Cropland prediction using remote sensing, ancillary data, and machine learning

Abstract

Temporal and spatial environmental factors have a substantial influence on crop yields, and an accurate prediction can benefit timely decision-making in global food production. Thus for better agricultural management, the precise estimation of the croplands is helpful. Mapping the cropland dynamics with regular requirement of crops is an important prerequisite for monitoring crops, yield estimation, and crop inventories. Remote sensing and geographic information systems play a significant role in tracing and understanding environmental impacts of agriculture. The use of machine learning aids in developing a model that can give precise predictions based on the historical data. The main objective of our study is to use these machine learning algorithms to make accurate predictions about the best crop types using the spectral, temporal, and polarimetric features. A big dataset incorporating optical and polarimetric aperture radar experimental values is used to train the machine learning classifiers for predicting the right crop type in the study area. It has been observed that these features aid in accurate mapping of the cropland. Our study involves the performance comparison of the various machine learning algorithms, and it has been observed that a single-layer neural network offers prediction accuracy of ∼99.6 % for this big PoISAR dataset.