An improved framework for mapping and assessment of dynamics in cropping pattern and crop calendar from NDVI time series across a heterogeneous agro-climatic region.
Crop calendar
Crop phenology
Crop-type mapping
Fast Fourier transform (FFT)
Lagrangian derivative
Random forest classifier
Journal
Environmental monitoring and assessment
ISSN: 1573-2959
Titre abrégé: Environ Monit Assess
Pays: Netherlands
ID NLM: 8508350
Informations de publication
Date de publication:
31 Oct 2024
31 Oct 2024
Historique:
received:
10
08
2024
accepted:
16
10
2024
medline:
1
11
2024
pubmed:
1
11
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
The absence of spatial and temporal cropping information in semi-arid regions poses a significant challenge in assessing the dynamics of agricultural systems at river basin scales. Satellite remote sensing provides qualitative and quantitative information to derive vegetation dynamics over extensive areas of inherent complexities due to limitations in the availability of field data and the diverse nature of agricultural cropping practices. Utilizing phenological information derived from MODIS Normalized Difference Vegetation Index (NDVI) time series data from 2003-2004 to 2021-2022, this study derives major crop types, and cropping calendar (sowing, maturity, and harvest dates) for each season and year at 250-m resolution. This study introduces an integrated function-fitting model based on the Fast Fourier Transform (FFT) and a Lagrangian 3-point derivative-based approach to extract phenological events from the NDVI time series automatically. The derived phenological information is used in a hybrid crop classification algorithm that combines a rule-based decision tree approach (using the phenological events/dates) and a random forest classifier (using the phenological metrics) to generate the classified crop map at a river basin scale for multiple seasons and years. The implemented approach successfully captured sowing and harvesting dates for every crop growing season over 19 years, with an RMSE of 9 days, as observed with the available field survey data from 2015 to 2021. The classification results from this hybrid approach demonstrate an overall 82% accuracy. The proposed method shows a substantial improvement in cropping area estimation with a 60% reduction in MAE and RMSE compared to the existing algorithm. From the long time series of derived seasonal crop data (19-year analysis period), the influence of monsoonal activities and shifts on the spatial and temporal dynamics of sowing time and cropping patterns are assessed for a large river basin, demonstrating the utility of this continuous crop calendar. Further, an extensive analysis of results highlights farmers' adaptive strategies in response to dry and wet years, providing foundational insights for future studies on assessing the impacts of climate change. Hence, the hybrid framework adopted in this study for deriving a continuous crop calendar holds immense relevance for parameterizing and utilizing such information for developing river basin scale hydrologic and crop growth models for water resources planning and assessment.
Identifiants
pubmed: 39480627
doi: 10.1007/s10661-024-13270-1
pii: 10.1007/s10661-024-13270-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1141Subventions
Organisme : National Hydrology Project (NHP)
ID : 8725-IN
Organisme : National Hydrology Project (NHP)
ID : 8725-IN
Organisme : National Hydrology Project (NHP)
ID : 8725-IN
Organisme : National Hydrology Project (NHP)
ID : 8725-IN
Organisme : National Hydrology Project (NHP)
ID : 8725-IN
Organisme : National Hydrology Project (NHP)
ID : 8725-IN
Organisme : Indian Council of Agricultural Research
ID : 2-11(21)/18-19NICRA
Organisme : Indian Council of Agricultural Research
ID : 2-11(21)/18-19NICRA
Organisme : Indian Council of Agricultural Research
ID : 2-11(21)/18-19NICRA
Organisme : Indian Council of Agricultural Research
ID : 2-11(21)/18-19NICRA
Organisme : Indian Council of Agricultural Research
ID : 2-11(21)/18-19NICRA
Organisme : Indian Council of Agricultural Research
ID : 2-11(21)/18-19NICRA
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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