahlback, D. Bolsée, J. R. Slusser, J. Stamnes, C. Torres, A. R. D. Smedley, L.-E. Paulsson, K. Lakkala, A. R. Webb, J. B. Ørbæk, A. A. Grimenes, T. Ringstad, T. Lange, and W. Josefsson. 2006. International intercomparison of multiband filter radiometers in Oslo 2005. Proceedings of SPIE,


Remote Sensing of Clouds and the Atmosphere XI, 6362, 63620W. doi: 10.1117/12.688918

Lantz, K., P. Disterhoft, C. Wilson, G. Janson, B. Durham, and J. Slusser. 2006. Long-term evaluation of the calibration of YES UVB-1 broadband radiometers of the Central UV Calibration Facility (1994-2005) and the suite of UV radiometers in the USDA UV Monitoring Network. Proceedings of SPIE, Remote Sensing of Clouds and the Atmosphere XI, 6362, 63620X. doi: 10.1117/12.690059

McKenzie, R., G. Bodeker, G. Scott, J. Slusser, and K. Lantz. 2006. Geographical differences in erythemally-weighed UV measured at mid-latitude USDA sites. Photochemical & Photobiological Sciences, 5, 343-352. doi: 10.1039/B510943D

Taylor, T. E., J. Slusser, A. Hernández, M. Grutter, B. Lefer. 2006. Ultraviolet aerosol optical properties retrieved during the 2006 MIRAGE-Mex experiment: initial results. Proceedings of SPIE, Remote Sensing of Clouds and the Atmosphere XI, 6362, 636203. doi: 10.1117/12.687942

2005 Beneski, E., J. H. Bassman, J. R. Slusser, and W. Gao. 2005. Application of CCD array digital

fiber optic spectrometers in determination of within-tree and within- canopy irradiances of UV-B radiation. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects V, 5886, 58860X. doi: 10.1117/12.620659

Davis, J. M., and J. R. Slusser. 2005. New USDA UVB synthetic spectra algorithm. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects V, 5886, 58860B. doi: 10.1117/12.620269

Davis, J. M., and J. R. Slusser. 2005. Impact of clouds with limited horizontal extent on UV radiation measurements. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects IV, 5545, 36-42. doi: 10.1117/12.561496

Goering, C. D., T. S. L'Ecuyer, G. L. Stephens, J. R. Slusser, G. Scott, J. Davis, J. C. Barnard, and S. Madronich. 2005. Simultaneous retrievals of column ozone and aerosol optical properties from direct and diffuse solar irradiance measurements. Journal of Geophysical Research: Atmospheres, 110(D5), D05204. doi: 10.1029/2004JD005330

Gong, L., X. Pan, Q. Shi, Z. Wang, and W. Gao. 2005. Land use pattern and influential factors in the upper reaches of Tarim River. Resources Science, 27(4), 71-75.

Grant, R. H., and J. R. Slusser. 2005. Estimation of ultraviolet-A irradiance from measurements of 368-nm spectral irradiance. Journal of Atmospheric and Oceanic Technology, 22(12), 1853-1863. doi: 10.1175/JTECH1823.1

Grant, R. H., and J. R. Slusser. 2005. The measurement and modeling of broadband UV-A irradiance. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects V, 5886, 58860C. doi: 10.1117/12.613318

Hicke, J. A., J. Slusser, and K. Lantz. 2005. Long-term variability in surface UV-B radiation across the United States. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects IV, 5545, 7-16. doi: 10.1117/12.560092

Janson, G. T., J. R. Slusser, G. Scott, P. Disterhoft, and K. Lantz. 2005. Long-term stability of UV multifilter rotating shadowband radiometers, part 2: lamp calibrations versus the Langley method. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects IV, 5545, 43-54. doi: 10.1117/12.562491

Krotkov, N. A., P. K. Bhartia, J. R. Herman, J. Slusser, G. Scoot, G. Labow, A. P. Vasilkov, T. Eck, O. Dubovik, and B. Holben. 2005. Measuring aerosol UV absorption optical thickness by combining use of shadowband and almucantar techniques. Proceedings of


SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects IV, 5545, 17-27. doi: 10.1117/12.559557

Lantz, K., P. Disterhoft, C. Wilson, G. Janson, J. Slusser, S. Bloms, and J. Michalsky. 2005. Out-of-band rejection studies of the UV multi-filter rotating shadow-band radiometers. Proceedings of SPIE, Remote Sensing of Clouds and the Atmosphere X, 5979, 59791N. doi: 10.1117/12.627813

