Abstract
With advancement in satellites and remote sensing technology, reflectance data are increasingly being used in agriculture. In this paper, the machine learning models have been explored with three distinct types of properties to classify the hydro-metrological rainfall parameter, Standardized Precipitation Index (SPI), and Vegetation Condition Index (VCI) to monitor the agriculture state of Rajasthan. These three distinct indexes are used to classify the geospatial Rainfall-SPI, Rainfall-VCI, and Rainfall-SPI-VCI models. The K-fold cross validation has been used to evaluate the robustness of the outperforming classification method. The outcome shows that DecisionTree and RandomForest classification model performs outstanding machine learning classification methods for vegetation with sensitivity of 0.798 and accuracy of 95.792% on test dataset with DecisionTree and of 0.796 and accuracy of 95.584% on testing dataset with RandomForest model.
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