农田不同粒级土壤含水量光谱特征及定量预测

【目的】土壤含水量是土壤属性的关键参数。摸清不同机械组成条件下土壤水分的光谱变化并实现土壤含水量的定量预测,为农田水分的快速监测及土壤其他属性的定量获取提供依据。【方法】通过人为控制获得不同粒级和不同含水量的土壤样品,确定室内土壤光谱测定的几何条件,采集不同土样的光谱特征并进行比较,按粒径等级利用最小二乘法(PLSR)建立农田土壤含水量的光谱定量预测模型。【结果】土壤光谱反射率总体趋势是随含水量增加而降低,其差异随着波长的增加和含水量的降低而增加,在1 400 nm和1 900 nm的水分敏感波段随含水量增加光谱吸收深度也增加。但当含水量大于40%时,通过孔径为0.15 mm筛子的土壤样品(处理D-1),在350—1 240 nm光谱反射率随含水量增加而升高,而1 240 nm以后随含水量增加而降低。相对于将所有样本数据混合建立模型,分粒级建立的模型在细颗粒土壤中预测效果得到了明显改善,并且样品越细模型在预测效果和稳定性也越好:最优模型均方根误差RMSE=4.13%,决定系数R2=0.90。同时,数据归一化处理后所建立的模型在一定程度上降低了噪声的影响,从而在预测效果和稳定性上也有所改善。【结论】土壤光谱随含水量的变化而变化,但并不都表现随含水量增加光谱反射率降低的特点,当含水量大于40%时,细颗粒土壤样本表现为在350—1 240 nm波段光谱反射率随含水量增加而升高;土壤含水量预测模型的精度和稳定性随着土壤粒径变小、样本量增大以及光谱数据归一化预处理而得到改善。

Spectral Characteristics and Quantitative Prediction of Soil Water Content under Different Soil Particle Sizes

【Objective】Hyper spectral technology is more and more widely applied in monitoring soil properties. Soil Water Content (SWC) is a key parameter of soil properties. This paper aimed to make clear the spectral response of soil moisture under different soil roughness to predict quantitatively soil water content, and further to provide the basis for rapid monitoring of farmland moisture and other soil properties. 【Method】 Soil samples were sieved through different mesh sizes to classify into different particle sizes, and then set different moisture levels. The spectral reflectance of those samples were compared, and the quantitative prediction models of soil water content were established by Partial Least Squares Regression (PLSR) method. 【Result】The results showed that the spectral reflectance decreased with the increase of soil water content, and the difference became bigger as the increase of wavelength and decrease of soil water content. The absorptions got deeper in 1 400 nm and 1 900 nm with the increase of water content. In those soil samples passed through a sieve with an aperture of 0.15 mm (denoted as D-1), the spectral reflectance increased in 350-1 240 nm and then decreased after 1 240 nm when the water content was more than 40%. Compared with the model constructed from all samples with different sizes, models from the same size were improved in predicting accuracy and stability: the smaller the particle size was, the better the prediction effect and stability of the predicting model were. The RMSE (root mean square error) and R2 of the optimal model were 4.13% and 0.90, respectively. Additionally, normalization of spectral data reduced the influence of noise, and improved the predicting accuracy and stability of the model. 【Conclusion】The spectral generally decreased with soil water content increasing, but soil with small size showed opposite in 350-1 240 nm when the moisture content was greater than 40%. The predicting model for soil water content was improved as size getting smaller and sample number involved getting larger, and the spectral data normalization also improved predicting accuracy and stability of model.