[IEEE 2014 Third International Conference on Agro-Geoinformatics - Beijing, China (2014.8.11-2014.8.14)] 2014 The Third International Conference on Agro-Geoinformatics - Impacts of crop rotation on vegetation condition index for species-level drought monitoring

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<ul><li><p>Impacts of Crop Rotation on Vegetation Condition Index for Species-level Drought Monitoring </p><p>Yonglin Shen, Xiuguo Liu, and Youxin Huang Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China </p><p>E-mail: yonglinshen@gmail.com </p><p> AbstractUnderstanding crop rotation on satellite remote </p><p>sensing derived vegetation indices is very necessary, because it helps us develop more scientific methods or indices for revealing the mechanism of agricultural drought in the species-level. In this paper, the impacts of crop rotation on vegetation condition index (VCI) was explored. First, we tried to justify that whether crop rotation is a typical agricultural practice in the study area, and counted the proportion of crop planting changes over any pixel in multi-year; and second, a neighbor-average based VCI index was developed for species-level cases, and the comparison with traditional VCI index had been conducted. The experimental study was conducted in state of Iowa, the primary corn-producing state in the Corn Belt of the United States. Moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series and NASS's cropland data layer (CDL) among years 2002-2013 were utilized for data analysis. The results shown that crop rotation limited impacts the VCI index on corn drought monitoring across the study area. Even so, the research inspires a more accurate and valuable mean in the future for examining the mechanisms and processes of species-level drought monitoring. </p><p>Keywordscrop rotation; corn; drought; remote sensing; vegetation condition index (VCI) </p><p>I. INTRODUCTION Human-induced climate change has played a hand in the </p><p>increases in many types of extreme weather, e.g., agricultural droughts. Agricultural droughts have escalated to be more frequent, severe and prolonged. and continued to impact to economies, societies and environments. Therefore, drought monitoring in accurate, dynamical, species-level is crucial for ensuring adequate general food availability. </p><p>Satellite remote sensing, which provides consistent measurements at broad-scale and frequent time intervals, has been increasingly adopted in drought monitoring on regional, continental, and global scales [1]. Analysis of multi-temporal remote sensing images could be a reliable and cost-effective approach. High temporal frequency products are commonly collected from coarse-to-moderate spatial resolution platforms, e.g., moderate resolution imaging spectroradiometer (MODIS). Especially, the spectral bands of MODIS NDVI are specifically designed for agricultural monitoring, which have been better navigation, atmospheric correction, reduced geometric distortions and improved radiometric sensitivity [2]. The most successful of drought monitoring approaches are based on tracking the temporal change of a vegetation index </p><p>(normalized difference vegetation index, NDVI) along the range of values observed in the same period in previous years, e.g., vegetation condition index (VCI) [3]. </p><p>NDVI-based satellite indices (e.g., VCI index) are suitable for agricultural drought monitoring [4]. However, VCI index is sensitive to the inter-annual variations of NDVI caused by land use and land cover changes [5]. For crop field, crop rotation could be the main factor affects the inter-annual land cover. Yagci et al. [4] and Deng et al. [6-7] explored the impacts of land cover change on VCI index, and found that the index is sensitive to changes in land cover. Brown et al. [8] alleged that it is difficult to isolate land cover change with weather interference on NDVI-derived satellite drought index. Therefore, understanding the crop rotation on satellite remote sensing derived vegetation indices is very necessary, because it helps us develop more scientific methods or indices for revealing the rules of agricultural drought in species-level. </p><p>In this paper, we focus on evaluating the impacts of crop rotation on VCI index for species-level (e.g., corn crop) drought monitoring. Species-level VCI index was developed to estimate the drought of corn crop in the growth season. Specifically, two problems are expected to be explored in this study: 1) Whether crop rotation impacts on the corn-related VCI value, and 2) how crop rotation impacts the species-level drought monitoring. </p><p>II. STUDY AREA, DATA SETS, AND DATA PROCESSING </p><p>A. Study Area State of Iowa (4036'N~4330'N, 895'W~9631'W), </p><p>which is the predominant corn-producing region, was selected for this study. It locates in the US Corn Belt, the most intensively cultivated region of the Midwest United States. In 2012, almost half of the United States suffered drought disaster. All of Iowa state was considered to be in severe drought [9], and 75% of them experienced extreme-drought conditions by October [10]. </p><p>B. Data Sets Two primary data