Soil acidification is a growing problem in semi-arid agroecosystems. In the state of Montana, USA, soil pH levels below 5.5 have been documented in nearly half of the counties. Acidic soils have the potential to reduce crop yield, but methods to identify and remediate acidic soils are costly and time-intensive. This study tests a relatively new approach for identifying areas of acidic soils using imagery derived from UAS (unmanned aerial systems). UAS provide a means to collect fine-scale, multi-spectral imagery at user-defined intervals for an area of interest—in this case, a 22 ha spring wheat field in southwestern Montana. In addition to 12 dates of spectral observations across a growing season, field measurements of soil pH and other soil attributes were collected to analyze their relationship with the normalized difference vegetation index (NDVI) using linear regression models, and to spatially predict soil pH across the field using a random forest model. The linear regression models indicated that most of the variation in early-season NDVI was attributed to differences in soil pH and soil organic matter, whereas variation in later-season NDVI was less related to soil pH. The random forest model predicted soil pH with reasonable accuracy (RMSE = 0.72). This study helps to fill a knowledge gap by bridging UAS-derived observations of NDVI with field-derived measurements of soil pH to identify areas of soil acidity. The methodology put forth by this study would enable land managers to easily identify and hence, remediate acidic soils in a more cost-efficient and timely manner.

土壤酸化是半干旱农业生态系统中日益严重的问题。在美国蒙大拿州，近一半县的土壤 pH 值低于 5.5。酸性土壤有可能降低作物产量，但识别和修复酸性土壤的方法成本高昂且耗时。本研究测试了一种使用来自 UAS（无人机系统）的图像识别酸性土壤区域的相对较新的方法。UAS 提供了一种以用户定义的间隔为感兴趣区域收集精细尺度、多光谱图像的方法——在这种情况下，是蒙大拿州西南部 22 公顷的春小麦田。除了整个生长季节的 12 个光谱观测日期外，收集土壤 pH 值和其他土壤属性的现场测量值，以使用线性回归模型分析它们与归一化差异植被指数 (NDVI) 的关系，并使用随机森林模型在空间上预测整个田间的土壤 pH 值。线性回归模型表明，早季NDVI的大部分变化归因于土壤pH值和土壤有机质的差异，而晚季NDVI的变化与土壤pH值的相关性较小。随机森林模型以合理的精度 (RMSE = 0.72) 预测土壤 pH 值。这项研究通过将 UAS 衍生的 NDVI 观测与现场衍生的土壤 pH 测量值联系起来，以确定土壤酸度区域，从而有助于填补知识空白。本研究提出的方法将使土地管理者能够轻松识别，因此，