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...

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Bibliographic Details
Published in:Scientific data 2022-11, Vol.9 (1), p.730-12, Article 730
Main Authors: Gangopadhyay, Prasun K., Shirsath, Paresh B., Dadhwal, Vinay K., Aggarwal, Pramod K.
Format: Article
Language:English
Subjects:
704/844/2739/2819
706/1143
Crop production
Crops
Data Descriptor
Datasets
Humanities and Social Sciences
Monsoons
multidisciplinary
Productivity
Remote sensing
Science
Science (multidisciplinary)
Seasonal variations
Statistics
Vegetation
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Summary: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. Measurement(s) gross primary productivity Technology Type(s) remote sensing Factor Type(s) vegetation, radiation, water Sample Characteristic - Organism vegetation Sample Characteristic - Location India
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-022-01828-y