Rochford, P. A., P. K. Acharya, S. M. Adler-Golden, A. Berk, L. S. Bernstein, M. W. Matthew, S. C. Richtsmeier, S. Gulick, and J. Slusser. 2005. Validation and refinement of hyperspectral/multispectral atmospheric compensation using shadowband radiometers. IEEE Transactions on Geoscience and Remote Sensing, 43(12), 2898-2907. doi: 10.1109/TGRS.2005.857901

Slusser, J., T. Taylor, and N. A. Krotkov. 2005. UV aerosol optical properties at three US sites. Proceedings of SPIE, Remote Sensing of Clouds and the Atmosphere X, 5979, 59791O. doi: 10.1117/12.628124

Sullivan, J. H., C. Xu, W. Gao, and J. R. Slusser. 2005. Development of UV-B screening compounds in response to variation in ambient levels of UV-B radiation. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects V, 5886, 58860L. doi: 10.1117/12.623738

Taylor, T. E., T. L’Ecuyer, J. Slusser, G. Stephens, N. Krotkov, J. Davis, and C. Goering. 2005. Characterization and error analysis of an operational retrieval algorithm for estimating column ozone and aerosol properties from ground based ultra-violet irradiance measurements. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects V, 5886, 58860Y. doi: 10.1117/12.628877

Tree, R. M., J. R. Slusser. 2005. Comparison of column ozone retrievals obtained by UV multifilter rotating shadowband radiometer with those from Brewer and Dobson spectrophotometers. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects IV, 5545, 81-89. doi: 10.1117/12.562500

Wetzel, M. A., and J. R. Slusser. 2005. Mesoscale distributions of ultraviolet spectral irradiance, actinic flux and photolysis rates derived from multispectral satellite data and radiative transfer models. Optical Engineering, 44(4), 041006. doi: 10.1117/1.1889467

2004 Beauharnois, M. C., P. Kiedron, and L. Harrison. 2004. The USDA high-resolution UV radiation

network: maintenance, calibration, and data tools. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects IV, 5545, 90-101. doi: 10.1117/12.559504

Davis, J. M., and J. R. Slusser. 2004. Impact of clouds with limited horizontal extent on UV radiation measurements. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects IV, 5545, 36-42. doi: 10.1117/12.561496

Flint, S. D., R. J. Ryel, and M. M. Caldwell. 2004. Serious complications in experiments in which UV doses are affected by using different lamp heights. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects IV, 5545, 56-62. doi: 10.1117/12.561696

Grant, R. H., and J. R. Slusser. 2004. Estimation of Photosynthetic Photon Flux Density from 368-nm Spectral Irradiance. Journal of Atmospheric and Oceanic Technology, 21(3), 481-487. doi: 10.1175/1520-0426(2004)021<0481:EOPPFD>2.0.CO;2

Grant, R. H., G. M. Heisler, and W. Gao. 2004. Impact of cloud cover on erythemal UV- B exposure under vegetation canopies. Proceedings of SPIE, Ultraviolet Ground- and


Space-based Measurements, Models, and Effects IV, 5545, 71-80. doi: 10.1117/12.562501

Hand, J. L., S. M. Kreidenweis, J. Slusser, and G. Scott. 2004. Comparisons of aerosol optical properties derived from Sun photometry to estimates inferred from surface measurements in Big Bend National Park, Texas. Atmospheric Environment, 38(39), 6813-6821. doi: 10.1016/j.atmosenv.2004.09.004

Heisler, G. M., R. H. Grant, W. Gao, and J. R. Slusser. 2004. Solar ultraviolet-B radiation in urban environments: the case of Baltimore, Maryland. Photochemistry and Photobiology, 80(3), 422-428. doi: 10.1111/j.1751-1097.2004.tb00108.x

Hicke, J. A., J. R. Slusser, and K. Lantz. 2004. Long-term variability in surface UV-B radiation across the United States. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects IV, 5545, 7-16. doi: 10.1117/12.560092

Janson, G. T., J. R. Slusser, G. Scott, P. Disterhoft, and K. Lantz. 2004. Long-term stability of UV multifilter rotating shadowband radiometers: part 2. Lamp calibrations versus the Langley method. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects IV, 5545, 43-48. doi: 10.1117/12.562491

Krotkov, N. A., P. K. Bhartia, J. R. Herman, J. Slusser, G. Scott, G. Labow, A. P. Vasilkov, T. Eck, O. Dubovik, and B. Holben. 2004. Measuring aerosol UV absorption optical thickness by combining use of shadowband and almucantar techniques. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects IV, 5545, 17-27. doi: 10.1117/12.559557