sets that span from 2002 to 2013 of corn </p><p>growing seasons (early of May throughout the end of September) were utilized for this study. </p><p>1) NDVI time series, which was derived from MODIS MOD13Q1 products, was computed from atmospherically corrected bi-directional surface reflectance that have been masked for water, clouds, heavy aerosols, and cloud shadows. The products are 16-day composite at 250 m spatial resolution </p></li><li><p>in the Sinusoidal projection. The data sets is publicly available via the 'level 1 and atmosphere archive and distribution system (LAADS)' (http://ladsweb.nascom.nasa.gov/index.html). </p><p>2) Cropland data layer (CDL), which has been produced annually by USDA/NASS. The products are available for the contiguous United States since 2000, and have been made for free access via the CropScape (http://nassgeodata.gmu.edu/) [11]. The spatial resolution of NASSs CDL differs in years. Years 2006-2009 have a spatial resolution of 56 m, and the rest 30 m. </p><p>C. Data Preprocessing VCI is a typical NDVI-derived index for measuring </p><p>drought conditions through vegetation vigor or greenness [1]. In order to extract the pixel- and species-level VCI value, data pre-processing was conducted over the original 16-day composite MODIS NDVI. The data pre-processing mainly includes: Image mosaic, which assemble strip images into a whole image covering target area based on the geo-referencing information; Projection, which transforms NDVI data from 'Sinusoidal projection' into 'USA Contiguous Albers Equal Area Conic projection, USGS version'; Image clip, which clips projected image to region of interest (ROI); And image masking, which eliminates non-corn pixels from 16-day composite NDVI image with the mask of NASSs CDL. </p><p>Cropland masking is critical for an accurate phenology analysis, which uses a mask image to exclude the components of other cropland covers. In this study, the process of cropland masking is conducted to extract only corn pixels from the NDVI image. During the masking process, any NDVI pixel, in which at least 60% area is recognized as corn with reference to the CDL data, will be kept. </p><p>D. Pixel- and Species-level VCI Index Traditional VCI index derived from NDVI by normalizing </p><p>to NDVIs multi-year maximum and minimum values [10]. It can be calculated by the following formula, </p><p> , , (1) + = . (1) (1) where , is traditional VCI value computed at the th Julian day of year , and NDVI , is the corresponding NDVI value. and are a dynamic neighbor-average minimum and maximum NDVI values over a pixel in multi-year. </p><p>The species-level VCI index, which considers crop types over a pixel in multi-year. The multi-year maximum and minimum values of NDVI were searched dynamically in neighborhood pixels, in the case that the pixel in a certain year not belongs corn crop. The species-level index , can be calculated by, </p><p> , , (2) + = . (1) (1) where and are the dynamic neighbor-average maximum and minimum NDVI values in </p><p>multi-year. For a certain year over a pixel, NDVI value was confirmed by following rules: if the center pixel belongs corn crop, then NDVI value of this pixel is used. Otherwise, search for the neighbor pixels, and confirm NDVI value by neighbor average. The size of neighborhood was set into eight in maximum, i.e., 2 km. </p><p>The values of pixel- and species-level VCI index range from 0 to 1. By referring to the U.S. Drought Monitor [10], the VCI-based drought severity is categorized by five levels, i.e., no drought, abnormally dry, moderate, severe, extreme drought, and exceptional drought [13], which corresponding to intervals of 0.45,1 , 0.35,0.45 , 0.25,0.35 , 0.15,0.15 , 0.05,0.15 , and 0, 0.05 , respectively. </p><p>III. RESULTS AND DISSCUSIONS </p><p>A. Interannual Variability of Cropland Crop rotation can improve soil structure, as well as </p><p>reducing the need for artificial fertilizers. Thus, this practices has been normally utilized in the agricultural regions of the United States. Fig. 1. illustrates the changes on corn field under the crop rotation mechanism across the state of Iowa in multi-year (2002 throughout 2013). Each pixel value in the figure range from 0 to 1, and indicate the probability of corn planted over the past 12 years. </p><p> Fig. 1. Multi-year corn field changes in state of Iowa, under the crop rotation mechanism (2002 throughout 2013). Each pixel value in the figure range from 0 to 1, and indicate the probability of corn planted over the past 12 years. </p><p> Fig. 2. Statistics the number of corn planted on all pixels of corn field in state of Iowa in the period of 2002-2013.</p></li><li><p>Intensity (%): 95.2 .. 2.1 1.2 0.7 0.4 0.4 </p><p>Intensity (%): 95.6 .. 2.0 1.2 0.6 0.3 0.3 </p><p>(a) Pixel-level VCI (DOY=161, year=2012) </p><p> (b) Species-level VCI (DOY=161, year=2012) </p><p>Intensity (%): 11.1 .. 4.4 5.1 5.7 5.9 67.9 </p><p>Intensity (%): 11.8 .. 4.5 5.1 5.5 5.6 67.5 </p><p>(c) Pixel-level VCI (DOY=225, year=2012) </p><p> (d) Species-level VCI (DOY=225, year=2012) </p><p>Intensity (%): 58.6 .. 11.2 8.8 6.5 4.5 10.5 </p><p>Intensity (%): 68.5 .. 