space-based soil moisture measurements …...bodeker scientific ltd space-based soil moisture...

SPACE-BASED SOIL MOISTURE MEASUREMENTS OVER NEW ZEALAND Alexander Schwertheim & Greg Bodeker

Front Cover – Photo Acknowledgements NZ Landscape: www.cpwl.co.nz Satellite: ESA/AOES Medialab

http://www.cpwl.co.nz

Space-Based Soil Moisture Measurements over New Zealand Bodeker Scientific Ltd

1

Contents 1. Motivation ..............................................................................................................................................3

2. Two different technologies: Microwave or Multispectral ......................................................................4

3. Basic L-Band Microwave Radiometry .....................................................................................................5

Blackbody radiation and brightness temperature ..........................................................................................5

4. Brightness temperatures, dielectric constants and soil moisture ..........................................................6

5. SMOS: The Soil Moisture and Ocean Salinity Experiment ......................................................................6

6. SMOS products .......................................................................................................................................7

Measuring Brightness Temperature ...............................................................................................................7

How SMOS calculates Soil Moisture from Brightness Temperature ..............................................................8

7. SMAP: Soil Moisture Active Passive .......................................................................................................9

8. SMAP products .......................................................................................................................................9

9. Soil Moisture from Multispectral Satellite Data using SEBAL ...............................................................10

10. SEBAL over New Zealand ..................................................................................................................11

11. Data assimilation ..............................................................................................................................11

12. JULES ................................................................................................................................................12

13. DISPATCH: Combining Microwave and Multispectral data to improve resolution ..........................12

14. S-mapOnline: Fast access to New Zealand Soil Data ........................................................................13

15. Root Zone Soil Moisture ...................................................................................................................13

16. Conclusions.......................................................................................................................................15

References ....................................................................................................................................................16

Appendix: SMOS Retrievals for Low Vegetation Cover ................................................................................18

Bodeker Scientific Ltd Space-Based Soil Moisture Measurements over New Zealand

2


3

1. Motivation Agriculture has always been at the heart of New Zealand’s economy. Currently, agriculture is still New Zealand’s largest sector of the tradable economy, contributing two-thirds of exported goods1. Because much of this agricultural activity takes place on land on which insufficient rain falls, supplementary irriga-tion is required. An estimated 20% of exported goods rely on irrigation2. Without irrigation there would be no Marlborough Sauvignon Blanc, no stone fruit from Otago, and no seed production in Canterbury. The large-scale transition to dairy over the past decade has seen the area of land being irrigated grow by 102,000 hectares between 2007 and 20123. Climate change is expected to lead to longer and more fre-quent droughts over New Zealand4 leading to unprecedented water shortages in the coming decades.

The use of water for irrigation purposes can be reduced by irri-gating more intelligently. Precision irrigation ensures that crops (hereafter including pasture) are provided with only the water they need, when they need it, to mini-mize water usage without reduc-ing farming efficiency. One meth-od of precision irrigation is varia-ble-rate irrigation which uses software to open and close valves remotely on centre-pivot or lat-eral-sprinkler irrigators, allowing a farmer to tailor irrigation to the needs of different soils with a sin-gle irrigator. This ensures that only water that is needed is provided, but not so much as to over-saturate the soil and/or cause excessive runoff. Research has shown that variable-rate irrigation can save between 9% and 26% of water and re-duce runoff and drainage by 55%5. When including electricity costs, variable-rate irrigation systems are estimated to save farmers $60-$150 per hectare per year. Precision irrigation requires information on factors such as soil moisture, weather forecasts and plant stress. To maximise profits, New Zealand farm-ers would benefit from using emerging technologies to provide this sort of information. This report ex-plores some of these emerging technologies.

Data products from a growing suite of Earth observing satellites are providing sophisticated data prod-ucts that can inform on-farm decision making in New Zealand. These satellites use remote sensing to de-rive information about the surface of the Earth while drifting hundreds of kilometres above it. These spacecraft can measure attributes such as sea surface temperatures in the middle of the Pacific Ocean, the mass of a glacier in Switzerland or the surface wind speed in Texas. The launch of the SMOS (Soil Moisture Ocean Salinity) and SMAP (Soil Moisture Active Passive) satellites have allowed soil moisture to be added as an Earth surface attribute that can be measured from space. By measuring the natural mi-crowave radiation that is emitted by soils, these satellites can derive soil moisture anywhere on Earth, with an uncertainty of less than 4%. All retrieved soil moisture data are available for download, free of charge, hours after sensing.

Source: http://growingtheconversation.blogspot.co.nz/2013_05_01_archive.html

http://growingtheconversation.blogspot.co.nz/2013_05_01_archive.html


4

To be of use to the farmer, up-to-date soil moisture information (i.e. in near real-time) must be made available daily at high spatial reso-lution (e.g. 40m x 40m) and at root zone depth. While no single data source can fulfil these require-ments, data from different sources can be combined to create a product that does. This report ex-plores some options for how this could be done.

