Abstract
Accurate and long-term soil moisture data is vital for drought monitoring and agricultural planning in monsoon-dependent and irrigation-intensive regions such as India. Satellite missions like SMAP and SMOS have improved global monitoring but remain limited by short records, shallow sensing depths, and reduced accuracy under dense vegetation and irrigation. To address these gaps, we reconstructed a 0.05° daily root-zone soil moisture (RZSM, 100 cm) dataset for India covering 1981–2024. The dataset was developed using a hybrid approach that combines simulations from the calibrated H08 land surface model with SMAP RZSM through Random Forest regression. Predictors included H08-derived soil moisture and evapotranspiration, precipitation, and temperature, trained against SMAP observations for 2016–2024. Cross-validation demonstrates strong agreement with SMAP, achieving R² and NSE values above 0.90 and an RMSE of less than 0.03 m³/m³ across most regions. Comparison with available in-situ measurement yields an RMSE of 0.04 m³/m³ and a correlation coefficient of 0.94. Independent validation with Solar-Induced Chlorophyll Fluorescence further confirmed consistency with vegetation activity during drought years (2002, 2009). This high-resolution, long-term dataset provides a robust resource for analysing drought variability, calibrating hydrological models, and assessing agricultural risks in India.
Similar content being viewed by others
Data availability
The gridded (0.05°) root-zone soil moisture data for India between 1981–2024 can be accessed from the Zenodo repository (https://doi.org/10.5281/zenodo.17014507). The repository also includes a README file that describes the data structure and file formats.
Code availability
The source codes for the H08 hydrological model and the CaMa-Flood routing model are publicly accessible at http://h08.nies.go.jp and https://hydro.iis.u-tokyo.ac.jp/~yamadai/cama-flood/, respectively. The Random Forest Regressor (RFR) used in this study is implemented using the scikit-learn library, an open-source machine learning toolkit available in Python.
References
Brocca, L., Ciabatta, L., Massari, C., Camici, S. & Tarpanelli, A. Soil Moisture for Hydrological Applications: Open Questions and New Opportunities. Water 9, 140 (2017).
Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: A review. Earth Sci Rev 99, 125–161 (2010).
Aadhar, S. & Mishra, V. Data Descriptor: High-resolution near real-time drought monitoring in South Asia. Sci Data 4, 1–14 (2017).
Jain, M. et al. Groundwater depletion will reduce cropping intensity in India. Sci Adv 7 (2021).
Vangala, G. & Chandrasekar, A. Analysis of soil moisture estimates from global and regional datasets over the Indian region. Journal of Earth System Science 131, 63 (2022).
Entekhabi, D. et al. The soil moisture active passive (SMAP) mission. Proceedings of the IEEE 98, 704–716 (2010).
Kerr, Y. H. et al. The SMOS soil moisture retrieval algorithm. IEEE Transactions on Geoscience and Remote Sensing 50, 1384–1403 (2012).
Chan, S. K. et al. Assessment of the SMAP Passive Soil Moisture Product. Ieee Transactions On Geoscience And Remote Sensing 54 (2016).
Kumar, S. V. et al. Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes. Hydrol Earth Syst Sci 19, 4463–4478 (2015).
Reichle, R. H. et al. Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements. J Hydrometeorol 18, 2621–2645 (2017).
Reichle, R. H. et al. Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. J Adv Model Earth Syst 11, 3106–3130 (2019).
Felfelani, F., Pokhrel, Y., Guan, K. & Lawrence, D. M. Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model. Geophys Res Lett 45, 12,892–12,902 (2018).
Martens, B. et al. GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci Model Dev 10, 1903–1925 (2017).
Luo, X. et al. Spatio-temporal changes in global root zone soil moisture from 1981 to 2017. J Hydrol (Amst) 626, 130297 (2023).
Hersbach, H. et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999–2049 (2020).
Tian, J. et al. Predicting root zone soil moisture using observations at 2121 sites across China. Science of The Total Environment 847, 157425 (2022).
Xu, Y., Calvet, J. C. & Bonan, B. The joint assimilation of satellite observed LAI and soil moisture for the global root zone soil moisture production and its impact on land surface and ecosystem variables. Agric For Meteorol 360, 110299 (2025).
Wu, K. et al. Automated drone-borne GPR mapping of root-zone soil moisture for precision irrigation. Remote Sens Environ 333, 115110 (2026).
Kasim, A. A. et al. Remote sensing of root zone soil moisture: A review of methods and products. J Hydrol (Amst) 656, 133002 (2025).
Srivastava, S. & Dhanapriya, M. Remote Sensing Based Soil Moisture Estimation Using In-Situ Probes in Varanasi District, India. International Journal of Environment and Climate Change 15, 389–403 (2025).
