Synopsis
Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analyzed to predict the spatial SOC content, using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyper-spectral image, field and laboratory scale data sets (350 - 2500 nm) were generated, consisting of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflection data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and three data set (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation, indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods have a great potential for estimating SOC content.
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About the Author
Mr. Sudheer Kumar Tiwari is working as Scientist in Andhra Pradesh Space Applications Centre (APSAC), Planning Department, Govt. of Andhra Pradesh. He has completed his M.Tech. in Remote Sensing and GIS with distinction from Indian Institute of Remote Sensing, ISRO, Dehradun in 2011.
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Soil Organic Carbon Mapping Using Hyperspectral Remote Sensing and ANN
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LAP LAMBERT Academic Publishing Jun 2017, 2017
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analyzed to predict the spatial SOC content, using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyper-spectral image, field and laboratory scale data sets (350 - 2500 nm) were generated, consisting of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflection data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and three data set (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation, indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods have a great potential for estimating SOC content. 60 pp. Englisch. Seller Inventory # 9783330326033
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Soil Organic Carbon Mapping Using Hyperspectral Remote Sensing and ANN
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Soil Organic Carbon Mapping Using Hyperspectral Remote Sensing and ANN
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Tiwari Sudheer KumarMr. Sudheer Kumar Tiwari is working as Scientist in Andhra Pradesh Space Applications Centre (APSAC), Planning Department, Govt. of Andhra Pradesh. He has completed his M.Tech. in Remote Sensing and GIS with disti. Seller Inventory # 151238084
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Soil Organic Carbon Mapping Using Hyperspectral Remote Sensing and ANN
Published by
LAP LAMBERT Academic Publishing Jun 2017, 2017
ISBN 10: 3330326034
ISBN 13: 9783330326033
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analyzed to predict the spatial SOC content, using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyper-spectral image, field and laboratory scale data sets (350 - 2500 nm) were generated, consisting of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflection data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and three data set (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation, indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods have a great potential for estimating SOC content.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch. Seller Inventory # 9783330326033
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Soil Organic Carbon Mapping Using Hyperspectral Remote Sensing and ANN
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ISBN 13: 9783330326033
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analyzed to predict the spatial SOC content, using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyper-spectral image, field and laboratory scale data sets (350 - 2500 nm) were generated, consisting of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflection data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and three data set (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation, indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods have a great potential for estimating SOC content. Seller Inventory # 9783330326033
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Soil Organic Carbon Mapping Using Hyperspectral Remote Sensing and ANN
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ISBN 10: 3330326034
ISBN 13: 9783330326033
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Taschenbuch. Condition: Neu. Soil Organic Carbon Mapping Using Hyperspectral Remote Sensing and ANN | Sudheer Kumar Tiwari (u. a.) | Taschenbuch | 60 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330326033 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 109362556