Making space-based soil moisture information available to New Zea-

land farmers in an accessible way will allow better decisions around water resource allocation to be made. Better water resource use will reduce irrigation costs, reduce runoff (and hence nitrification of New Zealand rivers) and reduce the need for expanded infrastructure (e.g. building new dams or raising existing ones). The feasibility of providing New Zealand farmers with soil moisture data products and an-cillary information derived from satellite-based measurements is explored in greater detail below. Two different techniques by which satellites measure soil moisture are described viz. microwave radiometry, and multispectral imaging. The report begins by comparing and contrasting these two techniques before describing in greater detail how L-band microwave radiometry works (Section 3). A description of the modelling of the dielectric constant of bare soils is given before two soil moisture satellites (SMOS and SMAP), and their resultant data products, are described in greater detail. More technical information such as how brightness temperatures are measured, how SMOS calculates soil moisture from brightness temperatures, and SMOS retrievals for low vegetation cover are provided in ‘boxes’ which can be skipped by readers less interested in the technical details of the measurement process. A description of SMOS retrievals for low vegetation cover is provided in an appendix. The report then goes on to consider the use of multispectral satellite data for deriving soil moisture and in particular the SEBAL algorithm. A de-scription of 4-dimensional variational data assimilation is provided in Section 11 which would likely use the JULES hydrological model, described in Section 12. Space-based microwave measurements and multi-spectral measurements can be combined to improve the spatial resolution of the resultant data products as described in Section 13. The challenges of measuring soil moisture in the root zone (rather than in the top few centimetres of soil) from space are explored in Section 15. Conclusions are presented in Section 16.

2. Two different technologies: Microwave or Multispectral The two different methods used to measure soil moisture from space, and which are explored further in this report, are microwave radiometry and the use of multispectral measurements. Microwave radiome-try measures natural microwave emissions from soil in the so-called ‘L-band’ to infer the ‘brightness tem-perature’. The brightness temperature depends on the dielectric properties of the soil which in turn re-lates to soil moisture as detailed in Section 4. This technique of space-based remote sensing of soil mois-ture is only possible using purpose-built satellites, such as the European SMOS and the American SMAP

Source: http://www.hunterdownsirrigation.co.nz/

http://www.hunterdownsirrigation.co.nz/


5

satellite. Because the transmission of microwaves from the Earth’s surface to space is unimpeded by clouds, this method has the advantage of being able to sense soil moisture under all weather conditions to provide maps of soil moisture over New Zealand daily. The multispectral method uses a combination of visual, near infrared and thermal infrared images in a process referred to as multispectral imaging. A radiative balance model, called SEBAL15, is used to infer soil moisture from multispectral images by deriv-ing energy fluxes. The images required by the multispectral imaging approach are available from a num-ber of satellites and at a much higher resolution than what is available from microwave radiometers such as SMOS and SMAP i.e. 30m x 30m resolution compared to 40km x 40km for SMOS and 3km x 3km for SMAP.

3. Basic L-Band Microwave Radiometry

The sunlight shining on Earth’s surface is a form of short-wave electromagnetic radiation. Some of that sunlight is absorbed by the surface which then warms. Warm objects, such as the Earth, emit radiation back into space in the form of infrared and microwave radiation, which is broadly called longwave radia-tion. Microwave radiometry is based on measuring the strength of the microwave radiation being emit-ted by a patch of land, which is also referred to as the brightness temperature. The brightness tempera-ture is determined by the physical temperature and dielectric constant of the patch of land emitting the microwave radiation6. The dielectric constant of a material dictates how it responds when illuminated by electromagnetic radiation, such as microwave radiation. If the temperature of the soil is known, then the dielectric constant can be inferred from the brightness temperature. At microwave frequencies, the die-lectric constant for dry soil has a value of around 4, but for water it is 806. Since wet soil is a combination of dry soil and water, if we can measure the dielectric constant of a patch of soil, we can infer its water content.

The microwaves naturally emitted from the Earth’s surface, and measured for soil moisture retrievals, are very weak (typically 10-12W/m2). Studies have found that microwaves with a frequency of exactly 1.41GHz (a wavelength of 21cm) are optimal for inferring soil moisture7 because they are very sensitive to soil moisture, and can also penetrate cloud and low vegetation. This allows for soil moisture retrievals to be

Blackbody radiation and brightness temperature A blackbody radiator is a theoretical object that absorbs all incoming electromagnetic radiation. By definition, a blackbody has an emissivity of 1 at all frequencies. Any object with an emissivity of less than 1 is considered a grey body. When in thermal equilibrium, a blackbody emits a spectrum of radi-ation that depends only on its temperature, according to Planck’s law. By inverting Planck’s law one can calculate the temperature of a blackbody by remotely measuring the emitted radiance at any wavelength in the spectrum. This measured quantity is called a brightness temperature. The bright-ness temperature is measured at a frequency of 1.41GHz for deriving soil moisture.

The brightness temperature of a grey body is the equivalent temperature a blackbody in thermal equi-librium with its surroundings would have to exhibit to replicate the observed radiance. For deriving soil moisture from radiometry, the brightness temperature of the soil is measured by the satellite and the ground temperature is provided from ancillary data. This allows the emissivity to be calculated, which is a function of the dielectric constant, and therefore soil moisture.


6

made regardless of weather. 1.41 GHz radiation falls into a class of microwaves called the L-band (bands are labelled alphabetically). Fortunately, L-band microwaves are a protected band such that it is illegal for anyone to produce this radiation intentionally. This greatly reduces the likelihood of manmade interfer-ence with the highly sensitive measurements of L-band emissions made from space.

4. Brightness temperatures, dielectric constants and soil mois-ture

The link between the brightness temperature (as determined from the microwave radiation measure-ment) and soil moisture is the aforementioned dielectric constant. The brightness temperature of a soil layer indicates how much energy is emitted as electromagnetic radiation, and is determined by the soil temperature and the soil’s emissivity6 & 8. At L-band frequencies and typical soil temperatures, the equa-tion for the brightness temperature simplifies to equation (1)8.

