Establishing the relationships between the CSM LAI and yield with backscatter value Satellite images: A case study over the Northern Karnataka

Abstract

Abstract India's extensive agrarian and aerospace research systems offer numerous potential technologies suited for the crop insurance sector. This study investigates the application of technology in domains such as yield prediction, loss assessment, and product design. The research was conducted in the Vijayapura, Karnataka by integrating an intrinsic bio-physical model with remote sensing. As the agricultural statistics play major role in shaping the strategies and plans under food security, an area of 51,482.3 ha was estimated using SAR data, achieving an accuracy of 87.2% and a kappa index of 0.74. Indigenously developed InfoCrop and SORGHUM- CERES were employed to estimate yield for selected monitoring locations, with simulated LAI and yield ranging from 2.6 to 4.65 and 749 to 1097 kg ha − 1 , respectively. When compared with observed data, results showed an agreement above 90% and RMSE less than 10% and R 2 above 70%. Using regression (R 2 > 0.75) equations [CSM’s LAI and backscattering (Db)] and [spatial LAI and CSM yield], LAI and yield maps were developed.

Plant phenotyping relevance

SARリモートセンシングと回帰モデルにより作物のLAI・収量を推定し、観測値との精度評価およびマッピングを行っており、植物形質の取得・推定法が実質的な中心である。

Code and data availability

The paper describes ground-truth LAI/yield collections, Sentinel-1A imagery, and CSM (DSSAT/InfoCrop) simulations, but provides no public deposit of its phenotype datasets, ground-truth points, trained models, or analysis code. All URLs cited (Copernicus SciHub, NASA POWER, FAOSTAT, KVK report, etc.) are generic public

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