fdata-02-00037-g0006_A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture.tif

The recently developed OPtical TRApezoid Model (OPTRAM) has been successfully applied for watershed scale soil moisture (SM) estimation based on remotely sensed shortwave infrared (SWIR) transformed reflectance (TRSWIR) and the normalized difference vegetation index (NDVI). This study is aimed at the evaluation of OPTRAM for field scale precision agriculture applications using ultrahigh spatial resolution optical observations obtained with one of the world's largest field robotic phenotyping scanners located in Maricopa, Arizona. We replaced NDVI with the soil adjusted vegetation index (SAVI), which has been shown to be more accurate for cropped agricultural fields that transition from bare soil to dense vegetation cover. The OPTRAM was parameterized based on the trapezoidal geometry of the pixel distribution within the TRSWIR-SAVI space, from which wet- and dry-edge parameters were determined. The accuracy of the resultant SM estimates is evaluated based on a comparison with ground reference measurements obtained with Time Domain Reflectometry (TDR) sensors deployed to monitor surface, near-surface and root zone SM. The obtained results indicate an SM estimation error between 0.045 and 0.057 cm3 cm−3 for the near-surface and root zone, respectively. The high resolution SM maps clearly capture the spatial SM variability at the sensor locations. These findings and the presented framework can be applied in conjunction with Unmanned Aerial System (UAS) observations to assist with farm scale precision irrigation management to improve water use efficiency of cropping systems and conserve water in water-limited regions of the world.

近期研发的光学梯形模型（OPtical TRApezoid Model, OPTRAM）已依托遥感短波红外（shortwave infrared, SWIR）变换反射率（transformed reflectance, TRSWIR）与归一化差分植被指数（normalized difference vegetation index, NDVI），成功应用于流域尺度土壤水分（soil moisture, SM）估算。本研究旨在评估OPTRAM在田间尺度精准农业场景中的应用性能，所用数据为部署于美国亚利桑那州马里科帕市的全球规模最大的田间机器人表型扫描仪之一获取的超高空间分辨率光学观测数据。本研究将NDVI替换为土壤调整植被指数（soil adjusted vegetation index, SAVI），该指数已被证实对从裸土过渡到致密植被覆盖的农田具有更高的估算精度。OPTRAM基于TRSWIR-SAVI特征空间内的像素分布梯形几何进行参数化，由此确定湿边与干边参数。最终得到的土壤水分估算结果的精度，通过与时域反射仪（Time Domain Reflectometry, TDR）传感器获取的地面参考测量值对比进行评估，该类传感器被部署用于监测表层、近表层与根区土壤水分。所得结果显示，近表层与根区的土壤水分估算误差介于0.045至0.057 cm³ cm⁻³之间。高分辨率土壤水分分布图清晰捕捉到了传感器布设点位处的空间土壤水分变异性。本研究的发现与所提出的分析框架，可结合无人机系统（Unmanned Aerial System, UAS）观测数据，用于辅助农田尺度精准灌溉管理，以提升种植系统的水资源利用效率，并为全球水资源受限地区的节水工作提供支撑。