Milchunas, D. G., J. Y. King, A. R. Mosier, J. C. Moore, J. A. Morgan, M. H. Quirk, and J. R. Slusser. 2004. UV radiation effects on plant growth and forage quality in a shortgrass steppe ecosystems. Photochemistry and Photobiology, 79(5), 404-410. doi: 10.1111/j.1751-1097.2004.tb00027.x

Tree, R., and J. R. Slusser. 2004. Comparison of column ozone retrievals obtained by UV multifilter rotating shadowband radiometer with those from Brewer and Dobson spectrophotometers. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects IV, 5545, 81-89. doi: 10.1117/12.562500

2003 Grant, R. H., and W. Gao. 2003. Diffuse fraction of UV radiation under partly cloudy skies as

defined by the Automated Surface Observation System (ASOS). Journal of Geophysical Research: Atmospheres, 108(D2), 4046-4055. doi: 10.1029/2002JD002201

Grant, R. H., and J. Slusser. 2003. Spatial variability in UV radiation during the growing season across the continental USA. Theoretical and Applied Climatology, 74(3), 167-177.

Harrison, L. C., M. Beauharnois, J. L. Berndt, P. W. Kiedron, and P. Disterhoft. 2003. Transfer of UV irradiance calibration to our field spectroradiometers: current performance and operational experience at Table Mountain, Colorado. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects III, 5156, 135-142. doi: 10.1117/12.506156

Janson, G. T., and J. R. Slusser. 2003. Long-term stability of UV multifilter rotating shadowband radiometers. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects III, 5156, 94-100. doi: 10.1117/12.508195

Kimlin, M. G., J. R. Slusser, K. A. Schallhorn, and R. S. Meltzer. 2003. U.S. EPA Brewer Spectrophotometer network and the USDA UVB monitoring and research program: data comparison from co-located instruments. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects III, 5156, 85- 93. doi: 10.1117/12.505752


Krotkov, N. A., P. K. Bhartia, J. R. Herman, J. R. Slusser, G. R. Scott, G. Labow, A. P. Vasilkov, T. Eck, O. Dubovik, and B. Holben. 2003. Goddard UV aerosol absorption closure experiment (2002-03). Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects III, 5156, 54-62. doi: 10.1117/12.508967

McArthur, L. J. B., D. H. Halliwell, O. J. Neibergall, N. T. O'Neill, J. R. Slusser, and C. Wehrli. 2003. Field comparison of network Sun photometers. Journal of Geophysical Research: Atmospheres, 108(D19), 4596. doi: 10.1029/2002JD002964.

Petters, J. L., V. K. Saxena, J. R. Slusser, B. N. Wenny, and S. Madronich. 2003. Aerosol single scattering albedo retrieved from measurements of surface UV irradiance and a radiative transfer model. Journal of Geophysical Research: Atmospheres, 108(D9), 4288. doi: 10.1029/2002JD002360.

Slusser, J. R., D. Bigelow, W. Gao, G. R. Scott, and B. Olson. 2003. Comparison of UV synthetic spectra with broadband and spectral irradiances. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects III, 5156, 403-408. doi: 10.1117/12.511736

Wetzel, M. A., G. E. Shaw, J. R. Slusser, R. D. Borys, and C. F. Cahill. 2003. Physical, chemical and ultraviolet radiative signatures of aerosol in central Alaska. Journal of Geophysical Research: Atmospheres, 108(D14). doi: 10.1029/2002JD003208

Wetzel, M. A., and J. R. Slusser. 2003. Mesoscale distribution of UV spectral irradiance obtained by merging satellite remote sensing and ground based measurements. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects III, 5156, 291-302. doi: 10.1117/12.508654

2002 Grant, R. H., G. M. Heisler, and W. Gao. 2002. Estimation of Pedestrian Level UV Exposure

Under Trees. Photochemistry and Photobiology, 75(4), 369-376. Grant, R. H., and J. R. Slusser. 2002. Spatial correlations of daily and weekly maximum day

exposure of solar UV radiation in the continental United States. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects II, 4896, 52-61. doi: 10.1117/12.466118

Heisler, G. M., R. H. Grant, W. Gao, J. R. Slusser, and C. Ehrlich. 2002. Solar ultraviolet-B radiation in urban environments: Baltimore , Maryland . Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects II, 4896, 62-69. doi: 10.1117/12.466229

Lantz, K., P. Disterhoft, E. Early., A. Thompson, J. DeLuisi, J. Berndt, L. Harrison, P. Kiedron, J. Ehramjian, G., Bernhard, L. Cabasug, J. Robertson, W. Mou, T. Taylor, J. Slusser, D. Bigelow, B. Durham, G. Janson, D. Hayes, M. Beaubien, and A. Beaubien. 2002. The 1997 North American Interagency Intercomparison of Ultraviolet Spectroradiometers Including Narrowband Filter Radiometers. Journal of Research of the National Institute of Standards and Technology, 107(1), 19-62, 2002.