푇 = 푒푇 (1)

Where Tb is the brightness temperature, e is the soil’s emissivity, and T is the temperature of the soil. The emissivity of a material is directly related to its dielectric constant ɛ, by9:

푒 = 1 −

1 − √휀1 + √휀

(2)

Therefore, the dielectric constant of a soil can be calculated if the brightness temperature and soil tem-perature are known. While the dielectric constant, and therefore the emissivity of a soil layer, has a strong dependence on soil moisture, other factors such as soil temperature, soil surface roughness, vege-tation cover, ice/snow cover also affect the soil dielectric constant6. The relationship between the dielec-tric constant and the soil moisture is therefore not as simple as suggested above. A Dielectric Mixing Model (DMM) is used to appropriately account for these factors when relating soil dielectric constant to soil moisture. DMMs vary from semi-empirical models such as the Dobson model10, to more physical models such as the Mironov model11.

5. SMOS: The Soil Moisture and Ocean Salinity Experiment

In 1983, a need for global soil moisture and ocean salinity measurements for hydrology, climatology and oceanography was expressed by the scientific community. This prompted discussions on the possibility for a space-based L-band radiometer tailored for such a purpose. Twenty six years later, in 2009, the Eu-ropean Space Agency (ESA) launched the Soil Moisture and Ocean Salinity experiment (SMOS). This 630kg spacecraft orbits Earth every 100 minutes at ~760 km altitude11. The payload on the satellite is the Mi-crowave Radiometer by Aperture Synthesis (MIRAS) which consists of rows of detectors along three 4.5m antennae positioned in a ‘Y’ shaped array. MIRAS is the first two dimensional interferometric radiometer to fly on a spacecraft. Due to the geometry of the detector, MIRAS can measure the brightness tempera-ture in a hexagonally shaped footprint (also called a pixel) with an area of 1000 km2, every 1.2 seconds11. Due to the large inclination of the SMOS orbit, and New Zealand’s relatively high latitude, the spacecraft


7

passes over New Zealand daily. SMOS is sun synchronous such that it passes overhead at either 6am or 6pm local solar time.

6. SMOS products

For non-commercial uses, ESA makes two levels of SMOS products freely available. The Centre Aval de Traitement des Donnees SMOS (CATDS) and the Centre d'Etudes Spatiales de la BIOsphère (CESBIO) pro-duce level 3 and 4 products (described below) which are ESA endorsed. Each level of data/product serves a different purpose (only the level 2, 3 and 4 products are likely to be of interest in the context of this report):

Level-1A data are correlation visibilities of brightness temperature snapshots, combined into a pole-to-pole product (explained below).

Level-1B data are the fourier components resulting from image reconstruction, in the antenna polarisation reference frame.

Level-1C data contain the brightness temperatures that are output from the image reconstruc-tion algorithm.

Level 2 data are retrieved soil moisture data along with ancillary data such as surface tempera-ture, dielectric constant and brightness temperature. This product is presented in half orbit swaths on a Icosahedral Snyder Equal Area (ISEA) grid. Note that while this grid has a 15km reso-lution, SMOS’s radiometric resolution is far coarser at ~40km.

Level 3 products are global soil moisture maps constructed from level 2 data from multiple orbits produced by CATDS. These are provided as daily, weekly and monthly mean global maps, with a 0.25° latitude and 0.25° longitude resolution. The latency of a product is the time between the taking of the measurement and the data becoming accessible. Even the daily product has a la-tency of 3-4 days because the algorithm integrates multiple retrievals to provide an average.

Level 4 products are data sets which have been constructed to provide a higher resolution or in-corporate model output. Some of the products which may be useful to the New Zealand agricul-tural community are high resolution maps or modelled products at root zone depth (see below).

Measuring Brightness Temperature

MIRAS is a two-dimensional interferometer with 66 LIght Cost Effective Front-End sensors (LICEFs). To measure the frequency of incoming microwave radiation, SMOS is tilted such that each LICEF is a slightly different distance from the Earth’s surface. Given that the target microwave radiation to be measured has a wavelength of 21cm, the small path difference between Earth’s surface and the two LICEFs allows the MIRAS to accurately measure the radiation frequency, much like a traditional inter-ferometer. Each pair of LICEFs calculates both the frequency and amplitude of the microwave radia-tion. The frequency and amplitude measurements from the LICEFs are combined into a single hexag-onally shaped footprint on the Earth’s surface called a “correlation visibility” which is the SMOS Lev-el-1A data product. Various image reconstruction and antialiasing algorithms are then applied to the Level-1A data in fourier space to generate the Level-1B data. This includes applying methods to compensate for sun glint and unwanted radio frequency interference. The Level-1B snapshots are then geographically sorted and combined to give Level-1C brightness temperature maps. These brightness temperature maps are later used to derive Level-2 soil moisture products.


8

Unlike level 2 data, which are provided in half orbit binary files, level 3 data come as a complete global map in a NetCDF file. Unfortunately, level 3 data will be of reduced use to the New Zealand agricultural community because it has a latency of three days or more. This is because SMOS can take up to three days to map the entire globe. New Zealand’s long but narrow shape means that the entire country is usu-ally covered in a single half orbit. The level 2 data currently have a latency of 1 to 3 days, which is possibly also too long to make them suitable for incorporation into products. ESA is work-ing on a new level 2 prod-uct which trains a neural network to calculate soil moisture. Tests have shown that after a training session, which takes about 15 minutes, the neural network is able to process the Level-1C data to Level-2 data almost instantane-ously12. This will reduce the latency of the data to near real time, which is about 3 hours after sensing13. The neural network product is expected to start becoming available in the latter half of 201512.

