Gupta, K and Ghale, B and Mamgain, S and Roy, Arijit and Bhati, S and Anurose, T J and Jayakumar, A and Danodia, A and Karnatak, H C and Kumar, Pramod and Singh, Raghavendra Pratap and Gumma, M K and Thenkabail, P S (2026) A New Remote Sensing-Derived High Resolution Eco-Physiological Land Use Land Cover (HR Eco-P LULC) Product in Support of Regional Climate Modeling over the Indian Landscape. Remote Sensing Applications: Society and Environment. ISSN 2352-9385 (In Press)
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Multiple global land use and land cover (LULC) datasets have been produced by different scientific organizations, but their classification schemes often do not align with the specific requirements of land surface models (LSMs) embedded within Regional Climate Models (RCMs). These datasets frequently miss regional variability and typically lack sufficient detail on the biophysical and ecological characteristics of vegetation. This issue is particularly significant in ecologically diverse regions like the Indian subcontinent, where distinct Plant Functional Types (PFTs) influence land-atmosphere-soil interactions. This study addresses these limitations by developing a ‘High Resolution Eco-Physiological LULC (HR Eco-P LULC)’ product at 50m spatial resolution to better represent the ecological, physiological, and morphological characteristics of the Indian land surface types. The dataset is generated through synthesis of six distinct and well established Remote Sensing products such as the Advanced Wide Field Sensor (AWiFS) LULC 250K, LULC 50K, Wasteland Map 50K, crop type maps, vegetation type, and elevation data, resulting in HR Eco-P LULC with an overall accuracy of 91.15% and a kappa coefficient of 0.89. To ensure compatibility with LSMs and facilitate direct comparison, HR Eco-P LULC was configured to represent sub-grid land surface heterogeneity using fractional layers, following the format used in the LSMs, where the ESA Climate Change Initiative (CCI) LULC has been traditionally employed. When evaluated against CCI LULC, HR Eco-P fractional layers revealed substantial spatial mismatches in the representation of PFTs and non-vegetation categories such as urban areas and bare soil, highlighting its improved ecological specificity and relevance for regional applications. The performance of the new dataset was evaluated using the high-resolution Delhi Model with Chemistry and aerosol framework (DM-Chem) over Delhi and Bhubaneswar, showing improved simulation of near-surface weather parameters with comparatively lower RMSE, particularly over Bhubaneswar. This versatility makes the product suitable for diverse applications such as agro-ecosystem modeling, land degradation assessment, climate projections, weather forecasting, and disaster management. It provides a valuable tool for researchers and policymakers to better understand land dynamics and support informed decision-making.
| Item Type: | Article |
|---|---|
| Divisions: | Global Research Program - Resilient Farm and Food Systems |
| CRP: | UNSPECIFIED |
| Uncontrolled Keywords: | regional climate modeling, High Resolution Eco-Physiological Land Use Land Cover (HR Eco-P LULC), remote sensing, land use and land cover |
| Subjects: | Others > Remote Sensing Others > India |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 23 Mar 2026 11:01 |
| Last Modified: | 23 Mar 2026 11:01 |
| URI: | http://oar.icrisat.org/id/eprint/13554 |
| Official URL: | https://www.sciencedirect.com/science/article/abs/... |
| Projects: | UNSPECIFIED |
| Funders: | UNSPECIFIED |
| Acknowledgement: | The authors express their gratitude to National Remote Sensing Centre (NRSC) for sharing AWiFS LULC 250k, LULC 50k, and Wasteland 50k data. The authors also thank Indian Institute of Remote Sensing (IIRS) for providing the necessary facilities to accomplish this work.... |
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