Remote Sensing Crop Water Stress Determination Using CNN-ViT Architecture

Abstract

Efficiently determining crop water stress is vital for optimising irrigation practices and enhancing agricultural productivity. In this realm, the synergy of deep learning with remote sensing technologies offers a significant opportunity. This study introduces an innovative end-to-end deep learning pipeline for within-field crop water determination. This involves the following: (1) creating an annotated dataset for crop water stress using Landsat 8 imagery, (2) deploying a standalone vision transformer model ViT, and (3) the implementation of a proposed CNN-ViT model. This approach allows for a comparative analysis between the two architectures, ViT and CNN-ViT, in accurately determining crop water stress. The results of our study demonstrate the effectiveness of the CNN-ViT framework compared to the standalone vision transformer model. The CNN-ViT approach exhibits superior performance, highlighting its enhanced accuracy and generalisation capabilities. The findings underscore the significance of an integrated deep learning pipeline combined with remote sensing data in the determination of crop water stress, providing a reliable and scalable tool for real-time monitoring and resource management contributing to sustainable agricultural practices.

Code and data availability

The paper's ground-truth crop water stress annotations derive from the public SMAPVEX16 Manitoba PALS brightness temperature and soil moisture/VWC dataset (NSIDC), cited in the Data Availability Statement and references. No author analysis code, trained models, or annotated dataset release is stated.