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...

Descripción completa

Detalles Bibliográficos
Autores principales: Gangopadhyay, Prasun K., Shirsath, Paresh B., Dadhwal, Vinay K., Aggarwal, Pramod K.
Formato: Journal Article
Lenguaje:Inglés
Publicado: Springer 2022
Materias:
Acceso en línea:https://hdl.handle.net/10568/129206
_version_ 1855535597369688064
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
work_keys_str_mv AT gangopadhyayprasunk anewtwodecade20012019highresolutionagriculturalprimaryproductivitydatasetforindia
AT shirsathpareshb anewtwodecade20012019highresolutionagriculturalprimaryproductivitydatasetforindia
AT dadhwalvinayk anewtwodecade20012019highresolutionagriculturalprimaryproductivitydatasetforindia
AT aggarwalpramodk anewtwodecade20012019highresolutionagriculturalprimaryproductivitydatasetforindia
AT gangopadhyayprasunk newtwodecade20012019highresolutionagriculturalprimaryproductivitydatasetforindia
AT shirsathpareshb newtwodecade20012019highresolutionagriculturalprimaryproductivitydatasetforindia
AT dadhwalvinayk newtwodecade20012019highresolutionagriculturalprimaryproductivitydatasetforindia
AT aggarwalpramodk newtwodecade20012019highresolutionagriculturalprimaryproductivitydatasetforindia