How SMOS calculates Soil Moisture from Brightness Temperature

To generate global maps of soil moisture, SMOS needs to be able to function for a variety of land co-vers. Each SMOS pixel is around 40km x 40km in size and therefore most pixels contain a mixture of land covers. SMOS generates only one soil moisture value per pixel per satellite overpass. The SMOS retrieval algorithm has access to a selection of different inversion models, each of which is specialized to estimate soil moisture for a particular land cover. The first task of the SMOS level 2 processor is to decide which land cover best represents the entire pixel, and therefore which inversion model to use. For this application, the processor is provided with auxiliary land cover data in the form of a map which divides the globe into a 4km x 4km grid, where each cell is classified in one of ten land covers viz. wet soil with low vegetation, forest, saline water, pure water, frost, rocks & barren, ice, urban, wet snow or mixed snow. The algorithm calculates what portion of the 40km x 40km brightness tem-perature pixel is made of what land cover. For example, a pixel may be 72% wet soil with vegetation, 15% pure water and 13% wet snow. These percentages are used in a decision tree to decide which single inversion model is best suited for the retrieval.

SMAP: Soil Moisture Active Passive satellite Artist’s rendering of the SMAP satellite. The width of the region scanned on Earth’s sur-face during each orbit is about 1000 km. Image credit: NASA/JPL-Caltech


9

7. SMAP: Soil Moisture Active Passive

NASA launched an L-band radiometer in January 2015. Soil Moisture Active Passive (SMAP) has two in-struments on board viz. a passive radiometer similar to the one flown on SMOS and an “active” Synthetic Aperture Radar (SAR)14. Unlike SMOS’s iconic ‘Y’ shaped two dimensional interferometer, SMAP increases its effective aperture size by bouncing microwave signals off a 6 meter wide deployable rotating reflector which is positioned 5 m above the spacecraft. The reflector focuses the beam slightly to produce a foot-print with an area of about 40km x 40km. The reflector rotates once every 4 seconds, so that the foot-print moves in a 1000km wide circle, producing a very large swath14. The rotation of the reflector allows the radiometer onboard SMAP to retrieve soil moisture with the same accuracy as its European counter-part, but at a 36km resolution rather than a 40km resolution.

The SMAP’s Synthetic Aperture Radar is considered “active” because it is constantly transmitting micro-waves at a frequency of 1.26GHz towards the surface of the Earth and then measuring the intensity of the reflected signal. Because water is a very strong absorber of microwave radiation, the SAR measures how much of the transmitted microwave signal is reflected and assumes that any absorption is due to water. The brighter the “backscatter”, or reflected signal off the soil, the less microwave radiation was absorbed, indicative of drier soil. While this method does not retrieve soil moisture as accurately as the method employed by radiometers (±6% uncertainty compared to ±4% uncertainty from radiometers), it allows SMAP to take advantage of the superior spatial resolution of a synthetic aperture radar i.e. 3km x 3km.

8. SMAP products

A list of all 15 planned SMAP products is provided in Table 1. It is important to highlight NASA’s footnote about latencies: The latencies shown in Table 1 are based on NASA's Earth Science Data Policy mission requirements, and therefore represent a hard upper limit. However, it is likely that actual latencies will be shorter than this.

Much like the SMOS products, the Level 2 SMAP products will probably be the most suitable for use by the agricultural community in New Zealand:

1. L2_SM_A is a high-resolution research-quality soil moisture product derived from high-resolution radar backscatter data (the L1C_S0_HiRes product). It will be provided on the Equal Area Scalable Earth-2 (EASE2) grid and will be available at 3km x 3km resolution. The accuracy of this product is not expected to be as high as the others i.e. around ±6%14.

2. The L2_SM_P soil moisture product is derived from the radiometer brightness temperature measurements in a similar way to the SMOS level 2 product14 and is available at 36km x 36km resolution, also on the EASE-2 grid.

3. L2_SM_AP is a combined active and passive (radar and radiometer) product that provides soil moisture measurements at 9km x 9km resolution. These are produced using the same retrieval algorithm as used for the L2_SM_P product but with additional ancillary data inputs and a differ-ent implementation due to the different resolution14. The expected accuracy is the same as that of SMOS i.e. ±4%.


10

Table 1 All planned SMAP products (taken from http://smap.jpl.nasa.gov/ 14)

# Over outer 70% of swath. ** The SMAP Project will make a best effort to reduce the data latencies beyond those shown in this table. * Product directly addresses the mission L1 science requirements.

Although the L2_SM_A data product is unlikely to be as accurate as the L2_SM_P and L2_SM_AP prod-ucts, it will produce useful soil moisture information at a much higher spatial resolution and may, there-fore, be more applicable for use by the agricultural community in New Zealand.

NASA expects that the radar will allow retrievals through medium density vegetation, which SMOS cur-rently struggles with.

9. Soil Moisture from Multispectral Satellite Data using SEBAL In this context ‘multispectral’ refers to the visible, Near InfraRed (NIR) and Thermal InfraRed (TIR) part of the electromagnetic spectrum i.e. wavelengths extending from hundreds of nanometres to millimetres.

