The IHSCYM integrates the satellite data from various agencies like Indian Space Research Organization (ISRO), European Space Agency (ESA) and National Aeronautics and Space Administration (NASA). The model predicts sugarcane acreage estimation , sugarcane biomass estimation , wheat and paddy productivity at village level without using retrospective empirical analysis, which constitutes a new opportunity for timely yield estimates for large regions. The merit of this IHSCYM is quick turnaround time, which means wheat productivity can be predicted just before the harvesting of the crop. Therefore, it is possible to identify the clusters of villages with low productivity and take appropriate remedial steps to enhance the productivity.
Smart agriculture reduces the negative environmental impacts on farming, increases resilience and soil health and decreases costs for farmers. Remote sensing (via satellites), GIS, crop and soil health monitoring, and technologies for farm management are commonplace in developed countries. In India Hon’ble Prime Minister has always emphasized upon the application of cutting-edge technology for agriculture.The traditional methods to assess crop damage have become inaccurate and irrelevant in such an ever-changing scenario. Technology based models to assess crop damage are the need of the hour to provide relief to the farmers as envisaged in the vision of our Hon’ble Prime Minister in the “Pradhan Mantri Fasal Bima Yojana and for assessment of farmer credit policy from banks”.
Derivation of Sentinel2 GPPSentinel-2A/B satellites having red-edge bands (680–780 nm) at 20-m spatial resolution are very much suitable to improve GPP estimation as these Red-edge bands are very much sensitive to leaf chlorophyll content which is directly correlated with photosynthesis as the pigment pool.
LAI green was calculated as (Gitelson et al., 2003b) LAI green = (R865 /R705) equation 1 (1) LAI green was used to compute fAPAR green (e.g., Ruimy et al., 1999) using the equation 2 as follows fAPAR green = 0.95 x (1 − exp( − λ × LAI green))
(2) where λ is the extinction coefficient. The value of λ equal to 0.45 was used for wheat crop (Jamieson et al. (1995)). LUEmax was derived using the following relation (Taifeng et al., 2016) Winter wheat LUEmax, r = 3.28* EVI 0. 8 6 (2) where λ is the extinction coefficient. The value of λ equal to 0.45 was used for wheat crop (Jamieson et al. (1995)). LUEmax was derived using the following relation (Taifeng et al., 2016) Winter wheat LUEmax, r = 3.28* EVI 0.86
(3)GPP using Sentinel2 data was derived using the eq 2 and 3 as followsGPPS2A/2B = LUE max x fAPARgreen x PAR (4) Wheat yield estimation The wheat yield estimates were computed as follows Yieldest = Summation of GPPdoy x 2.0 x 0.9 x HI
(5) t=1where Yieldest is the estimated yield (kg/ha), n is the total number of crop duration (135 days) GPPDOY is gross primary productivity of the day, carbon is converted into biomass by a factor of two, 0.9 (90%) is allocated to above-ground of the annual proportion of GPP and HI is the harvest index of 38% .