Remote Sensing-based Wheat Area and Yield Estimation: Insights from Uttarakhand, India

India is one of the world's leading exporters of wheat grain, making monitoring its growth and yield one of the country's top economic priorities. This study aimed to develop a methodology for delineating wheat cultivation areas and estimating wheat yields using Landsat 8 (30 m spatial resolution) data for the Nainital and Udham Singh Nagar districts of Uttarakhand, India. The cultivated wheat fields were identified using a supervised classification-based Random Forest (RF) algorithm during the growing season from November 2020 to April 2021. To characterize the wheat class, a total of 239 and 226 wheat points, along with 201 and 166 non-wheat geometry points, based on NDVI time series were allotted for Nainital and Udham Singh Nagar districts, respectively. The calculated wheat area was found to be 778.94 sq. km and 209.48 sq. km, compared to the actual reported areas by the Agriculture Department, Government of Uttarakhand of 1059.61 sq. km and 212.78 sq. km for Udham Singh Nagar and Nainital, respectively. The RF algorithm showed an underestimation for both districts, achieving a kappa coefficient of 0.97, producer accuracy of 0.97, user accuracy of 0.96, and overall accuracy of 0.98 for the Nainital district. For the Udham Singh Nagar district, the kappa coefficient was 0.89, with producer accuracy of 0.89, user accuracy of 0.93, and overall accuracy of 0.93. The study also utilized weather data along with Landsat 8 imagery as inputs for the Carnegie-Ames-Stanford Approach (CASA) to estimate wheat yields and get spatial wheat yield maps. The estimated mean yields were 3.73 t ha⁻¹ and 3.37 t ha⁻¹, whereas the actual mean yields were 3.82 t ha⁻¹ and 4.45 t ha⁻¹ for Nainital and Udham Singh Nagar districts, respectively. The study demonstrates the potential of combining remote sensing and supervised classification techniques for reliable wheat yield estimation in data-scarce regions, which can be a promising tool for agricultural policy and decision-making.

Keywords: Crop classification, Landsat 8, random forest, wheat, spatial yield map