Modeling temporal growth profile of vegetation index from Indian geostationary satellite for assessing in-season progress of crop area

Abstract

Highlights• In-season agricultural area tracking at regular interval from geostationary satellite.• Modelling of temporal profile of vegetation index spread across two consecutive agriculture seasons to track crop area.The crop area estimates and their frequent updates in an agricultural growing season are essential to formulate policies of country's food security. A new methodology has been developed with high temporal vegetation index data at 1000 m spatial resolution from Indian geostationary satellite (INSAT 3A) to track progress of country-scale rabi (post-rainy) crop area in six agriculturally dominant states of India. The 10-day (dekad) maximum normalized difference vegetation index (NDVI) composite products at 0700 GMT (Greenwich Mean Time) were generated and used in the study. A cubic function was fitted to NDVI temporal profile on the training data-sets of 2009-2010. Model parameters were standardized over 40 agroclimatic subzones, which were used to estimate rabi crop area at 10-day interval in the next two seasons. Uncertainties in the model, in terms of days, were found to be less than (3-8 days) compositing period. The INSAT-based estimates showed -18.1% to 14.6% deviations from reported rabi crop area. Subpixel heterogeneity was found to be the major cause for the delay in crop area tracking in study region. The interseasonal variability in the estimate was consistent with the reported statistics with a correlation coefficient of 0.89. A comparative study showed that INSAT estimated rabi area had 16.36% deviation from high spatial resolution AWiFS (Advanced Wide-Field Sensor)-estimated area at 2 km × 2 km grid over ground observation points. It is recommended that high temporal NDVI product with finer spatial resolution satellite would, by offsetting the impact of subpixel heterogeneity, enable improved country-scale crop area monitoring.

Yield Estimation of Wheat Using Cropland Masks from European Common Agrarian Policy: Comparing the Performance of EVI2, NDVI, and MTCI in Spanish NUTS-2 Regions

“…There was more aggregation due to its 1 km resolution, which integrates the spectral response of different crops and land covers in each pixel [81,82]. This resolution has been used to distinguish between agricultural or non-agricultural areas [83] or to map natural systems [69], but the high spatial variability and the complexity of agricultural systems required data with a greater spatial resolution. The main reason could be the presence of other crops in the arable land category.…”