The Surface Energy Balance Algorithm for Land (SEBAL) relates multispectral satellite images to estimate evapotranspiration15. SEBAL then calculates biomass growth, water deficit and, most importantly, soil moisture. At the core of SEBAL is a simple energy balance algorithm; the energy absorbed by the soil and air is the difference between the incoming and outgoing radiation (the energy used for photosynthesis is many orders of magnitude smaller and is therefore ignored). The warming rate of the soil and air can be calculated from TIR, leaving the remaining energy to be attributed to evapotranspiration. It is for this rea-son that SEBAL needs access to supplementary meteorological data such as wind speed, humidity, solar radiation and air temperature. The rate of evapotranspiration depends on the amount of water available in the soil, humidity, and wind speed15. Given that land surface characteristics such as surface albedo, leaf area index, vegetation index and surface temperature can all be derived from different parts of the measured spectrum, the only supplementary meteorological data SEBAL needs is wind speed, humidity,

http://smap.jpl.nasa.gov/


11

solar radiation and air temperature15. Because clouds are not transparent at all the frequencies at which SEBAL requires data, soil moisture derived from multispectral measurements are only possible under clear-sky conditions.

There are many Earth observing satellites that produce images in the visible, NIR and TIR parts of the spectrum. When compared to the L-band radiometry spacecraft, these multispectral instruments usually operate at much higher spatial resolution (e.g. 40m x 40m for Landsat 8), but, on the other hand, exhibit return times on the order of weeks. The MODIS (Moderate Resolution Imaging Spectroradiometer) in-strument, which is on-board the Terra and Aqua satellites, images the globe every one to two days and has a horizontal resolution of 1000 m. Hyperion, on-board the Earth Observing-1 satellite, follows Land-sat-7 one minute later, with the same resolution, but with a far larger spectral range. The SEBAL website claims that ETM, ASTER and instruments on-board NOAA’s POES satellites, can also provide the required inputs16.

10. SEBAL over New Zealand

Marc Greven and Wouter Meijninger performed a proof of concept for the use of SEBAL over New Zea-land using Landsat-7 images and compared the SEBAL-derived soil moisture values to in situ readings17. Unfortunately, the in situ measurements were only available once per week and this was usually not at the same time as the Landsat-7 overpasses which occurred every 16 days. This resulted in only three days of overlapping data being obtained. Greven and Meijninger also reported that many of the probes provid-ing the intercomparison data were not in appropriate positions e.g. alongside roads or under orchard nets which compromised their usefulness as a comparison standard. Because these probes were placed for monitoring irrigation, data from the probes were only available for irrigated land. In spite of these complications, Greven and Meijninger found an acceptable correlation of SEBAL-derived and surface probe measured soil moisture of r=0.62 for 11 March 201117. They concluded that “despite some current shortcomings in the results of this work, we believe there is a good potential for the use of this type of satellite information.”

11. Data assimilation

The temporal resolution of SMAP and SMOS seem ideal for a soil moisture product that can serve the agricultural community in New Zealand. However a spatial resolution of even 3km x 3km is far too coarse for practical use. The high spatial resolution of the multispectral data produced by SEBAL, on the other hand, suffers from too low a temporal resolution to be of much use. One possible solution to this is to simulate the time evolution of soil moisture at high spatial resolution over some region using a model, but to ensure that the simulation is tightly constrained by all available measurements which would in-clude space-based measurements of soil moisture. The process through which this is done is called data assimilation18. Data assimilation combines knowledge of the physics of an evolving system with meas-urements of various attributes of that system to ensure internal physical consistency of the system while generating fields that are consistent with the observations. It is this consistency and continuity, in both time and space, which makes data assimilation an attractive option. A Land Surface Model (LSM) is a type of numerical model well suited to simulating the hydrology of soils and which would likely be at the heart of any data assimilation system. As the LSM runs, it can assimilate data from a number of sources irre-spective of its temporal or spatial resolution.


12

In data assimilation, observations at current and past states of the system are compared to equivalent metrics derived from the model. Differences between observed and modelled quantities are used to tweak the evolution of the model to produce a best estimate of the current state of the system. This acts to balance any uncertainty in the data with any uncertainty in the constantly evolving model.

The Joint UK Land Environment Simulator (JULES)19 model appears to be an ideal LSM for a data assimila-tion purpose such as this. The most popular data assimilation method, typically used in numerical weath-er forecast models, is called 4-dimensional variational data assimilation. It requires the minimisation of a very complicated multi-dimensional cost function which is computationally demanding. For this reason, the less computationally expensive 3-dimensional variational data assimilation system is often used which only uses current observations and not historical observations to constrain the system.

12. JULES

JULES is a numerical land surface model which can function as a standalone model or can be coupled to an atmospheric model to obtain the precipitation, temperature and irradiance inputs that it re-quires19. JULES simulates interactions between the surface energy balance, the hydrological cycle, the carbon cycle and dynamic vegetation19. One of its outputs is soil moisture at various depths, making JULES an ideal candidate for the LSM at the core of a data assimilation system designed to combine soil moisture measurements from multiple sources. JULES is currently used by NIWA as part of the EcoCon-nect system.

Yang et al. (2014) assessed the ability of JULES to simulate soil related hydrological processes within New Zealand20. They compared the output from JULES at four different depths: 0.0–0.1m, 0.1–0.35m, 0.35–1.0m and 1.0–3.0m against in situ measurements at 32 locations over two years. They concluded that “at the 32 stations, the yearly mean absolute differences (MADs) of the simulated soil moisture volumetric ratio were about 5% or smaller, except at four South Island stations, where larger MADs of 8–12% were found”. They found that these four stations included a contribution from lateral soil water flow which could not be adequately modelled by the simplified one-dimensional configuration of JULES that was used for their study. Due to observational data constraints, Yang et al. were forced to assume a uniform soil texture for the entire soil column, but hypothesised that this was the largest source of error. It is worth noting that a location specific (as opposed to area mean) accuracy of 5% is high, especially when compared to SMOS which has a 4% accuracy over an assumed homogenous 40km x 40km pixel, or SMAP’s L2_SM_A product which has an expected 6% volumetric accuracy over a 3km x 3km pixel.

