Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia
Abstract Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500...
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| Autor*in: |
Mohanasundaram, S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: International journal of plant production - [Cham] : Springer International Publishing, 2007, 17(2022), 1 vom: 10. Nov., Seite 1-16 |
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| Übergeordnetes Werk: |
volume:17 ; year:2022 ; number:1 ; day:10 ; month:11 ; pages:1-16 |
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| DOI / URN: |
10.1007/s42106-022-00223-2 |
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| Katalog-ID: |
SPR049441884 |
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| 520 | |a Abstract Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000–2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85–1.2 t/ha which lies in the error range of 10–15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1–10 t/ha and 1–12.5 kg/$ m^{3} $, respectively. | ||
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| 650 | 4 | |a Rice yield |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a South and southeast Asia |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Santikayasa, I. Putu |4 aut | |
| 700 | 1 | |a Singh, Shilpa |4 aut | |
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10.1007/s42106-022-00223-2 doi (DE-627)SPR049441884 (SPR)s42106-022-00223-2-e DE-627 ger DE-627 rakwb eng Mohanasundaram, S. verfasserin (orcid)0000-0002-3199-8403 aut Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000–2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85–1.2 t/ha which lies in the error range of 10–15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1–10 t/ha and 1–12.5 kg/$ m^{3} $, respectively. Crop water productivity (dpeaa)DE-He213 Rice yield (dpeaa)DE-He213 South and southeast Asia (dpeaa)DE-He213 Downscaling (dpeaa)DE-He213 EVI, NDVI, GPP, LAI (dpeaa)DE-He213 Kasiviswanathan, K. S. aut Purnanjali, C. aut Santikayasa, I. Putu aut Singh, Shilpa aut Enthalten in International journal of plant production [Cham] : Springer International Publishing, 2007 17(2022), 1 vom: 10. Nov., Seite 1-16 (DE-627)529090082 (DE-600)2299088-4 1735-6814 nnns volume:17 year:2022 number:1 day:10 month:11 pages:1-16 https://dx.doi.org/10.1007/s42106-022-00223-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2472 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 1 10 11 1-16 |
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10.1007/s42106-022-00223-2 doi (DE-627)SPR049441884 (SPR)s42106-022-00223-2-e DE-627 ger DE-627 rakwb eng Mohanasundaram, S. verfasserin (orcid)0000-0002-3199-8403 aut Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000–2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85–1.2 t/ha which lies in the error range of 10–15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1–10 t/ha and 1–12.5 kg/$ m^{3} $, respectively. Crop water productivity (dpeaa)DE-He213 Rice yield (dpeaa)DE-He213 South and southeast Asia (dpeaa)DE-He213 Downscaling (dpeaa)DE-He213 EVI, NDVI, GPP, LAI (dpeaa)DE-He213 Kasiviswanathan, K. S. aut Purnanjali, C. aut Santikayasa, I. Putu aut Singh, Shilpa aut Enthalten in International journal of plant production [Cham] : Springer International Publishing, 2007 17(2022), 1 vom: 10. Nov., Seite 1-16 (DE-627)529090082 (DE-600)2299088-4 1735-6814 nnns volume:17 year:2022 number:1 day:10 month:11 pages:1-16 https://dx.doi.org/10.1007/s42106-022-00223-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2472 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 1 10 11 1-16 |
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10.1007/s42106-022-00223-2 doi (DE-627)SPR049441884 (SPR)s42106-022-00223-2-e DE-627 ger DE-627 rakwb eng Mohanasundaram, S. verfasserin (orcid)0000-0002-3199-8403 aut Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000–2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85–1.2 t/ha which lies in the error range of 10–15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1–10 t/ha and 1–12.5 kg/$ m^{3} $, respectively. Crop water productivity (dpeaa)DE-He213 Rice yield (dpeaa)DE-He213 South and southeast Asia (dpeaa)DE-He213 Downscaling (dpeaa)DE-He213 EVI, NDVI, GPP, LAI (dpeaa)DE-He213 Kasiviswanathan, K. S. aut Purnanjali, C. aut Santikayasa, I. Putu aut Singh, Shilpa aut Enthalten in International journal of plant production [Cham] : Springer International Publishing, 2007 17(2022), 1 vom: 10. Nov., Seite 1-16 (DE-627)529090082 (DE-600)2299088-4 1735-6814 nnns volume:17 year:2022 number:1 day:10 month:11 pages:1-16 https://dx.doi.org/10.1007/s42106-022-00223-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2472 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 1 10 11 1-16 |
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10.1007/s42106-022-00223-2 doi (DE-627)SPR049441884 (SPR)s42106-022-00223-2-e DE-627 ger DE-627 rakwb eng Mohanasundaram, S. verfasserin (orcid)0000-0002-3199-8403 aut Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000–2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85–1.2 t/ha which lies in the error range of 10–15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1–10 t/ha and 1–12.5 kg/$ m^{3} $, respectively. Crop water productivity (dpeaa)DE-He213 Rice yield (dpeaa)DE-He213 South and southeast Asia (dpeaa)DE-He213 Downscaling (dpeaa)DE-He213 EVI, NDVI, GPP, LAI (dpeaa)DE-He213 Kasiviswanathan, K. S. aut Purnanjali, C. aut Santikayasa, I. Putu aut Singh, Shilpa aut Enthalten in International journal of plant production [Cham] : Springer International Publishing, 2007 17(2022), 1 vom: 10. Nov., Seite 1-16 (DE-627)529090082 (DE-600)2299088-4 1735-6814 nnns volume:17 year:2022 number:1 day:10 month:11 pages:1-16 https://dx.doi.org/10.1007/s42106-022-00223-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2472 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 1 10 11 1-16 |
