A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images

Chen, Hui and Li, Huapeng and Liu, Zhao and Zhang, Ce and Zhang, Shuqing and Atkinson, Peter (2023) A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images. Remote Sensing of Environment, 295: 113679. pp. 1-16. ISSN 0034-4257

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Abstract

As a critical source of food and one of the most economically significant crops in the world, soybean plays an important role in achieving food security. Large area accurate mapping of soybean has long been a vital, but challenging issue in remote sensing, relying heavily on large-volume and representative training samples, whose collection is time-consuming and inefficient, especially for large areas (e.g., national scale). Thus, methods are needed that can map soybean automatically and accurately from single-date remotely sensed imagery. In this research, a novel Greenness and Water Content Composite Index (GWCCI) was proposed to map soybean from just a single Sentinel-2 multispectral image in an end-to-end manner without employing training samples. By capitalizing on the product of the NDVI (related to greenness) and the short-wave infrared (SWIR) band (related to canopy water content), the GWCCI provides the required information with which to discriminate between soybean and other land cover types. The effectiveness of the proposed GWCCI was investigated in seven typical soybean planting regions within four major soybean-producing countries across the world (i.e., China, the United States, Brazil and Argentina), with diverse climates, cropping systems and agricultural landscapes. In the experiments, an optimal threshold of 0.17 was estimated and adopted by the GWCCI in the first study site (S1) in 2021, and then generalised to the other study sites over multiple years for soybean mapping. The GWCCI method achieved a consistently higher accuracy in 2021 compared to two conventional comparative classifiers (support vector machine (SVM) and random forest (RF)), with an average overall accuracy (OA) of 88.30% and a Kappa coefficient (k) of 0.77; significantly greater than those of RF (OA: 80.92%, k: 0.62) and SVM (OA: 80.29%, k: 0.60). Furthermore, the OA of the extended years was highly consistent with that of 2021 for study sites S2 to S7, demonstrating the great generalisation capability and robustness of the proposed approach over multiple years. The proposed GWCCI method is straightforward, reliable and robust, and represents an important step forward for mapping soybean, one of the most significant crops grown globally.