基于野外实测Vis-NIR光谱的土壤肥力估算研究

为探讨野外实测光谱数据对土壤肥力的估算能力，采集青海省湟水流域表层0 ~ 20 cm土壤样品220份，同步测量其采样位置的野外实测光谱数据，实验室对土壤养分、机械组成含量以及pH值进行分析。基于上述数据，对野外实测光谱反射率进行多元散射校正（Multiplicative scatter correction，MSC）、SG-一阶导数变换（SG - First Derivative，SG-1st）预处理，采用稳定性竞争自适应重加权采样法（stability competitive adaptive reweighted sampling，SCARS）提取不同土壤养分、机械组成含量以及pH值的特征波段，以偏最小二乘回归（partial least squares regression，PLSR）模型对土壤全碳（TC）、有机质（OM）、全氮（TN）、碱解氮（AN）、pH、黏粒（clay）、粉粒（silt）、砂粒（sand）含量进行估算并对比分析，构建土壤养分含量、pH值以及机械组成含量的最优野外实测光谱估算模型。结果表明:通过MSC校正和SG-1st变换能够有效增强野外光谱特征；经SCARS选取的特征波段主要集中于近红外波段。基于野外实测光谱数据建立的PLSR模型能够对研究区土壤TC、OM、TN、AN含量以及pH值进行粗略估算；其中，对于TC、OM、TN含量及pH值而言，最佳估算模型为经SG-1st处理后的SCARS-PLSR模型，RPD值均达到1.70以上（RPD_TC = 1.76; RPD_OM = 1.82；RPD_TN = 2.04；RPD_pH = 1.89），RPIQ值均达到1.90以上（RPIQ_TC = 1.91；RPIQ_OM = 2.53；RPIQ_TN = 2.98；RPIQ_pH = 2.03）；对于土壤AN含量而言，经MSC处理后的SCARS-PLSR模型最佳，其RPD_AN值高达1.91，RPIQ值高达2.39。对土壤clay、silt以及sand含量野外光谱均无法估算，RPD值均在1.00左右，RPIQ值在1.20左右。

Estimating Fertility Index by Using Field-Measured Vis-NIR Spectroscopy in the Huanghui River Basin

In order to explore the ability of field vis-NIR spectroscopy for estimating soil fertility index, 220 soil samples in the depth profile of 20 cm were collected in 2015, 2016 and 2017 in the Huangshui river basin, respectively. Soil reflectance spectroscopy of 350-2500 nm were synchronously measured in field by using ASD Field Spectrometer. Soil nutrients, pH and mechanical composition were analyzed in laboratory. The pretreatments of multiplicative scatter correction (MSC) and SG-1st derivative transform (SG-1st) were used to obtain the spectral reflectance curve, and then stability competitive adaptive reweighted sampling (SCARS) was used to select characteristic wave bands of soil properties. The prediction models for the contents of soil total carbon (TC), organic matter (OM), total nitrogen (TN), alkeline nitrogen (AN), pH, clay, silt and sand were constructed by partial least squares regression(PLSR), then selecting the optimal model for every soil property by comparison analysis. The results showed that SG-1st transformation and MSC correction could effectively enhance the field spectral characteristics of soil nutrient, pH and soil mechanical composition. The characteristic bands selected by SCARS were mainly focused on the near infrared wave bands. PLSR model based on field vis-NIR spectroscopy could roughly estimate the contents of soil TC, OM, TN and AN as well as pH values. For the properties of soil TC, OM, TN and pH, the best estimation model was SCARS-PLSR by SG-1st processed with the RPD values over 1.70 (RPD_TC = 1.76; RPD_OM = 1.82; RPD_TN = 2.04; RPD_pH = 1.89), the RPIQ values over 1.90 (RPIQ_TC = 1.91; RPIQ_OM = 2.53; RPIQ_TN = 2.98; RPIQ_pH = 2.03). For soil AN contents, the best estimation model was SCARS-PLSR by MSC processed with the RPD values up to 1.91 and the RPIQ values up to 2.39. The contents of soil clay, silt and sand could not be estimated with all the RPD values around 1.00 and the RPIQ values around 1.20.