土壤含水量的时空分布与变化情况对土壤温度变化、陆地—大气间热量平衡以及陆面大气环流产生显著的影响,因此,对大范围内土壤含水量进行实时动态监测,获得某段时间内土壤含水量的连续变化情况具有重要的意义。研究目的是借助高光谱遥...土壤含水量的时空分布与变化情况对土壤温度变化、陆地—大气间热量平衡以及陆面大气环流产生显著的影响,因此,对大范围内土壤含水量进行实时动态监测,获得某段时间内土壤含水量的连续变化情况具有重要的意义。研究目的是借助高光谱遥感手段,通过构建不同质量含水量的土壤反射率光谱模拟模型,深入了解土壤质量含水量与土壤反射率光谱之间的关系,为监测土壤含水量提供有效手段。利用ASD Field Spectral FR野外光谱仪和加水称重法获得北京市8个采样点的土壤样品不同质量含水量下的土壤反射率光谱实测数据,利用其中2个土壤样品不同质量含水量下的光谱数据构建含水土壤反射率光谱模拟模型,并利用未参与建模的另外6个土壤样品数据对该模型的模拟效果进行了检验。通过数据验证发现,当土壤质量含水量小于田间持水量时,该模型的模拟精度较高;而且对于不同的土壤样品,模型的模拟效果都比较好。最后又利用北京大学校园内三个采样点的实地测量光谱数据对模型进行了验证,光谱的模拟值与实测值之间的均方根误差最小可达0.005 8。因此该模型可实现对质量含水量小于田间持水量的不同类型土壤的反射率光谱进行较高精度的模拟。展开更多
Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiomet...Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory condition. Partial least square regression (PLSR) models were constructed for predicting soil metal concentrations. The data pre-processing methods, first and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), were used for the spectral reflectance data pretreatments. Then, the prediction results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R 2 ). According to the criteria of minimal RRMSE and maximal R 2 , the PLSR models with the FD pretreatment (RRMSE = 0.24, R 2 = 0.61), SNV pretreatment (RRMSE = 0.08, R 2 = 0.78), and BC-pretreatment (RRMSE = 0.20, R 2 = 0.41) were considered as the final models for predicting As, Fe, and Cu, respectively. Wavebands at around 460, 1 400, 1 900, and 2 200 nm were selected as important spectral variables to construct final models. In conclusion, concentrations of heavy metals in contaminated soils could be indirectly assessed by soil spectra according to the correlation between the spectrally featureless components and Fe; therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination.展开更多
文摘土壤含水量的时空分布与变化情况对土壤温度变化、陆地—大气间热量平衡以及陆面大气环流产生显著的影响,因此,对大范围内土壤含水量进行实时动态监测,获得某段时间内土壤含水量的连续变化情况具有重要的意义。研究目的是借助高光谱遥感手段,通过构建不同质量含水量的土壤反射率光谱模拟模型,深入了解土壤质量含水量与土壤反射率光谱之间的关系,为监测土壤含水量提供有效手段。利用ASD Field Spectral FR野外光谱仪和加水称重法获得北京市8个采样点的土壤样品不同质量含水量下的土壤反射率光谱实测数据,利用其中2个土壤样品不同质量含水量下的光谱数据构建含水土壤反射率光谱模拟模型,并利用未参与建模的另外6个土壤样品数据对该模型的模拟效果进行了检验。通过数据验证发现,当土壤质量含水量小于田间持水量时,该模型的模拟精度较高;而且对于不同的土壤样品,模型的模拟效果都比较好。最后又利用北京大学校园内三个采样点的实地测量光谱数据对模型进行了验证,光谱的模拟值与实测值之间的均方根误差最小可达0.005 8。因此该模型可实现对质量含水量小于田间持水量的不同类型土壤的反射率光谱进行较高精度的模拟。
基金Project supported by the National Natural Science Foundation of China (No. 40571130)the Natural Science Foundation of Shanghai, China (No. 07ZR14032)
文摘Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory condition. Partial least square regression (PLSR) models were constructed for predicting soil metal concentrations. The data pre-processing methods, first and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), were used for the spectral reflectance data pretreatments. Then, the prediction results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R 2 ). According to the criteria of minimal RRMSE and maximal R 2 , the PLSR models with the FD pretreatment (RRMSE = 0.24, R 2 = 0.61), SNV pretreatment (RRMSE = 0.08, R 2 = 0.78), and BC-pretreatment (RRMSE = 0.20, R 2 = 0.41) were considered as the final models for predicting As, Fe, and Cu, respectively. Wavebands at around 460, 1 400, 1 900, and 2 200 nm were selected as important spectral variables to construct final models. In conclusion, concentrations of heavy metals in contaminated soils could be indirectly assessed by soil spectra according to the correlation between the spectrally featureless components and Fe; therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination.
