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Quantifying the area and spatial distribution of double- and triple-cropping croplands in India with multi-temporal MODIS imagery in 2005

Biradar, Chandrashekhar M., Xiao, Xiangming
International journal of remote sensing 2011 v.32 no.2 pp. 367-386
Census of Agriculture, agricultural land, algorithms, climatic factors, crop production, crops, data collection, double cropping, image analysis, moderate resolution imaging spectroradiometer, remote sensing, statistics, surface water, vegetation, India
Cropping intensity, defined as the numbers of crops (single, double and triple cropping) per year in a unit cropland area, is one of the major factors in crop production and agriculture intensification. Changes in cropping intensity are driven by a number of socio-economic and climate factors. Agricultural census statistics of India contain much information on the distribution of crop types and total cropland area but contain no detailed spatially explicit information on the spatial distribution of double and triple cropping fields. In this study, we used multi-temporal images from the Moderate Resolution Imaging Spectroradiometer (MODIS) in 2005 to identify and map double-and triple-cropping land in India. The phenology algorithm we developed is based on the temporal profile analysis of three vegetation indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI). The MODIS-based phenology algorithm estimates that India has a total area of 57.8 million ha of multiple- (including both double and triple-) cropping fields in 2005, which is about 6.4% higher than the estimate (54.3 million ha) from the agriculture census data in 2004/2005. The resultant MODIS-derived map of multiple-cropping croplands at 500-m spatial resolution in 2005 was compared with the state and district levels using the agricultural census data. We also evaluated the MODIS-derived map using in situ observation data collected in India. The results of this study demonstrate the potential of the phenology algorithm in delineating double-and triple-cropping practice in India. The resultant geospatial dataset of multiple-cropping croplands in India is useful for the study of irrigation, food security and climate.