8.7 6.4 4.6 3.3 8.4 </p><p>(e) Pixel-level VCI (DOY=225, year=2013) </p><p> (f) Species-level VCI (DOY=225, year=2013) </p><p>Intensity (%): 7.3 .. 6.9 9.7 11.6 14.3 50.2 </p><p>Intensity (%): 7.7 .. 6.7 9.4 10.8 12.6 52.7 </p><p>(g) Pixel-level VCI (DOY=273, year=2012) </p><p> (h) Species-level VCI (DOY=273, year=2012) </p><p>Fig. 3. Comparison between pixel-level VCI and species-level VCI in state of Iowa in corn growth seasons. (DOY= day of year, = no drought, = abnormally dry, = moderate drought, = severe drought, = extreme drought, and = exceptional drought) </p></li><li><p>For each accumulated years (ranges from 1 to 12), the percents of corn planted on all pixels of corn field in state of Iowa in the period of 2002-2013 have been estimated. The results was shown in Fig. 2. We found that the highest value is 28.4%, which corresponds to six years that plant corn crop in the same crop field, i.e., a crop field that plants corn crop is every two years. Further, the sum of parentage for five, six and seven years is about 53.5%. It means that the crop rotation mechanism is obviously for corn crop in the state of Iowa. </p><p>In Fig. 2, there are only 12.5% corn field pixels planting corn crop six times over the past 12 years. Because, VCI index derived from NDVI by normalizing to NDVIs multi-year maximum and minimum values. Few years cultivate corn crop over a pixel would be a potential problem for the calculated of VCI index. Therefore, a neighbor-averaging based method was employed to solve the problem of VCI index calculation </p><p>B. Cormparison between Pixel- and Species-level VCI Comparison between pixel-level VCI and species-level </p><p>VCI was presented in Fig. 3. The comparisons were conducted in state of Iowa in corn growth seasons. Four time points were selected, i.e., the 161th, 225th and 273th day in 2012, and the 225th day in 2013. Generally, corn crop emerged in the 161th day, and matured in the 273th day. Severe droughts occurred in the 225th day. The difference between pixel- and species-level VCI of the three time points is limited. While, in 2013, the difference is mainly on no drought category. As shown in Fig. 3(e), the percentage of no drought category for pixel-level VCI is about 58.6%, while the species-level VCI is about 68.5% (Fig. 3(f)). </p><p>The limited difference between pixel- and species-level VCI in Fig. 3. may caused by server factors. First, corn and soybean are main crop planted in state of Iowa, the corresponding percents are 36.6% and 25.4% respectively. Soybean crop normally planted late for corn crop for 1-2 weeks. Thus, crop rotation with corn and soybean will similar with VCI index. Further study on the effect of crop rotation is needed to determine if this applies to other areas. </p><p>IV. SUMMARY AND CONCLUSION This study had evaluated the impacts of crop rotation on </p><p>VCI index in the species-level drought monitoring applications. VCI index reflects terrestrial vegetation cover and growth conditions. Although it has been frequently utilized in agricultural cases, rarely has reported for species-level drought monitoring. Theoretically, land use and land cover changes, e.g., crop rotation, inevitably impact the species-level drought monitoring. Although we try to analyze crop rotation on corn crop drought monitoring, the results shown that the influence is very limited. </p><p>A variety of factors inevitably affect the results of the analysis. 1) Spatial resolution of MODIS NDVI. We have on the field measurement to part of Iowa state which was selected on the Google Earth. We found that most of the crop field in the length and width is around 1 km. The spatial resolution of MODIS is 250 m. A field will be </p><p>covered at least four MODIS pixels. Thus, pixel mixing will noising the images. 2) The reliability of CDL data [14]. 3) crop phenology changes. The computing of VCI refers to calendar year, which would be easily interfered with interannual phenological changes. Further studies will focus on the improving of data processing, e.g., cloud removing, mixed pixel analysis. More robust species-level VCI index will be developed by verified in different regions and diverse data sets. </p><p>ACKNOWLEDGMENT This work was supported in part by the China </p><p>Postdoctoral Science Foundation under Grant 2013M542086, and by the Fundamental Research Funds for the Central Universities under Grant CUGL140834. </p><p>REFERENCES [1] Y. Shen, L. Di, L. Wu, G. Yu, H. Tang, G. Yu, and Y. Shao. "Real-</p><p>time estimation of corn progress stages using hidden Markov models with multisource features," Remote Sensing, vol. 5, no. 4, pp. 1734-1753, 2013. </p><p>[2] R. Fensholt, K. Rasmussen, T.T. Nielsen, and C. Mbow. "Evaluation of earth observation based long term vegetation trendsIntercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data," Remote Sens. Environ., vol. 13, pp. 18861898, 2009. </p><p>[3] F. N. Kogan. "Remote sensing of weather impacts on vegetation in non-homogeneous areas," Int. J. Remote Sens., vol. 11, pp. 1405-1419, 1990. </p><p>[4] A. L. Yagci, L. Di, and M. Deng. "The effect of land-cover change...</p></li></ul>

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