ArticleScientific data2022
A new two-decade (2001-2019) high-resolution agricultural primary productivity dataset for India.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A harmonised gridded input dataset for simulation modelling of the major crops of South Asia.Data in brief · 2026Article
- A high-resolution satellite-based solar-induced chlorophyll fluorescence dataset for China from 2000 to 2022.Scientific data · 2024Article
- A new two-decade (2001-2019) high-resolution agricultural primary productivity dataset for India.Scientific data · 2022Article
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Authors and funding
4 authors.
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Abstract
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.
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Registered trials
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