Data from: Canopy spectral reflectance as a predictor of soil water potential in rice

Soil water potential (SWP) is a key parameter for characterizing water stress. Typically, a tensiometer is used to measure SWP. However, the measurement range for commercially available tensiometers is limited to -90 kPa and a tensiometer can only provide estimate of SWP at a single location. In this study, a new approach was developed for estimating SWP from spectral reflectance data of a standing rice crop over the visible to shortwave-infrared region (wavelength: 350 nm to 2500 nm). Five water stress treatments corresponding to targeted SWP of – 30 kPa, - 50 kPa, - 70 kPa, -120 kPa and - 140 kPa were examined by withholding irrigation during the vegetative growth stage of three rice varieties. Tensiometers and mechanistic water flow model were used for monitoring SWP. Spectral models for SWP was developed using partial-least-squares regression (PLSR), support vector regression (SVR), and coupled PLSR and feature selection (PLSRFS) approaches. Results showed that the SVR approach was the best model for estimating SWP from spectral reflectance data with the coefficient of determination values of 0.71 and 0.55 for the calibration and validation datasets, respectively. Observed root-mean-squared residuals for the predicted SWPs were in the range of -7 to -19 kPa. A new spectral water stress index was also developed using the reflectance values at 745 nm and 2002 nm, which showed strong correlation with relative water contents and electrolyte leakage. This new approach is rapid and non-invasive and may be used for estimating SWP over large areas.

土壤水势（Soil water potential, SWP）是表征水分胁迫的关键参数。当前主流测量手段为张力计（tensiometer），但商用张力计的测量范围局限于-90 kPa，且仅能获取单点的土壤水势估算值。
本研究开发了一种全新方法，可基于直立生长水稻在可见光至短波红外波段（波长范围350 nm至2500 nm）的光谱反射率数据估算土壤水势。
本研究针对3个水稻品种的营养生长期，通过暂停灌溉设置了5组水分胁迫处理，对应目标土壤水势分别为-30 kPa、-50 kPa、-70 kPa、-120 kPa与-140 kPa。实验采用张力计与机理性水流模型对土壤水势进行监测，并分别采用偏最小二乘回归（partial-least-squares regression, PLSR）、支持向量回归（support vector regression, SVR）以及偏最小二乘回归与特征选择耦合（coupled PLSR and feature selection, PLSRFS）三种方法构建土壤水势光谱估算模型。
研究结果表明，支持向量回归方法为最优的光谱反射率数据土壤水势估算模型，其校正集与验证集的决定系数分别为0.71与0.55；预测得到的土壤水势对应的观测均方根残差范围为-7 kPa至-19 kPa。
此外，本研究基于745 nm与2002 nm处的反射率值构建了一种新型光谱水分胁迫指数，该指数与相对含水量及电解质渗漏率均呈现较强相关性。本方法具备快速、非侵入式的技术优势，可应用于大区域范围的土壤水势估算。