Pitman, A. J. The evolution of, and revolution in, land surface schemes designed for climate models. International Journal of Climatology 23, 479–510 (2003).
He, Q., Lu, H. & Yang, K. Soil Moisture Memory of Land Surface Models Utilized in Major Reanalyses Differ Significantly From SMAP Observation. Earths Future 11, e2022EF003215 (2023).
Mishra, V. et al. Reconstruction of droughts in India using multiple land-surface models (1951–2015). Hydrol Earth Syst Sci 22, 2269–2284 (2018).
Kragh, S. J., Fensholt, R., Stisen, S. & Koch, J. The precision of satellite-based net irrigation quantification in the Indus and Ganges basins. Hydrol Earth Syst Sci 27, 2463–2478 (2023).
Han, Q. et al. Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale. Geosci Model Dev 16, 5825–5845 (2023).
Batchu, V., Nearing, G. & Gulshan, V. A Machine Learning Data Fusion Model for Soil Moisture Retrieval. https://arxiv.org/pdf/2206.09649 (2022).
Hanasaki, N. et al. An integrated model for the assessment of global water resources - Part 1: Model description and input meteorological forcing. Hydrol Earth Syst Sci 12, 1007–1025 (2008).
Hanasaki, N., Yoshikawa, S., Pokhrel, Y. & Kanae, S. A global hydrological simulation to specify the sources of water used by humans. Hydrol Earth Syst Sci 22, 789–817 (2018).
Chuphal, D. S., Kushwaha, A. P., Aadhar, S. & Mishra, V. Drought Atlas of India, 1901–2020. Sci Data 11, 1–12 (2024).
Zhang, X. & Tang, Q. Combining satellite precipitation and long-term ground observations for hydrological monitoring in China. Journal of Geophysical Research: Atmospheres 120, 6426–6443 (2015).
Teutschbein, C. & System, J. S.-H. and E. & 2013, undefined. Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions? hess.copernicus.orgC Teutschbein, J SeibertHydrology and Earth System Sciences, 2013•hess.copernicus.org 17, 5061–5077 (2013).
Chuphal, D. S. & Mishra, V. Increased hydropower but with an elevated risk of reservoir operations in India under the warming climate. iScience 26 (2023).
Yamazaki, D., Kanae, S., Kim, H. & Oki, T. A physically based description of floodplain inundation dynamics in a global river routing model. Water Resour Res 47, 4501 (2011).
Vegad, U., Pokhrel, Y. & Mishra, V. Flood risk assessment for Indian sub-continental river basins. Hydrol Earth Syst Sci 28, 1107–1126 (2024).
Chuphal, D. S. & Mishra, V. Hydrological model-based streamflow reconstruction for Indian sub-continental river basins, 1951–2021. Sci Data 10, 1–11 (2023).
Solanki, H. & Mishra, V. Machine learning based gap filling of streamflow and water level observations in India, 1961–2021. ESS Open Archive https://doi.org/10.22541/essoar.175103952.29794951/v1 (2025).
Magotra, B., Saharia, M. & Dhanya, C. T. Improved streamflow simulations in hydrologically diverse basins using physically-informed deep learning models. Hydrological Sciences Journal 70, 775–788 (2025).
Mu, Q., Zhao, M. & Running, S. W. MODIS global terrestrial evapotranspiration (ET) product (NASA MOD16A2/A3). Algorithm Theoretical Basis Document, Collection 5, 381–394 (2013).
Grogan, D. S. et al. Natural and anthropogenic drivers of the lost groundwater from the Ganga River basin. Environmental Research Letters 16, 114009 (2021).
MANABE, S. Climate and the ocean circulation: i. the atmospheric circulation and the hydrology of the earth’s surface. Mon Weather Rev 97, 739–774 (1969).
Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W. & Sitch, S. Terrestrial vegetation and water balance—hydrological evaluation of a dynamic global vegetation model. J Hydrol (Amst) 286, 249–270 (2004).
Robock, A., Vinnikov, K. Y., Schlosser, C. A., Speranskaya, N. A. & Xue, Y. Use of midlatitude soil moisture and meteorological observations to validate soil moisture simulations with biosphere and bucket models. J Clim 8, 15–35 (1995).
Deardorff, J. W. Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. J Geophys Res Oceans 83, 1889–1903 (1978).
Bhumralkar, C. M. Numerical Experiments on the Computation of Ground Surface Temperature in an Atmospheric General Circulation Model. J Appl Meteorol Climatol 14, 1246–1258 (1975).
Li, X. & Xiao, J. A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data. Remote Sensing 11, 517 (2019).
Sun, Y. et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science (1979) 358 (2017).
Gao, J. & O’Neill, B. C. Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways. Nature Communications 11, 1–12 (2020).
