Estimation of soil organic matter content by combining Zhuhai-1 hyperspectral and Sentinel-2A multispectral images

Abstract

Hyperspectral satellite imagery has significant advantages in rapidly monitoring soil organic matter (SOM) content over a large area. However, limitations in the timely acquisition of site-specific data may affect its effectiveness due to weather influences and revisit cycles. This paper proposes a novel method for estimating SOM content that combines the high temporal resolution of the Zhuhai-1 hyperspectral satellite image and Sentinel-2A multispectral satellite image to broaden the spectral range of Zhuhai-1 images. Multisource features, including spectral bands, topographic features, textural features, and spectral indexes, are extracted from the combined image and digital elevation model. An improved genetic algorithm (IGA) is proposed to optimize feature selection and extreme gradient boosting (XGBoost) is then applied to estimate SOM content. The proposed method was validated using 197 topsoil samples and satellite images collected from a demonstration area in Lishu County, Jilin Province, China. The results indicate that the estimation accuracy using the combined image was greater than using one single image. Compared with only using the Sentinel-2A and Zhuhai-1 images, the coefficient of determination (R²) values improved from 0.61 and 0.73 to 0.82, the ratio of the prediction to the deviation (RPD) values improved from 1.62 and 1.93 to 2.35, and the ratio of performance to the interquartile distance (RPIQ) values improved from 2.16 and 2.58 to 3.15, respectively. Furthermore, the XGBoost algorithm outperformed the random forest algorithm in terms of model accuracy and mapping reliability. The use of multisource features improved the R² value from 0.45 to 0.82 compared to only using 31 spectral bands of Zhuhai-1 and Sentinel-2A image, with contribution rates in descending order of spectral indexes (48.2%), topographic features (24.7%), spectral bands (19.9%), and textural features (7.2%). This paper thus presents a promising method for efficient periodic mapping of the SOM content by combining data from a hyperspectral satellite constellation and a multispectral satellite image.