Evaluating established deep learning methods in constructing integrated remote sensing drought index: A case study in China

Abstract

Agricultural drought seriously threatens the food and ecological security of most of the world’s developing countries. Data-driven integrated agricultural drought index with remote sensing provides an effective tool to monitor, evaluate, and predict the agricultural drought. However, there is still a lack of comprehensive analytical work on taking the most effective machine learning (ML) and deep learning (DL) methods to construct such integrated drought index. In other words, it is still unclear whether the recent DL methods can improve integrated drought monitoring as compared with the currently widely used ML methods. Therefore, we critically evaluated the performances of four representative DL methods (represents the four currently popular DL network types) i.e., Entity Embedding Deep Neural Network (EEDNN), One-dimensional Convolutional Neural Network (1D-CNN), Gated Recurrent Unit (GRU), and Self-Attention Mechanism (SAM) and three widely used tree-based ML methods i.e., Cubist, Random Forest (RF), and Light Gradient Boosting Machine (LGBM), through constructing a QuickDRI like integrated drought index (abbreviated as QuickDRI-China). About 30 years of meteorological data, 14 years of remote sensing data, and various biophysical variables in China such as land use/land cover, available water capacity, irrigated agriculture, elevation, and ecoregion were employed in this study. Results showed that the EEDNN performed best, followed by the RF and LGBM, and then the other methods including the currently wide used Cubist, according to the station accuracy evaluations, spatial description evaluations, and responses to specific drought event. The tree-based ML methods such as RF and LGBM are still competitive in constructing the integrated agricultural drought index at the current stage. However, the higher accuracy, the smoother spatial description, and the more responsive ability of the EEDNN demonstrate great potential of DL methods. The future integrated agricultural drought monitoring with remote sensing should develop a specialized DL network for heterogeneous agricultural drought features.

Keywords

• Agricultural drought
• Remote sensing
• Data driven
• Deep learning
• Machine learning
• Inductive bias