Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

Globally, estimating crop acreage and yield is one of the most critical issues that policy and
decision makers need for assessing annual crop productivity and food supply. Nowadays, satellite
remote sensing and geographic information system (GIS) can enable the estimation of these crop
production parameters over large geographic areas. The present work aims to estimate the wheat
(Triticum aestivum) acreage and yield of Maharajganj, Uttar Pradesh, India, using satellite-based data
products and the Carnegie-Ames-Stanford Approach (CASA) model. Uttar Pradesh is the largest
wheat-producing state in India, and this district is well known for its quality organic wheat. India
is the leader in wheat grain export, and, hence, its monitoring of growth and yield is one of the top
economic priorities of the country. For the calculation of wheat acreage, we performed supervised
classification using the Random Forest (RF) and Support Vector Machine classifiers and compared
their classification accuracy based on ground-truthing. We found that RF performed a significantly
accurate acreage assessment (kappa coefficient 0.84) compared to SVM (0.68). The CASA model was
then used to calculate the winter crop (Rabi, winter-sown, and summer harvested) wheat net primary
productivity (NPP) in the study area for the 2020–2021 growth season using the RF-based acreage
product. The model used for wheat NPP-yield conversion (CASA) showed 3100.27 to 5000.44 kg/ha
over 148,866 ha of the total wheat area. The results showed that in the 2020–2021 growing season,
all the districts of Uttar Pradesh had similar wheat growth trends. A total of 30 observational data
points were used to verify the CASA model-based estimates of wheat yield. Field-based verification
shows that the estimated yield correlates well with the observed yield (R2 = 0.554, RMSE = 3.36 Q/ha,
MAE 􀀀0.56 t ha􀀀1, and MRE = 􀀀4.61%). Such an accuracy for assessing regional wheat yield can
prove to be one of the promising methods for calculating the whole region’s agricultural yield. The
study concludes that RF classifier-based yield estimation has shown more accurate results and can
meet the requirements of a regional-scale wheat grain yield estimation and, thus, can prove highly
beneficial in policy and decision making.