JULES is available free of charge and appears to meet the requirements for a numerical land surface model at the core of a proposed soil moisture assimilation system.

13. DISPATCH: Combining Microwave and Multispectral data to improve resolution

The DISaggregation based on Physical And Theoretical scale CHange algorithm (DISPATCH) is designed to downscale passive microwave data to a higher spatial resolution when high resolution multispectral ob-servations are available21. It works by relating the coarse soil moisture measurements taken by SMOS to


13

the evaporative efficiency of the soil which can be calculated from multispectral images21. Merlin et al.22 used DISPATCH to increase the resolution of SMOS measurements using 1km resolution MODIS ta, 90m resolution ASTER data and 60m resolution Landsat-7 data. When compared to in situ measure-ments, the DISPATCH-derived high resolution soil moisture maps were found to have both a higher corre-lation coefficient against the measurements and a lower root mean square error than the original SMOS data22. They attribute this improvement in the data product to the non-representativeness of the large SMOS pixel. DISPATCH would be highly applicable to providing accurate high resolution soil moisture maps for the agricultural community in New Zealand without being computationally expensive.

14. S-mapOnline: Fast access to New Zealand Soil Data Information about soil characteristics is required for the application of both the SEBAL algorithm and the JULES LSM. Landcare Research has integrated a variety of soil survey reports to create S-mapOnline, a single combined New Zealand soil map (available at http://smap.landcareresearch.co.nz/). S-mapOnline provides profiles of soil drainage, depth to hard soil, and profile available water at high spatial resolution. Profile available water is an indication of the soil's capacity to hold water assessed for the soil profile to a depth of 0.9m and is expressed as millimetres of water. S-mapOnline provided the required soil charac-teristics used by Greven and Meijninger for their SEBAL study17.

15. Root Zone Soil Moisture

One common shortcoming of SEBAL, SMOS and SMAP products is their effective depth. SEBAL relies on an energy balance model which requires knowledge of radiation emissions from the top few centimetres of soil. Dry soil is more transparent at microwave frequencies such that SMOS and SMAP occasionally

http://tldealers.com/themes/rkirrigation/bg.jpg taken from http://rkirrigation.net/

http://smap.landcareresearch.co.nz/).

http://tldealers.com/themes/rkirrigation/bg.jpg

http://rkirrigation.net/


14

measure to depths of ~7cm. Typically, however, the product is representative of the soil moisture in the top 5cm only. This is likely to be of less use to farmers as it is shallower than the root zone of most crops. It is also fair to assume that most farmers will be able to assess the moisture in the top layer of soil with-out sophisticated technology.

The soil moisture community is aware of the need for a vertically resolved product, and, to this end, have developed different methods to estimate root zone soil moisture from space-based measurements.

Bergson et. al. (2013) derived an empirical relationship between in situ root zone soil moisture measure-ments and soil moisture calculated at the surface using SEBAL23. They found a close correlation between the measured and SEBAL-derived soil moisture values (p<0.05, r2=0.84). However, the area over which the testing was performed was highly homogenous, and different empirical relationships would need to be found for different land covers.

NASA’s SMAP team will produce a level 4 root zone soil moisture product by assimilating L3_SM_AP data into the Goddard Earth Observing Model System, Version 5 (GEOS-5)7. GEOS-5 uses a catchment model coupled to a microwave radiative transfer model, to provide a 9km resolution product of average soil moisture between 0 and 100cm depth7. Once the L2_SM_AP product and ancillary data are available, the L4 processor must integrate the LSM time step, run the ensemble Kalman filter analysis, and then report the results. Given the ancillary data have a mean latency of 48 hours, the SMAP team estimates that the Level 4 product will only be available 3 days after sensing7. The assimilated product will provided the 3-hour average soil moisture value every 3 hours. While the 3 day latency on this data product may pre-clude its use by the New Zealand agricultural sector, it may be useful for calibrating or validating other products. Ford et. al.24 related SMOS measurements of surface soil moisture to root zone measurements over Oklahoma and Nebraska. They then used an exponential decay filter to estimate root zone (which they define as 25-60cm depth) soil moisture from the SMOS surface measurements. After calibration,

NASA Dirt-Tracking SMAP Satellite As it launches spaceward on a United Launch Alliance Delta II rocket at 6:22 a.m. PST (9:22 a.m. EST/1422 GMT) from Vandenberg Air Force Base in California on Jan. 31, 2015.


15

they found a correlation of r2=0.57 and r2=0.24 for Oklahoma and Nebraska respectively. Surprisingly, they found that all but one of their 33 Oklahoma stations showed a smaller mean absolute error between their filtered root zone estimate and in situ measurements compared to the SMOS retrieval and in situ surface measurements. They concluded that while this approach is reasonably accurate, very fast and computationally efficient, it relies on homogeneity across a soil column and site-specific calibration.

CATDS is currently developing a SMOS derived root zone soil moisture product referred to as a Drought Monitor index25. There is little information currently available about the product, but we know that it uses L3 SMOS data and will simulate soil moisture at several depths. Given the latency of the SMOS L3 product, we can assume that this L4 root zone product will have too large a latency to be used as input for near-real-time data assimilation, but, much like the similar SMAP product, it may be useful for calibra-tion and validation purposes.

16. Conclusions

In writing this report, our goal has been to explore options whereby the New Zealand farming community might capitalize on freely available space-based measurements of soil moisture to guide decision making around the use of water resources, in particular for irrigation.

Requirements are for the soil moisture product to be available at near-real-time, with a high spatial and temporal resolution, and at root zone depth. Currently there is no single product that fulfils these re-quirements.

