Real-time data of reference evapotranspiration (ET₀) at different space-time scales are essential to regional agricultural drought assessment, water accounting at the watershed to basin scale, and provide irrigation advisory to farmers. Here, we present a data-fusion approach that integrates satellite-based insolation product (8 km) from an Indian geostationary satellite (Kalpana-1) sensor (VHRR; Very High Resolution Radiometer) and high-resolution (~ 5 km) short-range weather forecast into an FAO56 model based on the classical Penman–Monteith (P-M) formulation. Five year (2009–2013) mean monthly estimates from the daily ET₀ product over the Indian landmass were found to vary between 10 and 350 mm. It increased from January to May (70–350 mm), followed by a decrease to reach the lowest in November (10–140 mm), thus typically showing unimodal distribution. The comparison of daily space-based and station-based estimates (at six ground stations) produced a root mean square deviation (RMSD) ranging from 21% to 38% for 977 paired data sets with the correlation coefficient (r) varying from 0.32 to 0.82. The error was reduced from 25% to 10% with an increase in ‘r’ from 0.43 to 0.98 for daily to 10 day summation period. Spatial grid-to-grid comparison of monthly ET₀ estimates with Global Data Assimilation System (GDAS) potential evapotranspiration (PET) showed RMSD within a range of 1.4–18.4% for most of the months, except for two. Further ET₀ analysis over normal and drought years showed that it could be used for comprehensive drought assessment with other existing indicators.

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.

Agriculture productivity at spatio-temporal scales can be modelled through quantification of biophysical parameters like LAI (leaf area index) and radiation parameters from satellites. The Indian geostationary satellite INSAT 3A CCD was used to retrieve agricultural LAI at regular temporal intervals from the ProSail 1-D (Dimensional) canopy radiative transfer (CRT) model. The ProSail model was customized to simulate reflectance for three CCD spectral bands. The model was run in forward mode and then inverted by using reflectances from the CCD and the generated LUT by applying the least square distance approach to retrieve LAI for agricultural crops. Daily CCD data from January 1 to March 30, 2010 at 0700 GMT were used to retrieve agricultural crop LAI data. The validation of daily retrieved LAI was done with available in situ measurements over wheat crops in Punjab, Haryana and Madhya Pradesh states. The overall root mean square error (RMSE) of 0.84 with correlation of 0.8 was observed for 20 in situ measured LAI at different phenological phases of wheat crops. Retrieved INSAT CCD LAI has been compared with LAI retrieved from high-resolution IRS P6 AWiFS using an empirical approach for wheat crop. The CCD-derived wheat crop LAI showed a RMSE of 0.45 (n = 55, 14.2 percent from mean) with mean absolute error (MAE) of 0.34. It was also compared with the 8-day MODIS TERRA global LAI product from January to March 2010. The LAI profiles extracted for different regions of India representing different crops using CCD data and MODIS products were compared and an overall RMSE of 2.25 (n = 156, 73 percent from mean) with MAE of 2.85 was observed. INSAT CCD-retrieved LAI was further used for wheat yield estimation over Madhya Pradesh state. At district level, yield showed a RMSE of 516.6 kg ha⁻¹ with 29.4 percent deviation from the mean. Our demonstrative case studies recommended coupled use of satellite observations from multiple EO missions and radiative transfer simulation to make efficiency-based approaches operationally viable for regional crop yield estimation in near real time.