Slusser, J. R., N. A. Krotkov, W. Gao, J. R. Herman, G. Labow, and G. Scott. 2002. Comparisons of USDA UV shadow-band Irradiance Measurements with TOMS Satellite and DISORT Model Retrievels Under All Sky conditions. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects, 4482, 56-63. doi: 10.1117/12.452954

2001 Estupinan, J. G., M. H. Bergin, J. R. Slusser, and R. S. Meltzer. 2001. Measurement of aerosol

optical depths in the UV-A: A comparison between a USDA yankee environmental systems UV-multifilter rotating shadowband radiometer and an EPA brewer


spectrophotometer. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects, 4482, 212-223. doi: 10.1117/12.452921

Gao, W., J. Slusser, J. Gibson, G. Scott, D. Bigelow, J. Kerr, and B. McArthur. 2001. Direct-Sun column ozone retrieval by the ultraviolet multifilter rotating shadow-band radiometer and comparison with those from Brewer and Dobson spectrophotometers. Applied Optics, 40(19), 3149-3155. doi: 10.1364/AO.40.003149

Gao, W., J. R. Slusser, L. C. Harrison, P. Disterhoft, Q. Min, B. Olson, K. Lantz, and B. Davis. 2001. Comparisons of UV Synthetic Spectra Retrieved from the USDA UV Multifilter Rotating Shadowband Radiometer with Collocated USDA Reference UV Spectroradiometer and NIWA UV Spectroradiometer. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects, 4482, 408-414. doi: 10.1117/12.452944

Grant, R. H., and W. Gao. 2001. Estimating the UV diffuse fraction of solar radiation under partly cloudy skies. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects, 4482, 160-168. doi: 10.1117/12.452914

Heisler, G. M., R. H. Grant, and W. Gao. 2001. Urban tree influences on ultraviolet irradiance. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects, 4482 , 277-290. doi: 10.1117/12.452929

Michalsky, J. J., J. A. Schlemmer, W. E. Berkheiser, J. L. Berndt, L. C. Harrison, N. S. Laulainen, N. R. Larson, and J. C. Barnard. 2001. Multiyear measurements of aerosol optical depth in the Atmospheric Radiation Measurement and Quantitative Links programs. Journal of Geophysical Research, 106(D11), 12099-12107. doi: 10.1029/2001JD900096

Slusser, J. R., N. Krotkov, W. Gao, J. R. Herman, G. Labow, and G. Scott. 2001. Comparisons of USDA UV shadow-band irradiance measurements with TOMS satellite and DISORT model retrievels under all sky conditions. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects, 4482, 56-63. doi: 10.1117/12.452954

Vuilleumier, L., R. A. Harley, N. J. Brown, J. R. Slusser, D. Kolinski, and D. S. Bigelow. 2001. Variability in ultraviolet total optical depth during the Southern California Ozone Study (SCOS97). Atmospheric Environment, 35(6), 1111-1122. doi: 10.1016/S1352-2310(00)00259-4

Zheng, Y., W. Gao, and G. Shi. 2001. Calculating Solar Ultraviolet Radiation by Computational Models in Nanjing Region. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects, 4482, 455-459. doi: 10.1117/12.452951

2000 Bigelow, D. S., and J. R. Slusser. 2000. Establishing the Stability of Multi-filter UV Rotating

Shadowband Radiometers. Journal of Geophysical Research, 105, 4833-4840. Frederick, J. E., J. R. Slusser, and D. S. Bigelow. 2000. Annual and Interannual Behavior of

Solar Ultraviolet Irradiance Revealed by Broadband Measurements. Photochemistry and Photobiology, 72(4), 488-496.

Slusser, J. R., J. H. Gibson, D. S. Bigelow, D. Kolinski, P. Disterhoft, K. Lantz, and A. Beaubien. 2000. Langley Method of Calibrating UV Filter Radiometers. Journal of Geophysical Research, 105(D4), 4841-4849.

1999 Bodhaine, B. A., N. B. Wood, E. G. Dutton, and J. R. Slusser. 1999. On Rayleigh Optical Depth

Calculations. Journal of Atmospheric and Oceanic Technology, 16(11), 1854-1861.


Kaye, J. A., B. B. Hicks, E. C. Weatherhead, C. S. Long, and J. Slusser. 1999. U.S. Interagency UV Monitoring Program Established and Operating. Eos, Transactions American Geophysical Union, 80(10), 113-116. doi: 10.1029/99EO00075

Lantz, K. O., P. Disterhoft, J. J. Deluisi, E. Early, A. Thompson, D. Bigelow, and J. Slusser. 1999. Methodology for Deriving Clear-Sky Erythemal Calibration Factors for UV Broadband Radiometers of the US Central UV Calibration Facility. Journal of Atmospheric and Oceanic Technology, 16(11), 1736-1752.

