Plant growth processes and productivity of agroecosystems depend highly on evapotranspiration from the land (soil-crop cover complex) surface. A study was carried out using MODIS TERRA optical and thermal band data and ground observations to estimate evaporative fraction and daily actual evapotranspiration (AET) over agroecosystems in India. Five study regions, each covering a 10 km610 km area falling in agricultural land use, were selected for ground observations at a time closest to TERRA overpasses. The data on radiation and crop parameters in paddy (irrigated and rainfed), cotton (rainfed), groundnut (residual moisture) crops were recorded at 14-day intervals between August 2003 to January 2004 from 2 km62 km homogeneous crop patches within each study region. Eight MODIS scenes in seven optical (1, 2, 3, 4, 5, 6, 7) and two thermal bands (31, 32) level 1B data acquired from the National Remote Sensing Agency, Hyderabad, India and resampled at 1 km, were used to generate surface albedo (a), land surface temperature (T s, MODIS ) and emissivity (e s ). Evaporative fraction and daily AET were generated using a single source energy balance approach with (i) ground based observations only ('stand alone' approach), and (ii) 'fusion' of MODIS derived land surface variables on cloud free dates and coincident ground observations. Land cover classes were assigned using a hierarchical decision rule applied to multi-date Normalized Difference Vegetation Index (NDVI). The exponential model could be fitted between 1-EF ins, ground (ground based evaporative fraction) and difference between T s, MODIS and air temperature (T a ) with R 2 50.77. Linear fit (R 2 50.74) could be obtained between 1-EF ins, ground and temperature vegetation dryness index (TVDI), derived from T s, MODIS -NDVI triangle. Energy balance daily AET from the 'fusion' approach was found to deviate from water balance AET by between 4.3% to 24.5% across five study sites with a mean deviation of 11.6%. The root mean square error (RMSE) from the energy balance AET was found to be 8% of the mean water balance AET. The satellite based energy balance approach can be used to generate spatial AET, but needs more refinements before operational use in the light of progress in algorithms and their validation with huge datasets.

The time of forcing of spatial LAI to crop models at single or multiple stages is important to simulate crop biomass and yield in varying agro-climatic conditions and scales. The high temporal resolution (5-day) by Advanced Wide Field Sensor (AWiFS) on-board Resourcesat-1 Satellite IRS-P6 with 56 m spatial resolution and large swath (740 km) has substantially increased the availability of regional clear sky optical remote sensing data. The present study aimed at developing empirical vegetation index VI-LAI models for wheat using AWiFS optical data in four bands and in-situ measurements sampled over five different agro-climatic regions (ACRs) during 2005-2006 followed by validation during 2006-2007. While nonlinear relations exist for all the three normalized indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and Green NDVI, linear relation was the best fit for ratio vegetation index (RVI). Both NDVI and RVI models generally showed better correlation ranges (0.65-0.84 for NDVI and 0.37-0.76 for RVI) than other indices. The common NDVI-LAI model was found to produce lower root mean square errors (RMSE) between 0.5 and 1.1 from pooled model than those between 0.5 and 1.32 from regional models. The rate of substantial increase in errors from NDVI-LAI model (RMSE of modelled LAI: 0.85 to 1.28) as compared to RVI-LAI model (RMSE of modelled LAI: 1.12 to 1.17) at LAI greater than 3, than below 3 revealed the early saturation of NDVI than RVI. It is therefore recommended that LAI estimates can be used to force crop simulation model upto early vegetative stage based on NDVI and maximum vegetative to reproductive stages based on RVI.

Clear-sky dekadal relative evapotranspiration (RET) was derived using the surface energy-balance approach applied to 10-day composite NOAA PAL (8 km68 km) datasets over the Indian landmass. This was further used to differentiate between growth characteristics for an irrigated intensive agriculture over a northern India state (e.g. Punjab) and a rainfed ill-posed agriculture over a central India state (e.g. Madhya Pradesh) using time-series data sets for five growing years (. The triangular scatter between RET and normalized difference vegetation index (NDVI) showed that the minimum RET increases linearly with NDVI producing a 'basal line' that represents relative canopy transpiration only. A clear distinction in scatter was found between the two contrasting agroecosystems showing a higher RET or root zone wetness in irrigated than rainfed systems. In rainfed rice-growing regions, an inverse correlation (0.6-0.75) was found between RET and the Keetch-Byram meteorological drought index (KBDI), and a substantial reduction in RET was also found in a sub-normal (2000) compared with a normal (1999) monsoon season. RET estimates were found to be most sensitive to atmospheric transmissivity followed by other landsurface radiation budget inputs, such as NDVI, LST, and albedo. Error propagation due to three surface parameters is the opposite of that for transmissivity. The maximum possible error in clear-sky NOAA PAL RET was estimated to be 12-15%. This test study would be helpful in deriving RET using optical and thermal data from a suite of current and future Indian geostationary satellite sensors for monitoring growing conditions.

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