Alluvial plains are vexing landscapes for soil mapping and spatial soil property predictions. Alluvial sediments often exhibit unpredictable spatial variability from both fluvial and anthropogenic disturbance. The determination of optimal number of soil sampling points for capturing soil variability remains a persistent issue for mapping and monitoring soil conditions. Here, soil organic matter (SOM) and cation exchange capacity (CEC) were estimated in an alluvial plain from a dense array of 2145 soil samples over 250 ha. The primary goals were to i) use pixel-based statistics from time-series Sentinel-2 satellite reflectance data to estimate SOM and CEC; ii) evaluate the use of images with vegetation cover versus bare soil images; and, iii) investigate the optimal number of sampling points to map alluvial soils using different sampling strategies. The optimal sample density was 1 sample per 2.5 ha based on the overlapping of prediction distributions ( > 0.9). Conditioned Latin Hypercube Sampling (cLHS) was the most efficient sampling strategy. Random grid sampling provided the least consistent results. The use of cLHS coupled with the mean and standard deviation bands was the optimal sampling strategy. Pixel-based statistics from readily available satellite imagery captured persistent soil-reflectance relationships that enabled the use of row crops as proxies to predict soil properties. The combined use of pixel-based statistics as inputs to cLHS can reduce the chances of oversampling and associated costs. Pixel-based statistics provided persistent spatial information that can enhance precision agriculture management, carbon mapping, and is likely applicable to other soil properties and remotely sensed products. Use of pixel-based statistics from Sentinel-2 and similar global-coverage imagery could provide readily-calculable inputs for improved soil mapping and more optimal site sample selections in other agricultural row crop areas, globally.

中文翻译：

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来自遥感图像的基于像素的时空统计改进了冲积土的空间预测和采样策略


冲积平原是土壤测绘和空间土壤性质预测中令人烦恼的景观。冲积沉积物常常因河流和人为干扰而表现出不可预测的空间变化。确定用于捕获土壤变异性的最佳土壤采样点数量仍然是绘制和监测土壤状况的一个长期问题。在此，我们根据 250 公顷的密集 2145 个土壤样本估算了冲积平原的土壤有机质 (SOM) 和阳离子交换能力 (CEC)。主要目标是 i) 使用来自时间序列 Sentinel-2 卫星反射率数据的基于像素的统计数据来估计 SOM 和 CEC； ii) 评估植被覆盖图像与裸土图像的使用情况； iii) 研究使用不同采样策略绘制冲积土地图的最佳采样点数量。根据预测分布的重叠 (> 0.9)，最佳样本密度为每 2.5 公顷 1 个样本。条件拉丁超立方采样 (cLHS) 是最有效的采样策略。随机网格采样提供了最不一致的结果。使用 cLHS 与平均值和标准偏差带相结合是最佳采样策略。来自现成卫星图像的基于像素的统计数据捕获了持久的土壤反射率关系，从而可以使用中耕作物作为预测土壤特性的代理。结合使用基于像素的统计数据作为 cLHS 的输入可以减少过采样的机会和相关成本。基于像素的统计数据提供了持久的空间信息，可以增强精准农业管理、碳测绘，并且可能适用于其他土壤特性和遥感产品。 使用来自 Sentinel-2 的基于像素的统计数据和类似的全球覆盖图像可以提供易于计算的输入，以改进土壤测绘并在全球其他农业中耕作物区域进行更优化的站点样本选择。