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10.1007/s42106-022-00223-2 doi (DE-627)SPR049441884 (SPR)s42106-022-00223-2-e DE-627 ger DE-627 rakwb eng Mohanasundaram, S. verfasserin (orcid)0000-0002-3199-8403 aut Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000–2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85–1.2 t/ha which lies in the error range of 10–15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1–10 t/ha and 1–12.5 kg/$ m^{3} $, respectively. Crop water productivity (dpeaa)DE-He213 Rice yield (dpeaa)DE-He213 South and southeast Asia (dpeaa)DE-He213 Downscaling (dpeaa)DE-He213 EVI, NDVI, GPP, LAI (dpeaa)DE-He213 Kasiviswanathan, K. S. aut Purnanjali, C. aut Santikayasa, I. Putu aut Singh, Shilpa aut Enthalten in International journal of plant production [Cham] : Springer International Publishing, 2007 17(2022), 1 vom: 10. Nov., Seite 1-16 (DE-627)529090082 (DE-600)2299088-4 1735-6814 nnns volume:17 year:2022 number:1 day:10 month:11 pages:1-16 https://dx.doi.org/10.1007/s42106-022-00223-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2472 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2022 1 10 11 1-16 |
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Mohanasundaram, S. @@aut@@ Kasiviswanathan, K. S. @@aut@@ Purnanjali, C. @@aut@@ Santikayasa, I. Putu @@aut@@ Singh, Shilpa @@aut@@ |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000–2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85–1.2 t/ha which lies in the error range of 10–15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. 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Mohanasundaram, S. |
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Mohanasundaram, S. misc Crop water productivity misc Rice yield misc South and southeast Asia misc Downscaling misc EVI, NDVI, GPP, LAI Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia |
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Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia Crop water productivity (dpeaa)DE-He213 Rice yield (dpeaa)DE-He213 South and southeast Asia (dpeaa)DE-He213 Downscaling (dpeaa)DE-He213 EVI, NDVI, GPP, LAI (dpeaa)DE-He213 |
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misc Crop water productivity misc Rice yield misc South and southeast Asia misc Downscaling misc EVI, NDVI, GPP, LAI |
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misc Crop water productivity misc Rice yield misc South and southeast Asia misc Downscaling misc EVI, NDVI, GPP, LAI |
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misc Crop water productivity misc Rice yield misc South and southeast Asia misc Downscaling misc EVI, NDVI, GPP, LAI |
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Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia |
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Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia |
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Mohanasundaram, S. |
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International journal of plant production |
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Mohanasundaram, S. Kasiviswanathan, K. S. Purnanjali, C. Santikayasa, I. Putu Singh, Shilpa |
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Mohanasundaram, S. |
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downscaling global gridded crop yield data products and crop water productivity mapping using remote sensing derived variables in the south asia |
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Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia |
| abstract |
Abstract Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000–2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85–1.2 t/ha which lies in the error range of 10–15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1–10 t/ha and 1–12.5 kg/$ m^{3} $, respectively. © Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000–2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85–1.2 t/ha which lies in the error range of 10–15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1–10 t/ha and 1–12.5 kg/$ m^{3} $, respectively. © Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000–2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85–1.2 t/ha which lies in the error range of 10–15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1–10 t/ha and 1–12.5 kg/$ m^{3} $, respectively. © Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia |
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https://dx.doi.org/10.1007/s42106-022-00223-2 |
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Kasiviswanathan, K. S. Purnanjali, C. Santikayasa, I. Putu Singh, Shilpa |
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Kasiviswanathan, K. S. Purnanjali, C. Santikayasa, I. Putu Singh, Shilpa |
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10.1007/s42106-022-00223-2 |
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| score |
7.3990545 |