SMOS and SMAP retrieve soil moisture daily but with a coarse spatial resolution. The SMOS level 2 data product and the SMAP L2_SM_AP data product both exhibit an accuracy of ±4% at a resolution of 40km x 40km and 36km x 36km respectively. SMAP’s L2_SM_A product will improve this resolution to 3km x 3km but at a reduced accuracy of ±6%.

SEBAL allows the retrieval of soil moisture from multispectral images at a resolution that is orders of magnitudes better than the SMOS and SMAP data products. For example, Landsat-7 has a grid size of 40m x 40m. These satellites typically have a very long revisit time, usually only imaging New Zealand twice a month.

The DISPATCH algorithm has the ability to combine microwave and multispectral measurements to pro-vide high resolution soil moisture maps at an improved accuracy.

All space-based measurements only retrieve the soil moisture at the surface. Root zone data can be modelled or estimated from surface values, but these process are either very slow or require pixel specif-ic calibration.

The use of data assimilation is likely the only viable solution for generating useful soil moisture maps for the New Zealand agricultural community. By feeding both microwave and multispectral data into a land surface model such as JULES, accurate root zone soil moisture products at any time, for anywhere in New Zealand, can be generated.


16

References 1. Brazil, S., ed. New Zealand Official Yearbook. Statistics New Zealand. p. 357. ISBN 978-1-86953-

717-3, 2008. 2. http://www.scoop.co.nz/stories/SC1208/S00032/water-conservation-orders-have-no-role-in-

sustainable-future.htm 3. http://www.stats.govt.nz/browse_for_stats/industry_sectors/agriculture-horticulture-

forestry/AgriculturalProduction_final_MRJun12final.aspxn 4. https://www.niwa.co.nz/climate/information-and-resources/drought 5. http://www.landcareresearch.co.nz/publications/newsletters/discovery/discovery-issue-

30/variable-rate-irrigation 6. Monerris, A. and Schmugge, T., Soil moisture estimation using L-band radiometry, Chapter 21 in

Advances in Geoscience and Remote Sensing, 2009. 7. Entekhabi et al., SMAP Handbook: Soil Moisture Active Passive, available at

http://smap.jpl.nasa.gov/mission/description/ 8. http://www.cv.nrao.edu/course/astr534/BlackBodyRad.html 9. Behari, J. Microwave Dielectric Behaviour of Wet Soils, Volume 8 of Remote Sensing and Digital

Image Processing 10. Hallikainen, M.T. et al., Microwave Dielectric Behavior of Wet Soil - Part 1: Empirical Models and

Experimental Observations, Geoscience and Remote Sensing, IEEE Transactions on, GE-23, 1, 1985. 11. Slides titled “SMOS Level 2 Soil Moisture Retrieval Algorithms” by Philippe Richaume, SMOS Train-

ing Course, ESAC, 20 May 2015. 12. http://www.cesbio.ups-tlse.fr/SMOS_blog/?page_id=3827 13. Rodriguez-Fernandez, N.; Richaume, P.; Aires, F.; Prigent, C.; Kerr, Y.; Kolassa, J.; Jimenez, C.; Cab-

ot, F. and Mahmoodi, A., Soil moisture retrieval from SMOS observations using neural networks, Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International. DOI: 10.1109/IGARSS.2014.6946963

14. http://smap.jpl.nasa.gov/ 15. Bastiaanssena, W.G.M.; Menentia, M.; Feddesb, R.A. and Holtslagc, A.A.M., A remote sensing sur-

face energy balance algorithm for land (SEBAL) 1. Formulation, Journal of Hydrology, 212–213, 198–212, 1998.

16. http://www.eleaf.com/Technology-sebal 17. Greven, M. and Meijninger, W., Satellites for improved irrigation advice, Available at:

http://www.mrc.org.nz/wp-content/uploads/2012/11/7611-Marc-Greven-Satellites-for-improved-irrigation-advice1.pdf

18. Houtekamer, P.L. and Mitchell, H.L., Data assimilation using an ensemble kalman filter technique. Mon. Wea. Rev., 126, 796–811, 1998.

19. Best, M.J. et al., The Joint UK Land Environment Simulator (JULES), model description – Part 1: En-ergy and water fluxes, Geosci. Model Dev., 4, 677-699, 2011.

20. Yang, Y.; Uddstrom, M.; Revell, M. and Moore, S., Soil moisture simulation by JULES in New Zea-land: verification and sensitivity tests, Meteorol. Appl. 21, 888–897, 2014.

21. Djamai, N. et al., Downscaling SMOS derived soil moisture for very wet conditions using a physical-ly based approach, Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International , 3198, 3201, 13-18 July 2014.

http://www.scoop.co.nz/stories/SC1208/S00032/water-conservation-orders-have-no-role-in-

http://www.stats.govt.nz/browse_for_stats/industry_sectors/agriculture-horticulture-

https://www.niwa.co.nz/climate/information-and-resources/drought

http://www.landcareresearch.co.nz/publications/newsletters/discovery/discovery-issue-

http://smap.jpl.nasa.gov/mission/description/

http://www.cv.nrao.edu/course/astr534/BlackBodyRad.html

http://www.cesbio.ups-tlse.fr/SMOS_blog/?page_id=3827

http://smap.jpl.nasa.gov/

http://www.eleaf.com/Technology-sebal

http://www.mrc.org.nz/wp-content/uploads/2012/11/7611-Marc-Greven-Satellites-for-improved-


17

22. Merlin, O. et al., Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An eval-uation study at 3 km and 100 m resolution in Catalunya, Spain, Remote Sensing of Environment, Elsevier, 2012.

