ScatSat-1 Leaf Area Index Product: Models Comparison, Development, and Validation Over Cropland

The leaf area index (LAI) is a crucial parameter that governs the physical and biophysical processes of plant canopies and acts as an input variable in land surface and soil moisture modeling. The ScatSat-1 is the latest microwave Ku-band scatterometer mission of Indian Space Research Organization (...

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Veröffentlicht in:IEEE geoscience and remote sensing letters 2020-04, Vol.17 (4), p.563-567
Hauptverfasser: Singh, Ujjwal, Srivastava, Prashant K., Pandey, Dharmendra Kumar, Chaurasia, Sasmita, Gupta, Dileep Kumar, Chaudhary, Sumit Kumar, Prasad, Rajendra, Raghubanshi, A. S.
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container_title IEEE geoscience and remote sensing letters
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creator Singh, Ujjwal
Srivastava, Prashant K.
Pandey, Dharmendra Kumar
Chaurasia, Sasmita
Gupta, Dileep Kumar
Chaudhary, Sumit Kumar
Prasad, Rajendra
Raghubanshi, A. S.
description The leaf area index (LAI) is a crucial parameter that governs the physical and biophysical processes of plant canopies and acts as an input variable in land surface and soil moisture modeling. The ScatSat-1 is the latest microwave Ku-band scatterometer mission of Indian Space Research Organization (ISRO), provides data at a higher temporal and spatial resolution for various applications. Due to its all-weather operational capability, it could be used as an alternative to the optical/IR sensors for the LAI estimation. In the technical literature domain, no testing has been done to estimate the LAI using ScatSat-1 scatterometer data. Therefore, the objective of this study is to retrieve the LAI using the ScatSat-1 backscattering by modifications of two different models viz. water cloud model (WCM) and the recently developed Oveisgharan et al. model and compared against the PROBA-V, MODIS, and ground-based LAI products. To assess the performance of these models, coefficient of determination ( R^{2} ), root-mean-squared error (RMSE) and bias are computed. For Oveisgharan et al. , the values of R^{2} , RMSE and bias were obtained as 0.87, 0.57 m 2 m −2 , and 0.05 m 2 m −2 respectively, whereas for WCM model, the values were found as 0.82, 0.67 m 2 m −2 , and 0.32 m 2 m −2 respectively. This investigation showed that the modifications in Oveisgharan et al. model provide marginally better results in the retrieval of LAI using ScatSat-1 data than the WCM model. The models' limitation may be less serious for crop management studies because the majority of crops attains its maturity at LAI values less than 6 m 2 /m 2 .
doi_str_mv 10.1109/LGRS.2019.2927468
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Therefore, the objective of this study is to retrieve the LAI using the ScatSat-1 backscattering by modifications of two different models viz. water cloud model (WCM) and the recently developed Oveisgharan et al. model and compared against the PROBA-V, MODIS, and ground-based LAI products. To assess the performance of these models, coefficient of determination (<inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula>), root-mean-squared error (RMSE) and bias are computed. For Oveisgharan et al. , the values of <inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula>, RMSE and bias were obtained as 0.87, 0.57 m 2 m −2 , and 0.05 m 2 m −2 respectively, whereas for WCM model, the values were found as 0.82, 0.67 m 2 m −2 , and 0.32 m 2 m −2 respectively. This investigation showed that the modifications in Oveisgharan et al. model provide marginally better results in the retrieval of LAI using ScatSat-1 data than the WCM model. 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S.</creatorcontrib><title>ScatSat-1 Leaf Area Index Product: Models Comparison, Development, and Validation Over Cropland</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description><![CDATA[The leaf area index (LAI) is a crucial parameter that governs the physical and biophysical processes of plant canopies and acts as an input variable in land surface and soil moisture modeling. The ScatSat-1 is the latest microwave Ku-band scatterometer mission of Indian Space Research Organization (ISRO), provides data at a higher temporal and spatial resolution for various applications. Due to its all-weather operational capability, it could be used as an alternative to the optical/IR sensors for the LAI estimation. In the technical literature domain, no testing has been done to estimate the LAI using ScatSat-1 scatterometer data. 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S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ScatSat-1 Leaf Area Index Product: Models Comparison, Development, and Validation Over Cropland</atitle><jtitle>IEEE geoscience and remote sensing letters</jtitle><stitle>LGRS</stitle><date>2020-04-01</date><risdate>2020</risdate><volume>17</volume><issue>4</issue><spage>563</spage><epage>567</epage><pages>563-567</pages><issn>1545-598X</issn><eissn>1558-0571</eissn><coden>IGRSBY</coden><abstract><![CDATA[The leaf area index (LAI) is a crucial parameter that governs the physical and biophysical processes of plant canopies and acts as an input variable in land surface and soil moisture modeling. The ScatSat-1 is the latest microwave Ku-band scatterometer mission of Indian Space Research Organization (ISRO), provides data at a higher temporal and spatial resolution for various applications. Due to its all-weather operational capability, it could be used as an alternative to the optical/IR sensors for the LAI estimation. In the technical literature domain, no testing has been done to estimate the LAI using ScatSat-1 scatterometer data. Therefore, the objective of this study is to retrieve the LAI using the ScatSat-1 backscattering by modifications of two different models viz. water cloud model (WCM) and the recently developed Oveisgharan et al. model and compared against the PROBA-V, MODIS, and ground-based LAI products. To assess the performance of these models, coefficient of determination (<inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula>), root-mean-squared error (RMSE) and bias are computed. 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The models' limitation may be less serious for crop management studies because the majority of crops attains its maturity at LAI values less than 6 m 2 /m 2 .]]></abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LGRS.2019.2927468</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0002-4155-630X</orcidid><orcidid>https://orcid.org/0000-0001-6553-0983</orcidid><orcidid>https://orcid.org/0000-0001-7475-6482</orcidid><orcidid>https://orcid.org/0000-0002-9325-4653</orcidid><orcidid>https://orcid.org/0000-0002-4060-5368</orcidid></addata></record>
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source IEEE Electronic Library (IEL)
subjects Agricultural land
Agriculture
Backscatter
Backscattering
Bias
Computational modeling
Crop management
Indexes
Indian space program
Leaf area
Leaf area index
leaf area index (LAI)
Leaves
Oveisgharan <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">et al.
Root-mean-square errors
ScatSat-1
Scatterometers
Sea measurements
Soil
Soil moisture
Space research
Spatial discrimination
Spatial resolution
Synthetic aperture radar
Technical literature
Vegetation mapping
water cloud model (WCM)
Weather
title ScatSat-1 Leaf Area Index Product: Models Comparison, Development, and Validation Over Cropland
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