Satellite-Based Identification of Rice Cropland, Crop Parameters, and Yield Prediction Using an Integrated GIS Approach in Karnal District, Haryana

Abstract

Accurate estimation of crop area and yield is essential for agricultural planning, food security assessment, and resource management. The present study focuses on the satellite-based identification of rice cropland, crop parameter analysis, and yield prediction using an integrated GIS approach in Karnal district, Haryana. Multi-temporal satellite data from Sentinel-2 and Landsat-8 were used to identify rice cropland and derive important crop parameters using vegetation indices such as NDVI, SAVI, RGVI, LAI, MSI, and NDWI. These indices were generated using raster algebra operations in a GIS environment to assess vegetation vigor, crop health, and moisture conditions during the rice growing season.

The spatial analysis revealed significant variation in rice cultivation across different blocks of Karnal district. Assandh block recorded the highest rice cultivation area (87.5%), followed by Nissing (83.2%) and Nilokheri (81.7%), whereas Karnal block showed the lowest rice area (54.8%) with comparatively higher non-crop land (26.7%). Field observations were conducted through Crop Cutting Experiment (CCE) to obtain ground truth data for yield estimation and model validation. The CCE carried out in Sagga village (Plot No. 117//34) for the rice variety P-126 recorded a dry grain yield of 52.45 kg from a 100 m² plot, which corresponds to an estimated yield of 5,245 kg/ha (5.245 t/ha).

Based on the satellite-derived rice area of 187,186.30 hectares in Karnal district, the total rice production was estimated to be approximately 981,790 tonnes (9.82 lakh tonnes) during the Kharif season. The integration of satellite imagery, GIS-based spatial analysis, and field-based CCE observations demonstrated high potential for accurate identification of rice cropland and reliable crop yield prediction. The study highlights the effectiveness of geospatial technologies in supporting agricultural monitoring, crop management, and decision-making at the district level.