23. Bezerra, B.G. et al., Estimation of soil moisture in the root-zone from remote sensing data, Rev. Bras. Ciênc. Solo [online], 37(3), 2013.

24. Ford, T.W.; Harris, E. and Quiring, S.M., Estimating root zone soil moisture using near-surface ob-servations from SMOS, Hydrol. Earth Syst. Sci., 18, 139-154, 2014.

25. http://www.cesbio.ups-tlse.fr/SMOS_blog/?page_id=2589 26. Slides titled “Brightness temperature modelling of vegetated soils using L-MEB” by Jennifer Grant,

SMOS Training Course, ESAC, 20 May 2015.

http://www.cesbio.ups-tlse.fr/SMOS_blog/?page_id=2589


18

Appendix

SMOS Retrievals for Low Vegetation Cover Most pixels that contain New Zealand farmland will fall into the ‘wet soil with vegetation’ category (see the “How SMOS calculates soil moisture from brightness temperature” box). This holds for grassy pas-tures and most shrubs and crops. For these pixels, the SMOS level 2 processor selects the L-band Micro-wave Emission of the Biosphere model (L-MEB) to retrieve soil moisture. The L-MEB is supplied with Lev-el-1C brightness temperatures (see “Measuring Brightness Temperature” in Appendix), vegetation data, and auxiliary surface data provided by the European Centre for Medium-Range Weather Forecasting (ECMWF)26. Not only does vegetation emit its own L-band radiation, it also attenuates any radiation trav-eling through it. Another complication that L-MEB addressed is the brightness temperature of the sky resulting from atmospheric and galactic sources.

The equation at the heart of L-MEB is26:

푇 (푃,휃) = 푒 푇 훾 + (1 − 휔)(1 − 훾)푇 + (1 −휔)(1 − 훾)푇 (1 − 푒 )훾 + 푇 , 훾 (1 − 푒 ) (3)

1 2 3 4

Where 푇 is the measured brightness temperature, 푃 is the polarization (horizontal or vertical) and θ is the incidence angle. 푇 is the temperature of the soil and 푇 is the temperature of the vegetation. The transmissivity of the vegetation (훾) and the vegetation scattering albedo (휔) also depend on polarization and incidence angle. 푇 , is the brightness temperature of the sky or space. Given a brightness tempera-ture, it is the emissivity of the soil (푒 ) that must be solved for since, using equation (2), soil moisture can then be derived.

The four terms in equation (3) represent contributions from the four different mechanisms shown in Fig-ure (1).

Considering each term individually:

The soil factor (1): This is the largest contributor to the total brightness temperature and depends on the soil temperature and soil emissivity. Because the microwave radiation emitted from the soil passes through the overlaying vegetation before arriving at the spacecraft, it is scaled by the vegetation trans-missivity.

The vegetation factor (2): This is the microwave radiation emitted by the vegetation. The fraction of the emitted radiation lost as a result of scattering by the vegetation is given by 휔. It follows that the fraction not scattered or lost is absorbed, and is given by 1 −휔. Similarly, the fraction that is not transmitted is 1 − 훾. This is the fraction of the vegetation that is opaque, and so emits radiation. Taking these two fac-tors into account gives a vegetation emission of (1 −휔)(1 − 훾)푇 .

The vegetation-soil factor (3): Radiation from vegetation can reach MIRAS by a second process i.e. L-band radiation emitted by the vegetation is not only emitted upwards but in all directions. The radiation emitted downwards can then reflect off the soil and travel back upwards through the vegetation. The


19

emission of the radiation by the vegetation is the same as in term 2. The remaining term in the vegeta-tion-soil factor (term 3 of equation 3) is (1 − 푒 )훾. With soil emissivity given by 푒 , the reflectance is (1 − 푒 ). The vegetation emission which is reflected by the soil, and is then transmitted back through the canopy before being detected, therefore includes this (1 − 푒 )훾 term.

The sky factors (4): Very weak L-band radiation is emitted by some galaxies and other astronomical sources. This combines with the radiation emitted by the sky which reflects off the soil and is detected by MIRAS. The reflection off the soil is the same as in term 3 but now accounts for transmission through the vegetation layer twice, once on the way down and once on the way back up after reflecting off the soil. This explains the 훾 factor.

The L-MEB assumes that both the soil and the vegetation are homogenous over the pixel being sampled and are both in thermal equilibrium. It also only ignores second and higher order reflections.

The L-MEB returns soil emissivity which is then used to calculate the dielectric constant. This calculation is more complicated than suggested by equation (2) but the details of the more complex calculation are considered outside the scope of this report. Finally a dielectric mixing model (previously the Dobson model, now the Mironov model) estimates soil moisture from the dielectric constant26.

Figure 1

The source of microwave radiation incident on the MIRAS which are taken into account by L-MEB. Ray 1 is emitted by the soil, and then attenuated by the vegetation. Ray 2 travels upwards from the vegetation. Ray 3 is emitted by the vegetation, travels downwards before being reflected back through the vegetation from the soil. Ray 4 is the radiation emitted by the sky/galaxy which reflects off the soil. Adapted from 26.


20

42 Russell Street, Alexandra 9320 Ph: 03 448 8118 www.bodekerscientific.com

http://www.bodekerscientific.com

42 Russell Street, Alexandra 9320 Ph: 03 448 8118 www.bodekerscientific.com

http://www.bodekerscientific.com

space-based soil moisture measurements …...bodeker scientific ltd space-based soil moisture...

Documents