Fine-scale characterization of irrigated and rainfed croplands at national scale using multi-source data, random forest, and deep learning algorithms

Abstract

Knowledge of the extent and distribution of irrigated and rainfed croplands is critical in providing the necessary baseline data for enhancing agricultural efficiency and making informed policy decisions. Accurately identifying and mapping irrigated and rainfed croplands can hasten the attainment of Sustainable Development Goals (SDGs) 1 and 2, aimed at reducing poverty and hunger, respectively. However, traditional methods employed to identify and map cropland areas are expensive and require substantial labour, particularly in extensive environments. As a result, this study presents a comprehensive and spatially explicit methodological framework for identifying and mapping national-scale irrigated and rainfed croplands in South Africa. This framework leverages low-cost earth observation technologies (Sentinel-2 MSI) and employs highly accurate classification algorithms, namely Deep Learning Neural Network (DNN) and Random Forest (RF). The proposed methodology strategically integrates data from multiple sources, including public repositories (e.g., cropland data, evapotranspiration), ongoing research (e.g., land cover maps), and field data, to enhance the accuracy and reliability of the results. The methodology begins by employing a robust random forest model to classify the study area into distinct land cover types. Leveraging the power of a deep learning neural network (DNN), the method accurately distinguishes between irrigated and rainfed croplands in South Africa. The random forest model achieved a notable classification accuracy of 0.77 when identifying the main land-use and land cover types. Meanwhile, the deep learning neural network (DNN) model achieved an accuracy of 0.71 in differentiating rainfed and irrigated croplands at a national scale. These results highlight the effectiveness of the proposed methodology in providing baseline information relevant to crop monitoring, yield forecasting, and understanding agricultural food supply systems. Furthermore, the proposed methodology has the potential to offer timely and accurate information on cropland areas and their extent which could assist in implementing targeted interventions for optimising agricultural productivity. With its potential to be upscaled to other sub-Saharan countries, this methodology enriches agricultural decision-making and plays a vital role in bolstering food security and advancing the attainment of SDGs.