Study on the Extraction of Topsoil-Loss Areas of Cultivated Land Based on Multi-Source Remote Sensing Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 3; p. 547
• Main Authors: Zhang, Xinle, Qin, Chuan, Ma, Shinai, Liu, Jiming, Wang, Yiang, Liu, Huanjun, An, Zeyu, Ma, Yihan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2025
• Subjects: Accuracy; Agricultural production; Agriculture; Algorithms; Analysis; China; Classification; Crop development; Crop growth; Crops; Cultivated lands; Data processing; Desertification; Digital Elevation Models; Digital imaging; Economic development; Efficiency; Environmental monitoring; Farms; Feature extraction; Information processing; Machine learning; Mathematical optimization; Monitoring; multi-source data; Natural resources; Particle swarm optimization; Plant protection; Productivity; random forest; Remote sensing; Satellite imagery; Soil degradation; Soil erosion control; Soil fertility; Soil management; Soils; Strategic planning (Business); support vector machine; Support vector machines; Surveys; Sustainability management; Sustainable agriculture; Sustainable development; Topsoil; topsoil loss; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs17030547

Soil, a crucial natural resource and the cornerstone of agriculture, profoundly impacts crop growth, quality, and yield. However, soil degradation affects over one-third of global land, with topsoil loss emerging as a significant form of this degradation, posing a grave threat to agricultural sustainability and socio-economic development. Therefore, accurate monitoring of topsoil-loss distribution is essential for formulating effective soil protection and management strategies. Traditional survey methods are limited by time-consuming and labor-intensive processes, high costs, and complex data processing. These limitations make it particularly challenging to meet the demands of large-scale research and efficient information processing. Therefore, it is imperative to develop a more efficient and accurate extraction method. This study focuses on the Heshan Farm in Heilongjiang Province, China, as the research subject and utilizes remote sensing technology and machine learning methods. It introduces multi-source data, including Sentinel-2 satellite imagery and Digital Elevation Model (DEM) data, to design four extraction schemes. (1) spectral feature extraction; (2) spectral feature + topographic feature extraction; (3) spectral feature + index extraction; (4) spectral feature + topographic feature + index extraction. Models for topsoil loss identification based on Random Forest (RF) and Support Vector Machine (SVM) algorithms are developed, and the Particle Swarm Optimization (PSO) algorithm is introduced to optimize the models. The performance of the models is evaluated using overall accuracy and Kappa coefficient indicators. The results show that Scheme 4, which integrates spectral features, topographic features, and various indices, performs the best in extraction effects. The RF model demonstrates higher classification accuracy than the SVM model. The optimized PSO-RF and PSO-SVM models show significant improvements in extraction accuracy, especially the PSO-RF model, with an overall accuracy of 0.97 and a Kappa coefficient of 0.94. The PSO-RF model using Scheme 4 improves OA by 34.72% and Kappa by 38.81% compared to the RF model in Scheme 1. Topsoil loss has a significant negative impact on crop growth, severely restricting the normal growth and development of crops. This study provides an efficient technical means for monitoring soil degradation in black-soil regions and offers a scientific basis for formulating effective agricultural ecological protection strategies, thereby promoting the sustainable management of soil resources.