Slusser, J. R., J. H. Gibson, D. Bigelow, D. Kolinski, W. Mou, G. Koenig, and A. Beaubien. 1999. Comparison of column ozone retrievals by use of an UV multifilter rotating shadow-band radiometer with those from Brewer and Dobson spectrophotometers. Applied Optics, 38(9), 1543-1551.

1998 Bigelow, D. S., J. R. Slusser, A. F. Beaubien, and J. H. Gibson. 1998. The USDA Ultraviolet

Radiation Monitoring Program. Bulletin of the American Meteorological Society, 79(4), 601-615.

Early, E., A. Thompson, C. Johnson, J. DeLuisi, P. Disterhoft, D. Wardle, E. Wu, W. Mou, J. Ehramjian, J. Tusson, T. Mestechkina, M. Beaubien, J. Gibson, and D. Hayes. 1998. The 1996 North American Interagency Intercomparison of Ultraviolet Monitoring Spectroradiometers. Journal of Research of the National Institute of Standards and Technology, 103(5), 449-482.

Early, E., A. Thompson, C. Johnson, J. DeLuisi, P. Disterhoft, D. Wardle, E. Wu, W. Mou, Y. Sun, T. Lucas, T. Mestechkina, L. Harrison, J. Berndt, and D. Hayes. 1998. The 1995 North American Interagency Intercomparison of Ultraviolet Monitoring Spectroradiometers. Journal of Research of the National Institute of Standards and Technology, 103(1), 15-62.

Early, E. A., E. A. Thompson, and P. Disterhoft. 1998. Field Calibration Unit for Ultraviolet Spectroradiometers. Applied Optics, 37(28), 6664-6670.

Min, Q., and L. C. Harrison. 1998. Synthetic spectra for terrestrial ultraviolet from discrete measurements. Journal of Geophysical Research: Atmospheres, 103(D14), 17033-17039. doi: 10.1029/98JD01452

1997 Thompson, A., E. A. Early, J. Deluisi, P. Disterhoft, D. Wardle, J. Kerr, J. Rives, Y. Sun, T.

Lucas, T. Mestechkina, and P. Neale. 1997. The 1994 North American Interagency Intercomparison of Ultraviolet Monitoring Spectroradiometers. Journal of Research of the National Institute of Standards and Technology, 102(3), 279-322.

1995 Michalsky, J. J., L. C. Harrison, and W. E. Berkheiser III. 1995. Cosine Response Characteristics

of Some Radiometric and Photometric Sensors. Solar Energy, 54(6), 397-402. 1994 Harrison, L., and J. Michalsky. 1994. Objective algorithms for the retrieval of optical depths

from ground-based measurements. Applied Optics, 33(22), 5126-5132. Harrison, L., J. Michalsky, and J. Berndt. 1994. Automated multifilter rotating shadow-band

radiometer: an instrument for optical depth and radiation measurements. Applied Optics, 33(22), 5118-5125.


Program Area 2: Effects of UV-B on Agriculture 2016 Reddy, K. R., H. Patro, S. Lokhande, N. Bellaloui, and W. Gao. 2016. Ultraviolet-B Radiation

Alters Soybean Growth and Seed Quality. Food and Nutrition Sciences, 7, 55-66. doi: 10.4236/fns.2016.71007

2015 Abukari, I. A., M. W. Shankle, and K. R. Reddy. 2015. Sweetpotato [Ipomoea batatas (L.) Lam.]

Response to S-Metolachlor and Rainfall under Three Temperature Regimes. American Journal of Plant Sciences, 6, 702-717. doi: 10.4236/ajps.2015.65076

Lokhande, S., and K. R. Reddy. 2015. Cotton reproductive and fiber quality responses to nitrogen nutrition. International Journal of Plant Production, 9(2), 191-209.

