Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Pádua Dias Av., 11, Postal Box 09, Piracicaba, São Paulo, 13416-900, Brazil.
Coordination of Integrate Technical Assistance of Secretariat of Agriculture and Supply-CATI/SAA, Piracicaba Regional, Campos Salles Street, 507, Piracicaba, São Paulo State, 13400-200, Brazil.
Sci Rep. 2023 Jul 5;13(1):10897. doi: 10.1038/s41598-023-36219-9.
The pressure for food production has expanded agriculture frontiers worldwide, posing a threat to water resources. For instance, placing crop systems over hydromorphic soils (HS), have a direct impact on groundwater and influence the recharge of riverine ecosystems. Environmental regulations improved over the past decades, but it is difficult to detect and protect these soils. To overcome this issue, we applied a temporal remote sensing strategy to generate a synthetic soil image (SYSI) associated with random forest (RF) to map HS in an 735,953.8 km area in Brazil. HS presented different spectral patterns from other soils, allowing the detection by satellite sensors. Slope and SYSI contributed the most for the prediction model using RF with cross validation (accuracy of 0.92). The assessments showed that 14.5% of the study area represented HS, mostly located inside agricultural areas. Soybean and pasture areas had up to 14.9% while sugar cane had just 3%. Here we present an advanced remote sensing technique that may improve the identification of HS under agriculture and assist public policies for their conservation.
粮食生产的压力促使世界各地农业扩张，威胁到水资源。例如，将作物系统置于水成型土壤（HS）上，会直接影响地下水并影响河流生态系统的补给。过去几十年来，环境法规有所改善，但这些土壤很难被发现和保护。为了解决这个问题，我们应用了一种时间遥感策略来生成与随机森林（RF）相关的合成土壤图像（SYSI），以在巴西 735,953.8 平方公里的区域内绘制 HS。HS 呈现出与其他土壤不同的光谱模式，允许卫星传感器进行检测。坡度和 SYSI 对使用 RF 进行交叉验证的预测模型贡献最大（准确率为 0.92）。评估显示，研究区域的 14.5%表示 HS，主要位于农业区内部。大豆和牧场区域高达 14.9%，而甘蔗仅为 3%。在这里，我们提出了一种先进的遥感技术，可以提高农业下 HS 的识别能力，并为保护 HS 提供公共政策支持。
