A Global Meta-Analysis of Soil Salinity Prediction Integrating Satellite Remote Sensing, Soil Sampling, and Machine Learning

作者：Haiyang Shi, Olaf Hellwich, Geping Luo, Chunbo Chen, Huili He, Friday Uchenna Ochege, Tim Van de Voorde, Alishir Kurban, Philippe De Maeyer · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2021 · DOI：10.1109/tgrs.2021.3109819 · 被引用次数：51 · 研究领域：Soil Geostatistics and Mapping、Soil Moisture and Remote Sensing、Soil and Unsaturated Flow

Despite the growing interest among researchers, satellite-based prediction of soil salinity remains highly uncertain. The improvements in prediction accuracy reported in previous studies are usually limited to a single area. We performed a meta-analysis of regional satellite-based soil salinity predictions combined within situsoil sampling and machine learning. Based on$R^{2}$and root-mean-square error (RMSE) collected, we evaluated the effects of various features on the model accuracy and established a Bayesian network to evaluate the joint causal effect of multifeatures. Most significant differences were found in soil sampling schemes and characteristics of the study area, including the mean and variability (averaged$R^{2}$of 0.75 for soil sample sets with lower salinity variation and 0.62 for others) of the salinity, climate type ($R^{2}$of 0.64 in arid areas and 0.74 in others), soil texture ($R^{2}$of 0.66 in sandy areas and 0.57 in others), and the interval between sampling date and satellite data acquisition date ($R^{2}$of 0.53 under the condition of over 15 days and 0.65 in others). Generally, using different satellite data has limited effects on model performance among which Sentinel-2 performed better ($R^{2} $= 0.72) than Landsat ($R^{2} $= 0.66). The sampling of subsamples for each sample should focus on their subpixel-scale spatial heterogeneity across satellite data rather than the number of subsamples. It is also necessary to select appropriate v...