A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India
The present study describes a new dataset that estimates seasonally integrated agricultural gross primary productivity (GPP). Several models are being used to estimate GPP using remote sensing (RS) for regional and global studies. Using biophysical and climatic variables (MODIS, SBSS, ECWMF reanalys...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/129206 |
| _version_ | 1855535597369688064 |
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| author | Gangopadhyay, Prasun K. Shirsath, Paresh B. Dadhwal, Vinay K. Aggarwal, Pramod K. |
| author_browse | Aggarwal, Pramod K. Dadhwal, Vinay K. Gangopadhyay, Prasun K. Shirsath, Paresh B. |
| author_facet | Gangopadhyay, Prasun K. Shirsath, Paresh B. Dadhwal, Vinay K. Aggarwal, Pramod K. |
| author_sort | Gangopadhyay, Prasun K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The present study describes a new dataset that estimates seasonally integrated agricultural gross primary productivity (GPP). Several models are being used to estimate GPP using remote sensing (RS) for regional and global studies. Using biophysical and climatic variables (MODIS, SBSS, ECWMF reanalysis etc.) and validated by crop statistics, the present study provides a new dataset of agricultural GPP for monsoon and winter seasons in India for two decades (2001–2019). This dataset (GPPCY-IN) is based on the light use efficiency (LUE) principle and applied a dynamic LUE for each year and season to capture the seasonal variations more efficiently. An additional dataset (NGPPCY-IN) is also derived from crop production statistics and RS GPP to translate district-level statistics at the pixel level. Along with validation with crop statistics, the derived dataset was also compared with in situ GPP estimations. This dataset will be useful for many applications and has been created for estimating integrated yield loss by taking GPP as a proxy compared to resource and time-consuming field-based methods for crop insurance. |
| format | Journal Article |
| id | CGSpace129206 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1292062025-11-06T13:08:20Z A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India Gangopadhyay, Prasun K. Shirsath, Paresh B. Dadhwal, Vinay K. Aggarwal, Pramod K. agriculture governance remote sensing data crop production The present study describes a new dataset that estimates seasonally integrated agricultural gross primary productivity (GPP). Several models are being used to estimate GPP using remote sensing (RS) for regional and global studies. Using biophysical and climatic variables (MODIS, SBSS, ECWMF reanalysis etc.) and validated by crop statistics, the present study provides a new dataset of agricultural GPP for monsoon and winter seasons in India for two decades (2001–2019). This dataset (GPPCY-IN) is based on the light use efficiency (LUE) principle and applied a dynamic LUE for each year and season to capture the seasonal variations more efficiently. An additional dataset (NGPPCY-IN) is also derived from crop production statistics and RS GPP to translate district-level statistics at the pixel level. Along with validation with crop statistics, the derived dataset was also compared with in situ GPP estimations. This dataset will be useful for many applications and has been created for estimating integrated yield loss by taking GPP as a proxy compared to resource and time-consuming field-based methods for crop insurance. 2022-11-27 2023-03-06T14:53:13Z 2023-03-06T14:53:13Z Journal Article https://hdl.handle.net/10568/129206 en Open Access application/pdf Springer Gangopadhyay, P. K., Shirsath, P. B., Dadhwal, V. K., & Aggarwal, P. K. (2022). A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01828-y |
| spellingShingle | agriculture governance remote sensing data crop production Gangopadhyay, Prasun K. Shirsath, Paresh B. Dadhwal, Vinay K. Aggarwal, Pramod K. A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India |
| title | A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India |
| title_full | A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India |
| title_fullStr | A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India |
| title_full_unstemmed | A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India |
| title_short | A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India |
| title_sort | new two decade 2001 2019 high resolution agricultural primary productivity dataset for india |
| topic | agriculture governance remote sensing data crop production |
| url | https://hdl.handle.net/10568/129206 |
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