Nearing, G. S. et al. What Role Does Hydrological Science Play in the Age of Machine Learning? Water Resour Res 57, e2020WR028091 (2021).
Zhao, W., Sánchez, N., Lu, H. & Li, A. A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. J Hydrol (Amst) 563, 1009–1024 (2018).
Zhang, H. et al. Downscaling of AMSR-E Soil Moisture over North China Using Random Forest Regression. ISPRS International Journal of Geo-Information 11, 101 (2022).
Carranza, C., Nolet, C., Pezij, M. & van der Ploeg, M. Root zone soil moisture estimation with Random Forest. J Hydrol (Amst) 593, 125840 (2021).
Adab, H., Morbidelli, R., Saltalippi, C., Moradian, M. & Ghalhari, G. A. F. Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data. Water 12, 3223 (2020).
Simons, G., Koster, R. & Droogers, P. Hihydrosoil v2. 0-high resolution soil maps of global hydraulic properties. Future Works.[online] Available from https://www.futurewater.eu/projects/hihydrosoil (2020).
Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos, Transactions American Geophysical Union 89, 93–94 (2008).
Sulla-Menashe, D. & Friedl, M. A. User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) product. Usgs: Reston, Va, Usa 1, 18 (2018).
Pedregosa Fabianpedregosa, F. et al. Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot. Journal of Machine Learning Research 12, 2825–2830 (2011).
Breiman, L. Random forests. Mach Learn 45, 5–32 (2001).
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M. & Gräler, B. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ 2018, e5518 (2018).
Chen, P. Y., Chen, C. C., Kang, C., Liu, J. W. & Li, Y. H. Soil water content prediction across seasons using random forest based on precipitation-related data. Comput Electron Agric 230, 109802 (2025).
Xu, J., Wu, Z., Wang, C. & Jia, X. Machine unlearning: Solutions and challenges. IEEE Trans Emerg Top Comput Intell 8, 2150–2168 (2024).
Probst, P., Wright, M. N. & Boulesteix, A. L. Hyperparameters and tuning strategies for random forest. Wiley Interdiscip Rev Data Min Knowl Discov 9, e1301 (2019).
Chuphal, D. S. Development of High-Resolution Soil Moisture Product for India, 1981–2024. Zenodo https://doi.org/10.5281/zenodo.17014507 (2025).
Kushwaha, A. P. et al. Multimodel assessment of water budget in Indian sub-continental river basins. J Hydrol (Amst) 603, 126977 (2021).
Han, Q. et al. Global long term daily 1 km surface soil moisture dataset with physics informed machine learning. Sci Data 10, 1–12 (2023).
Shokati, H. et al. Random Forest-Based Soil Moisture Estimation Using Sentinel-2, Landsat-8/9, and UAV-Based Hyperspectral Data. Remote Sens (Basel) 16, 1962 (2024).
Goswami, M. M. et al. Understanding the soil water dynamics during excess and deficit rainfall conditions over the Core monsoon zone of India. https://arxiv.org/pdf/2308.15196 (2023).
Ganeshi, N. G. et al. Assessing the impact of soil moisture-temperature coupling on temperature extremes over the Indian region. NPJ Clim Atmos Sci 6 (2022).
Malik, I. & Mishra, V. Sub-seasonal to seasonal (S2S) prediction of dry and wet extremes for climate adaptation in India. Clim Serv 34, 100457 (2024).
Lawston, P. M., Santanello, J. A. & Kumar, S. V. Irrigation Signals Detected From SMAP Soil Moisture Retrievals. Geophys Res Lett 44, 11,860–11,867 (2017).
Ozdogan, M., Rodell, M., Beaudoing, H. K. & Toll, D. L. Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data. J Hydrometeorol 11, 171–184 (2010).
Leng, G. et al. Modeling the effects of irrigation on land surface fluxes and states over the conterminous United States: Sensitivity to input data and model parameters. Journal of Geophysical Research: Atmospheres 118, 9789–9803 (2013).
Acknowledgements
We acknowledge all the data-providing agencies for freely providing the datasets used in the study. All datasets can be downloaded from the respective sources upon registration. The work is supported by funding from the Major Research & Development Program (MRDP) by the Department of Science and Technology, India (Grant MRDP4356).
Author information
Authors and Affiliations
Contributions
V.M. designed the study. D.S.C. and A. performed the analysis and wrote the first draft, A.P.K. and G.V. contributed to the analysis, and V.M. finalised the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Chuphal, D.S., Abhishek, Kushwaha, A.P. et al. Development of Gridded Root-Zone Soil Moisture Product for India, 1981–2024. Sci Data (2026). https://doi.org/10.1038/s41597-026-06940-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41597-026-06940-x
