A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed

Abstract A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer mo...
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Gespeichert in:

Autor*in:	Hui, Jiang [verfasserIn] Yao, Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Leaf area index Poyang Lake GF-1 MODIS LAI products

Übergeordnetes Werk:	Enthalten in: Journal of the Indian Society of Remote Sensing - Neu Delhi : Springer India, 2008, 46(2017), 4 vom: 01. Dez., Seite 551-560
Übergeordnetes Werk:	volume:46 ; year:2017 ; number:4 ; day:01 ; month:12 ; pages:551-560

Links:	Volltext

DOI / URN:	10.1007/s12524-017-0731-5

Katalog-ID:	SPR026024225

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245	1	2	\|a A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed
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520			\|a Abstract A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring.
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allfields	10.1007/s12524-017-0731-5 doi (DE-627)SPR026024225 (SPR)s12524-017-0731-5-e DE-627 ger DE-627 rakwb eng 550 ASE Hui, Jiang verfasserin aut A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring. Leaf area index (dpeaa)DE-He213 Poyang Lake (dpeaa)DE-He213 GF-1 (dpeaa)DE-He213 MODIS LAI products (dpeaa)DE-He213 Yao, Liu verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 46(2017), 4 vom: 01. Dez., Seite 551-560 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:46 year:2017 number:4 day:01 month:12 pages:551-560 https://dx.doi.org/10.1007/s12524-017-0731-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2017 4 01 12 551-560
spelling	10.1007/s12524-017-0731-5 doi (DE-627)SPR026024225 (SPR)s12524-017-0731-5-e DE-627 ger DE-627 rakwb eng 550 ASE Hui, Jiang verfasserin aut A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring. Leaf area index (dpeaa)DE-He213 Poyang Lake (dpeaa)DE-He213 GF-1 (dpeaa)DE-He213 MODIS LAI products (dpeaa)DE-He213 Yao, Liu verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 46(2017), 4 vom: 01. Dez., Seite 551-560 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:46 year:2017 number:4 day:01 month:12 pages:551-560 https://dx.doi.org/10.1007/s12524-017-0731-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2017 4 01 12 551-560
allfields_unstemmed	10.1007/s12524-017-0731-5 doi (DE-627)SPR026024225 (SPR)s12524-017-0731-5-e DE-627 ger DE-627 rakwb eng 550 ASE Hui, Jiang verfasserin aut A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring. Leaf area index (dpeaa)DE-He213 Poyang Lake (dpeaa)DE-He213 GF-1 (dpeaa)DE-He213 MODIS LAI products (dpeaa)DE-He213 Yao, Liu verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 46(2017), 4 vom: 01. Dez., Seite 551-560 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:46 year:2017 number:4 day:01 month:12 pages:551-560 https://dx.doi.org/10.1007/s12524-017-0731-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2017 4 01 12 551-560
allfieldsGer	10.1007/s12524-017-0731-5 doi (DE-627)SPR026024225 (SPR)s12524-017-0731-5-e DE-627 ger DE-627 rakwb eng 550 ASE Hui, Jiang verfasserin aut A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring. Leaf area index (dpeaa)DE-He213 Poyang Lake (dpeaa)DE-He213 GF-1 (dpeaa)DE-He213 MODIS LAI products (dpeaa)DE-He213 Yao, Liu verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 46(2017), 4 vom: 01. Dez., Seite 551-560 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:46 year:2017 number:4 day:01 month:12 pages:551-560 https://dx.doi.org/10.1007/s12524-017-0731-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2017 4 01 12 551-560
allfieldsSound	10.1007/s12524-017-0731-5 doi (DE-627)SPR026024225 (SPR)s12524-017-0731-5-e DE-627 ger DE-627 rakwb eng 550 ASE Hui, Jiang verfasserin aut A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring. Leaf area index (dpeaa)DE-He213 Poyang Lake (dpeaa)DE-He213 GF-1 (dpeaa)DE-He213 MODIS LAI products (dpeaa)DE-He213 Yao, Liu verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 46(2017), 4 vom: 01. Dez., Seite 551-560 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:46 year:2017 number:4 day:01 month:12 pages:551-560 https://dx.doi.org/10.1007/s12524-017-0731-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2017 4 01 12 551-560
language	English
source	Enthalten in Journal of the Indian Society of Remote Sensing 46(2017), 4 vom: 01. Dez., Seite 551-560 volume:46 year:2017 number:4 day:01 month:12 pages:551-560
sourceStr	Enthalten in Journal of the Indian Society of Remote Sensing 46(2017), 4 vom: 01. Dez., Seite 551-560 volume:46 year:2017 number:4 day:01 month:12 pages:551-560
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Leaf area index Poyang Lake GF-1 MODIS LAI products
dewey-raw	550
isfreeaccess_bool	false
container_title	Journal of the Indian Society of Remote Sensing
authorswithroles_txt_mv	Hui, Jiang @@aut@@ Yao, Liu @@aut@@
publishDateDaySort_date	2017-12-01T00:00:00Z
hierarchy_top_id	573088853
dewey-sort	3550
id	SPR026024225
language_de	englisch
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author	Hui, Jiang
spellingShingle	Hui, Jiang ddc 550 misc Leaf area index misc Poyang Lake misc GF-1 misc MODIS LAI products A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed
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topic_title	550 ASE A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed Leaf area index (dpeaa)DE-He213 Poyang Lake (dpeaa)DE-He213 GF-1 (dpeaa)DE-He213 MODIS LAI products (dpeaa)DE-He213
topic	ddc 550 misc Leaf area index misc Poyang Lake misc GF-1 misc MODIS LAI products
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title	A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed
ctrlnum	(DE-627)SPR026024225 (SPR)s12524-017-0731-5-e
title_full	A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed
author_sort	Hui, Jiang
journal	Journal of the Indian Society of Remote Sensing
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author_browse	Hui, Jiang Yao, Liu
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author-letter	Hui, Jiang
doi_str_mv	10.1007/s12524-017-0731-5
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title_sort	method to upscale the leaf area index (lai) using gf-1 data with the assistance of modis products in the poyang lake watershed
title_auth	A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed
abstract	Abstract A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring.
abstractGer	Abstract A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring.
abstract_unstemmed	Abstract A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring.
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container_issue	4
title_short	A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed
url	https://dx.doi.org/10.1007/s12524-017-0731-5
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up_date	2024-07-03T18:25:13.822Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR026024225</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111132729.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2017 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12524-017-0731-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR026024225</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12524-017-0731-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hui, Jiang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Leaf area index</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Poyang Lake</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GF-1</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MODIS LAI products</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yao, Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of the Indian Society of Remote Sensing</subfield><subfield code="d">Neu Delhi : Springer India, 2008</subfield><subfield code="g">46(2017), 4 vom: 01. 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A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed

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