土壤含水量（θ）是影响作物生长和作物产量的主要因素之一。 旨在评估基于光谱特征参数的各种回归模型估算土壤含水量的精度, 并比较人工神经网络（BP-ANN）和光谱特征参数模型的性能。 2014年在室内获取砂土和壤土的土壤含水量和光谱反射率数据。 结果表明: （1）当砂土容重为1.40 g·cm-3时, 900～970 nm最大反射率和900～970 nm反射率总和估算θ达到极显著水平（R2超过0.90）; 容重为1.50 g·cm-3时, 用蓝边最大反射率和900～970 nm反射率总和估算θ相关性最好（超过0.70）; 容重为1.60 g·cm-3时, 780～970 nm反射率总和与560～760 nm归一化吸收深度的R2均超过0.90, 达到极显著水平; 容重为1.70 g·cm-3时, 900～970 nm最大反射率和900～970 nm反射率总和的R2为0.88, 呈极显著水平。 （2）当土壤类型为壤土时, 用900～970 nm最大反射率和900～970 nm反射率总和估算θ相关性最好。 （3）蓝边反射率总和（R2=0.26和RMSE=0.09 m3·m-3）和780～970 nm吸收深度（R2=0.32和RMSE=0.10 m3·m-3）估算砂土的含水量相关性最好。 在估算壤土的含水量时, 900～970 nm最大反射率（R2=0.92和RMSE=0.05 m3·m-3）与900～970 nm反射率总和估算模型的精度最高（R2=0. 92和RMSE=0.04 m3·m-3）。 （4）用人工神经网络模型能够更好地估算两种土壤的含水量（R2=0.87和RMSE=0.05 m3·m-3）。 因此, 人工神经网络模型对θ估算具有巨大的潜力。

Soil water content (θ) is an important factor for the crop growth and crop production. The objectives of this study were to (i) test various regression models for estimating θ based on spectral feature parameters, and (ii) compare the performance of the proposed models by using artificial neural networks (ANN) and spectral feature parameters. The θ data of sand and loam and concurrent spectral parameters were acquired at the laboratory experiment in 2014. The results showed that: (1) the maximum reflectance with 900～970 nm and the sum reflectance within 900～970 nm estimate θ had the significant, when sand bulk density was 1.40 g·cm－3; the maximum reflectance with blue edge and the sum reflectance within 900～970 nm had the best correlation (R2>0.70) when sand bulk density was 1.50 g·cm－3; while soil bulk density was 1.60 g·cm－3, the sum reflectance within 780～970 nm and normalized absorption depth in 560～760 nm reached a significant (R2>0.90); when soil bulk density was 1.70 g·cm－3, the maximum reflectance with 900～970 nm and the sum reflectance within 900～970 nm had the best correlation estimate θ (R2>0.88). 2) When the soil type was loam, the maximum reflectance with 900～970 nm and the sum reflectance within 900～970 nm had a best correlation estimate θ. The spectral feature parameters the sum reflectance within blue edge (R2=0.26 and RMSE=0.09 m3·m－3) and 780～970 nm absorption depth (R2=0.32 and RMSE=0.10 m3·m－3) were best correlated with θ in the sand. The θ model based on maximum reflectance with 900～970 nm (R2=0.92 and RMSE=0.05 m3·m－3) and the sum reflectance within 900～970 nm had a high correlation (R2=0.92 and RMSE=0.04 m3·m－3) in the loam. The BP-ANN model presented a better estimation accuracy of θ (R2=0.87 and RMSE=0.05 m3·m－3) in two soils. Thus, the ANN model has great potential for estimating θ. Thus, the BP-ANN model has great potential for θ estimation.