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.

Review of Applications of Remote Sensing towards Sustainable Agriculture in the Northern Savannah Regions of Ghana

“…For instance, the Global Agriculture Geo-monitoring Initiative (GEOGLAM) is an RS-based system developed by USAID and the World Food Programme for monitoring food production in food-insecure regions across the globe [49]. A combination of RADAR data and social and economic food security indicators is a critical asset towards achieving this goal [50,51].…”

Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia

“…Both the backscattering coefficients of different polarization modes, as well as derivatives and radar vegetation indices derived thereof have been used in the context of crop classification [5,[12][13][14]. In recent years, several studies worldwide have made use of approaches that combine optical and SAR data (e.g., [5,[15][16][17][18][19][20][21]).…”

“…Methods recently applied to crop type classification include mostly machine learning algorithms, such as support vector machines, random forest, and artificial neural networks (e.g., [15,20,22]). Random forest and other decision tree models especially are widely used (e.g., [23][24][25]).…”