利用多时相图像中的新型裸土像素最小化植被对土壤盐分绘图的影响,Geoderma

光学遥感卫星可以快速获取区域表土盐碱化绘图。然而，基于光谱反射率的表土盐渍化绘图总是受到植被覆盖、秸秆覆盖和土壤类型等背景材料的影响。鉴于这些挑战，本研究探讨了图像融合的潜力，将原始土壤像素图像与裸露土壤像素图像相结合，以尽量减少植被覆盖对表土盐度测绘的影响。使用同步的 Sentinel-2 MSI 图像（称为原始图像）和 2020 年 10 月收集的 255 个地面实况数据，对典型植被覆盖区域进行了案例研究，与植被覆盖和盐回归的周期相一致。此外，为了获得新的裸露土壤像素，在两个不同的时间间隔内获取了多时相 Sentinel-2 MSI 图像：3 月至 5 月和 9 月至 11 月，跨越 2018 年至 2021 年。合成土壤图像 (SYSI) 是通过以下方式获得的：从多时相图像中提取裸土像素。将两幅图像（原始，SYSI）采用非负矩阵分解（NMF）方法进行融合，命名为 SYSI fused。然后，利用叠加机算法对不同土壤类型下的土壤盐分进行制图，评价SYSI融合对土壤盐分预测精度的影响。结果显示，SYSI融合的效果优于原始图像（最佳模型的R 2增加了 0.054–0.242，RMSE 和 MAE 分别下降了 0.049–0.780 和 0.012–0.546）。基于SYSI fused，土壤类型的影响顺序为滨海沼泽盐土>冲积土>褐土>珊瑚盐渍土>总体样本，其对模型R 2 的改善作用分别为0.141、0.085、0.022，分别为 0.012。此外，融合SYSI 的叠加模型提供了最佳的预测性能（R 2  = 0.742，RMSE = 0.377，MAE = 0.362）。本研究引入了将原始图像与 SYSI 合并的概念，从而显着提高了植被覆盖区域土壤盐分绘图的准确性。

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Optical remote sensing satellites provide rapid access to regional topsoil salinization mapping. However, mapping topsoil salinization based on spectral reflectance is always affected by background material like vegetation cover, straw mulching and soil types. In light of these challenges, this study investigates the potential of image fusion, where images of original and bare soil pixels were combined, to minimize the impact of vegetation cover on topsoil salinity mapping. A case study was presented for the typical vegetation cover area using synchronized Sentinel-2 MSI image (named original image) and 255 ground-truth data collected in October 2020, aligning with periods of vegetation cover and salt return. Furthermore, to obtain novel bare soil pixels, multi-temporal Sentinel-2 MSI images were acquired during two distinct intervals: March to May and September to November, spanning the years from 2018 to 2021. The synthetic soil image (SYSI) was obtained by extracting bare soil pixels from multi-temporal images. Two images (original, SYSI) were fused with non-negative matrix factorization (NMF) method, named SYSIfused. Then, the stacking machine algorithm was used for soil salinity mapping under different soil types, with evaluating the impact of SYSIfused on the accuracy of soil salinity prediction. The results showed the SYSIfused outperformed the original image (the R2 of the best models increased by 0.054–0.242, RMSE and MAE decreased by 0.049–0.780 and 0.012–0.546, respectively). Based on the SYSIfused, the order of the effect of soil types was coastal bog solonchaks > alluvial soil > cinnamon soil > coral saline soil > overall samples, and their roles in improving the R2 of the model were 0.141, 0.085, 0.022, 0.012, respectively. Besides, stacking models with the SYSIfused provided the best prediction performances (R2 = 0.742, RMSE = 0.377, MAE = 0.362). This study introduces the concept of merging original images with SYSI, resulting in a significant improvement in the accuracy of soil salinity mapping in areas covered by vegetation.