Wijewardana, C., M. Hock, B. Henry, and K. R. Reddy. 2015. Screening corn hybrids for cold tolerance using morphological traits for early-season seeding. Crop Science, 55, 851-867. doi: 10.2135/cropsci2014.07.0487

Wijewardana, C., W. B. Henry, and K. R. Reddy. 2015. Interactive Effects on CO2, drought, and ultraviolet-B radiation on corn (Zea mays) growth and development. Southern Regional Branch of American Society of Agronomy Annual Meetings, Atlanta, GA, USA

2014 Abukari, I. A., M. W. Shankle, and K. R. Reddy. 2014. S-metolachlor and rainfall effects on

sweetpotato (Ipomoea batatas L. [Lam]) growth and development. Scientia Horticulturae, 185, 98-104. doi: 10.1016/j.scienta.2015.01.018

Gajanayake, B., K. R. Reddy, M. W. Shankle, and R. A. Arancibia. 2014. Growth, developmental, and physiological responses of two sweetpotato (Ipomoea batatus (L.) Lam] cultivars to early season soil moisture deficit. Scientia Horticulturae, 168, 218-228. doi: 10.1016/j.scienta.2014.01.018

Gajanayake, B., K. R. Reddy, M. W. Shankle, R. A. Arancibia, and A. O. Villordon. 2014. Quantifying storage root initiation, growth, and developmental responses of sweetpotato to early season temperature. Agronomy Journal, 106, 1795-1804. doi: 10.2134/agronj14.0067

Lokhande, S., and K. R. Reddy. 2014. Quantifying temperature effects on cotton reproductive efficiency and fiber quality. Agronomy Journal, 106(4), 1275-1282. doi: 10.2134/agronj13.0531

Lokhande, S., and K. R. Reddy. 2014. Reproductive and fiber quality responses of Upland cotton to moisture deficiency. Agronomy Journal, 106(3), 1060-1069. doi: 10.2134/agronj13.0537

Singh, S. K., K. R. Reddy, V. R. Reddy, and W. Gao. 2014. Maize growth and developmental responses to temperature and ultraviolet-B radiation interaction. Photosynthetica, 52(2), 262-271. doi: 10.1007/s11099-014-0029-6

2013 Reddy, K. R., S. K. Singh, S. Koti, V. G. Kakani, D. Zhao, W. Gao, and V. R. Redd. 2013.

Quantifying the Effects of Corn Growth and Physiological Responses to Ultraviolet-B Radiation for Modeling. Agronomy Journal, 105(5), 1367-1377. doi: 10.2134/agronj2013.0113

Singh, R. P., P. V. V. Prasad, and K. R. Reddy. 2013. Impacts of changing climate and climate variability on seed production and seed industry. In D. L. Spark (Ed.), Advances in Agronomy, Vol. 118 (pp. 49-110). Academic Press, Oxford, UK.

2012


Liang, X., M. Xu, W. Gao, K. R. Reddy, K. Kunkel, D. L. Schmoldt, and A. N. Samel. 2012. A Distributed Cotton Growth Model Developed from GOSSYM and Its Parameter Determination. Agronomy Journal, 104(3), 661-674. doi: 10.2134/agronj2011.0250

Liang, X., M. Xu, W. Gao, K. R. Reddy, K. Kunkel, D. L. Schmoldt, and A. N. Samel. 2012. Physical Modeling of U.S. Cotton Yields and Climate Stresses during 1979 to 2005. Agronomy Journal, 104(3), 675-683. doi: 10.2134/agronj2011.0251

2009 Surabhi, G.-K., K. R. Reddy, and S. K. Singh. 2009. Photosynthesis, fluorescence, shoot biomass

and seed weight responses of three cowpea (Vigna unguiculata [L.] Walp.) cultivars with contrasting sensitivity to UV-B radiation. Environmental and Experimental Botany, 66, 160-171. doi: 10.1016/j.envexpbot.2009.02.004

2008 Singh, S. K., G.-K. Surabhi, W. Gao, and K. R. Reddy. 2008. Assessing genotypic variability of

cowpea (Vigna unguiculata [L.] Walp.) to current and projected ultraviolet-B radiation. Journal of Photochemistry and Photobiology B: Biology, 93(2), 71-81. doi: 10.1016/j.jphotobiol.2008.07.002

2007 Koti, S., K. R. Reddy, V. G. Kakani, D. Zhao, and W. Gao. 2007. Effects of carbon dioxide,

temperature and ultraviolet-B radiation and their interactions soybean (Glycine max L.) growth and development. Environmental and Experimental Botany, 60(1), 1-10. doi: 10.1016/j.envexpbot.2006.05.001

2006 Koti, S., K. R. Reddy, G. W. Lawrence, V. R. Reddy, V. G. Kakani, D. Zhao, and W. Gao. 2006.

Effect of enhanced UV-B radiation on reniform nematode (Rotylenchus reniformis Linford and Oliveira) populations in cotton (Gossypium hirsutum L.). Plant Pathology Journal, 6(1), 51-59.

