土壤重金属高光谱遥感建模理论上能够大大降低传统化学分析测定所需成本, 正逐步发展为有效探查土壤污染空间分布与开展污染土壤综合防治的关键技术。 然而土壤重金属高光谱遥感调查技术目前多局限于稳定可控条件下的实验室光谱模型, 野外诸多因素(光照、 湿度、 土壤粗糙度等)影响下野外原位光谱模型的有效性已成为困扰该项技术大范围推广亟待突破的关键科学问题。 以湖南衡阳市某矿区为例, 分别利用ASD地物光谱仪和等离子发射光谱法测定46个土壤样品350~2 500 nm的实验室光谱和Cd含量, 并在土壤取样时同步测量样品野外原位光谱。 在运用DS(direct standardization)转换算法处理野外光谱的基础上, 融合实验室光谱先验知识, 基于主成分逐步回归建模方法开展了土壤Cd含量实验室与野外原位DS光谱联合反演实验, 交叉验证了模型的稳定性。 同时为深入探究实验室与野外原位DS光谱联合反演模型的有效性, 将其与基于实验室光谱、 野外原位光谱、 野外原位DS光谱、 实验室与野外原位光谱联合建立的主成分逐步回归模型开展了对比分析。 结果表明: 野外原位光谱反演模型精度(R2=0.56)明显低于实验室光谱反演模型(R2=0.64), 野外原位DS光谱反演模型与之相比精度有所提升(R2=0.66); 在野外原位光谱DS转换校正基础上, 联合实验室光谱先验知识的土壤Cd含量反演模型精度最高, R2可达0.72。 与此同时, 实验室与野外原位DS光谱联合反演模型揭示482, 565, 979和2 206 nm波段对研究区土壤Cd含量有较好指示性, 此结果与实验室光谱反演模型所识别的特征波段一致, 两者物理意义相同。 研究结果证实了实验室光谱先验知识以及DS转换算法能够提升野外原位光谱模型的可靠性, 可为发展土壤Cd含量野外原位高光谱遥感探测提供重要的提供理论与方法支撑。

Theoretically, hyperspectral remote sensing aided content estimation of soilheavy metal can greatly reduce the cost of conventional chemistries. As a result, hyperspectral remote sensing is gradually becoming a key technology to effectively explore the spatial distribution of soil heavy metal and consequently guide theprevention and remediation of heavy metal polluted soil. However, currently reported hyperspectral retrieval models for soil heavy metal estimation are mostly with laboratory spectra under specifically controlled conditions. Due to the impacts of environmental factors, such as illumination, soil moisture content, and roughness onin-situ field spectra, the wide implementation of in-situ field spectra based remote sensing detection of soil heavy metal is still experiencing the difficulty of reliability. For this, 46 soil samples were firstly collected from a mining area in Hengyang of Hunan Province, China. Then the spectra (ranged 350~2 500 nm) and Cd content of these soil samples were measured using ASD field spectrometer and ICP-atomic emission spectrometry under in-situ field and laboratory conditions, respectively. Then, considering the prior knowledge of laboratory spectra, the principal stepwise regression method was used to develop the Cd content estimation model based on combined laboratory and direct standardization (DS) algorithm transformed in situ field DS spectra with the model robust test by cross-validation. In order to further prove the effectiveness of the model with combined laboratory and DS transformed in-situ field DS spectra, the performance of this model was then compared with four types of hyperspectral remote sensing models including those with spectra from the laboratory, in-situ field, DS transformed in-situ field only, as well as with combined laboratory and in-situ field, one by one. The result shows that while the precision of the hyperspectral remote sensing model with in-situ field spectra (R2=0.56) is lower than the one with laboratory spectra (R2=0.64), the precision of the model with DS transformed in-situ field spectra is improved (R2=0.66). The model with combined laboratory and DS transformed in-situ field spectra is the one with the highest accuracy (R2, 0.72). Meanwhile, this highest robust model discloses that the wavebands located at 482, 565, 979, and 2 206 nm have significantly strong correlations with the soil Cd content. And this result is physically consistent with the model with laboratory spectra. In summary, results in this study confirm the role of the prior knowledge of laboratory spectra and DS algorithm in enhancing the reliability of in-situ field spectra based hyperspectral remote sensing model for soil Cd content estimation. It could provide new theoretical and methodological evidence for the development of soil Cd content estimation by using hyperspectral remote sensing.