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
Monitoring algal blooms by optical remote sensing is limited by cloud cover.In this study,synthetic aperture radar(SAR) was deployed with the aim of monitoring cyanobacteria-dominant algal blooms in Taihu Lake in clou...Monitoring algal blooms by optical remote sensing is limited by cloud cover.In this study,synthetic aperture radar(SAR) was deployed with the aim of monitoring cyanobacteria-dominant algal blooms in Taihu Lake in cloudy weather.The study shows that dark regions in the SAR images caused by cyanobacterial blooms damped the microwave backscatter of the lake surface and were consistent with the regions of algal blooms in quasi-synchronous optical images,confirming the applicability of SAR for detection of surface blooms.Low backscatter may also be associated with other factors such as low wind speeds,resulting in interference when monitoring algal blooms using SAR data alone.After feature extraction and selection,the dark regions were classified by the support vector machine method with an overall accuracy of 67.74%.SAR can provide a reference point for monitoring cyanobacterial blooms in the lake,particularly when weather is not suitable for optical remote sensing.Multi-polarization and multi-band SAR can be considered for use in the future to obtain more accurate information regarding algal blooms from SAR data.展开更多
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.
基金Acknowledgements This research was supported by the National Science and Technology Program of China （Grant No. SinoProbe-08）, the National Natural Science Foundation of China （Grant No. 40902093）, the National Social Science Foundation of China （Grant No. 07CZZ019）, the Development Foundation of Experimental Teaching Equipment in East China Normal University （Grant No. 64100010） and the Open Foundation of Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration （Grant No. SHUES2011A06）. We would like to thank the Editor and two anonymous reviewers for their helpful and constructive suggestions for improving the paper.
基金Supported by the High Resolution Earth Observation Systems of National Science and Technology Major Projects(No.05-Y30B02-9001-13/155)the National High Technology Research and Development Program of China(Nos.2012AA12A301,2013AA12A302)the Key Basic Research Project of the Science and Technology Commission of Shanghai Municipality(No.12510502000)
文摘Monitoring algal blooms by optical remote sensing is limited by cloud cover.In this study,synthetic aperture radar(SAR) was deployed with the aim of monitoring cyanobacteria-dominant algal blooms in Taihu Lake in cloudy weather.The study shows that dark regions in the SAR images caused by cyanobacterial blooms damped the microwave backscatter of the lake surface and were consistent with the regions of algal blooms in quasi-synchronous optical images,confirming the applicability of SAR for detection of surface blooms.Low backscatter may also be associated with other factors such as low wind speeds,resulting in interference when monitoring algal blooms using SAR data alone.After feature extraction and selection,the dark regions were classified by the support vector machine method with an overall accuracy of 67.74%.SAR can provide a reference point for monitoring cyanobacterial blooms in the lake,particularly when weather is not suitable for optical remote sensing.Multi-polarization and multi-band SAR can be considered for use in the future to obtain more accurate information regarding algal blooms from SAR data.