Wang, X., W. Gao, K. R. Reddy, J. Slusser, and M. Xu. 2006. Preliminary results of a UV-B effect incorporated GOSSYM Model. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability III, 6298, 62980O. doi: 10.1117/12.681446

2005 Koti, S., K. R. Reddy, V. R. Reddy, V. G. Kakani, and D. Zhao. 2005. Interactive effects of

carbon dioxide, temperature and ultraviolet-B radiation on soybean (Glycine max L.) flower and pollen morphology, pollen production, germination and tube lengths. Journal of Experimental Botany, 56(412), 725-736. doi: 10.1093/jxb/eri044

Reddy, K. R., S. Koti, V. G. Kakani, D. Zhao, and W. Gao. 2005. Genotypic variation of soybean and cotton crops in their response to UV-B radiation for vegetative growth and physiology. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects V, 5886, 58860K. doi: 10.1117/12.619899

Xu, M., X. Liang, W. Gao, K. R. Reddy, J. R. Slusser, and K. Kunkel. 2005. Preliminary results of the coupled CWRF-GOSSYM system. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability II, 5884, 588409. doi: 10.1117/12.621017

Zhao, D., K. R. Reddy, V. G. Kakani, S. Koti, and W. Gao. 2005. Physiological causes of cotton fruit abscission under conditions of high temperature and enhanced ultraviolet-B radiation. Physiologia Plantarum, 124(2), 189-199. doi: 10.1111/j.1399-3054.2005.00491.x

2004


Kakani, V. G., K. R. Reddy, D. Zhao, and W. Gao. 2004. Leaf senescence and hyperspectral reflectance of cotton leaves exposed to ultraviolet-B radiation and carbon dioxide. Physologia Plantarum, 121(2), 250-257.

Kakani, V. G., K. R. Reddy, D. Zhao, and W. Gao. 2004. Senescence and hyperspectral reflectance of cotton leaves exposed to ultraviolet-B radiation and carbon dioxide. Physologia Plantarum, 121(2), 250-257. doi: 10.1111/j.1399-3054.2004.00314.x

Koti, S., K. R. Reddy, V. G. Kakani, D. Zhao, and V. R. Reddy. 2004. Soybean (Glycine max) Pollen Germination Characteristics, Flower and Pollen Morphology in Response to Enhanced Ultraviolet-B Radiation. Annals of Botany, 94(6), 1-10. doi: 10.1093/aob/mch212

Reddy, K. R., V. G. Kakani, D. Zhao, S. Koti, and W. Gao. 2004. Interactive Effects of Ultraviolet-B Radiation and Temperature on Cotton Physiology, Growth, Development and Hyperspectral Reflectance. Photochemistry and Photobiology, 79(5), 416-427. doi: 10.1111/j.1751-1097.2004.tb00029.x

Zhao, D., K. R. Reddy, V. G. Kakani, A. R. Mohammed, J. J. Read, and W. Gao. 2004. Leaf and canopy photosynthetic characteristics of cotton (Gossypium hirsutum) under elevated CO2 concentration and UV-B radiation. Journal of Plant Physiology, 161(5), 581-590. doi: 10.1078/0176-1617-01229

2003 Kakani, V. G., K. R. Reddy, D. Zhao, and K. Sailaja. 2003. Field crop responses to ultraviolet-B

radiation: a review. Agricultural and Forest Meteorology, 120(1-4), 191-218. doi: 10.1016/j.agrformet.2003.08.015

Reddy, K. R., S. Koti, D. Zhao, V. G. Kakani, and W. Gao. 2003. Interactive effects of atmospheric carbon dioxide and ultraviolet-B radiation on cotton growth and physiology. Proceedings of SPIE, Ultraviolet Ground- and Space-based Measurements, Models, and Effects III, 5156, 262-272. doi: 10.1117/12.510531

Reddy, K. R., V. G. Kakani, D. Zhao, A. R. Mohammed, and W. Gao. 2003. Cotton responses to Ultraviolet-B radiation: experimentation and algorithm development. Agricultural and Forest Meteorology, 120, 249-266.

Program Area 3: Integrated Assessment System 2015 Wu, Y., X. Liang, W. Gao. 2015. Climate change impacts on the U.S. agricultural economy.

Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability XII, 9610, 96100J. doi: 10.1117/12.2192469

2014 Liu, S., X. Liang, W. Gao, and T. J. Stohlgren. 2014. Regional climate model downscaling may

improve the prediction of alien plant species distributions. Frontiers of Earth Science, 8(4), 457-471. doi: 10.1007/s11707-014-0457-4

Xu, M., X. Liang, A. Samel, and W. Gao. 2014. MODIS consistent vegetation parameter specifications and their impacts on regional climate simulations. Journal of Climate, 27(22), 8578-8596. doi: 10.1175/JCLI-D-14-00082.1

2012 Liang, X., M. Xu, W. Gao, K. R. Reddy, K. Kunkel, D. L. Schmoldt, and A. N. Samel. 2012. A

Distributed Cotton Growth Model Developed from GOSSYM and Its Parameter Determination. Agronomy Journal, 104(3), 661-674. doi: 10.2134/agronj2011.0250


Liang, X., M. Xu, W. Gao, K. R. Reddy, K. Kunkel, D. L. Schmoldt, and A. N. Samel. 2012. Physical Modeling of U.S. Cotton Yields and Climate Stresses during 1979 to 2005. Agronomy Journal, 104(3), 675-683. doi: 10.2134/agronj2011.0251

Liang, X., M. Xu, X. Yuan, T. Ling, H. I. Choi, F. Zhang, L. Chen, S. Liu, S. Su, F. Qiao, J. X. L. Wang, K. E. Kunkel, W. Gao, E. Joseph, V. Morris, T.-W. Yu, J. Dudhia, and J. Michalakes. 2012. Regional Climate-Weather Research and Forecasting Model. Bulletin of the American Meteorological Society, 93(9), 1363-1387. doi: 10.1175/BAMS-D-11-00180.1

2011 Liu, S., X. Liang, W. Gao, Y. He, and T. Ling. 2011. Regional climate model simulations of the

1998 summer China flood: dependence on initial and lateral boundary conditions. The Open Atmospheric Science Journal, 5, 96-105. doi: 10.2174/1874282301105010096

2010 Xu, M., X. Liang, W. Gao, and N. Krotkov. 2010. Comparison of TOMS retrievals and UVMRP

measurements of surface spectral UV radiation in the United States. Atmospheric Chemistry and Physics, 10, 8669-8683. doi: 10.5194/acp-10-8669-2010

2009 Liu, S., W. Gao, M. Xu, X. Wang, and X. Liang. 2009. China summer precipitation simulations

using an optimal ensemble of cumulus schemes. Frontiers of Earth Science, 3(2), 248-257. doi: 10.1007/s11707-009-0022-8

2008 Liu, S., X. Liang, W. Gao, and H. Zhang. 2008. Application of Climate-Weather Research and

Forecasting Model (CWRF) in China: Domain Optimization. Chinese Journal of Atmospheric Sciences, 32(3), 457-468.

2006 Liu, S., W. Gao, X. Liang, H. Zhang, and J. Slusser. 2006. CWRF simulations of the China 1991

and 1998 summer floods. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability III, 6298, 62981J. doi: 10.1117/12.676218

Liu, S., W. Gao, X. Liang, H. Zhang, and J. Slusser. 2006. Sensitivity of CWRF simulations of the China 1998 summer flood to cumulus parameterizations. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability III, 6298, 62981I. doi: 10.1117/12.676216

Wang, X., W. Gao, J. R. Slusser, J. Davis, Z. Gao, G. Scott, B. Olson, N. Krotkov, M. Xu, and X. Liang. 2006. Spectral distribution of UV-B irradiance derived by synthetic model compared with simulation results of TUV and ground measurements. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability III, 6298, 62980L. doi: 10.1117/12.681464

Xu, M., X. Liang, W. Gao, J. Slusser, and K. Kunkel. 2006. Validation of the TUV module in CWRF using USDA-UVB network observations. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability III, 6298, 62980N. doi: 10.1117/12.680122

2005 Liang, X., M. Xu, W. Gao, K. Kunkel, J. Slusser, Y. Dai, Q. Min, P. R. Houser, M. Rodell, C. B.

Schaaf, and F. Gao. 2005. Development of land surface albedo parameterization based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. Journal of Geophysical Research: Atmospheres, 110(D11), D11107. doi: 10.1029/2004JD005579


Xu, M., X. Liang, W. Gao, K. R. Reddy, J. R. Slusser, and K. Kunkel. 2005. Preliminary results of the coupled CWRF-GOSSYM system. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability II, 5884, 588409. doi: 10.1117/12.621017

2004 Liang, X., M. Xu, W. Gao, K. Kunkel, J. Slusser, Y. Dai, and Q. Min. 2004. New land surface

albedo parameterization based on MODIS data: preliminary result. Proceedings of SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability, 55-60. doi: 10.1117/12.563449

2002 Liang, X., W. Gao, K. Kunkel, J. R. Slusser, X. Pan, H. Liu, and Y. Ma. 2002. Sustainability of

vegetation over Northwest China:I. Climate Response to Grassland. Proceedings of SPIE, Ecosystems Dynamics, Ecosystem-Society Interactions, and Remote Sensing Applications for Semi-Arid and Arid Land, 4890, 29-44. doi: 10.1117/12.466845

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