Bihao Gao
College of Water Conservancy, Shenyang Agricultural University, Shenyang 110161, China
Songshi Zhao
College of Water Conservancy, Shenyang Agricultural University, Shenyang 110161, China
Xiaolei Yang
Architectural Engineering Institute, City Institute, Dalian University of Technology, Dalian 116600, China
Wei Xu
College of Water Conservancy, Shenyang Agricultural University, Shenyang 110161, China
Dali Guo
College of Water Conservancy, Shenyang Agricultural University, Shenyang 110161, China
Hongyu Zhang
College of Water Conservancy, Shenyang Agricultural University, Shenyang 110161, China
Maosen Lin
College of Water Conservancy, Shenyang Agricultural University, Shenyang 110161, China
The low spatial resolution of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), a mainstream Global Precipitation Measurement (GPM) product, limits its use in refined drought analysis over complex monsoon underlying surfaces. To resolve this issue, this study proposes an integrated meteorological drought monitoring framework (SPTI), which enhances accuracy by coupling 0.05° downscaled IMERG precipitation data with Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data. First, a Normalized Difference Vegetation Index (NDVI)-topography guided multivariate regression kriging downscaling scheme was built using monthly precipitation data from 23 Liaoning stations (2010–2018), geographic-topographic factors, and NDVI covariates, downscaling original 0.1° IMERG data to 0.05° (achieving R2 > 0.7 in ten months, except for June and July). Second, a high-performance multi-source drought model was established via regression of Standardized Precipitation Evapotranspiration Index (SPEI) against downscaled IMERG-Z Index (derived from IMERG precipitation using the drought Z-index method) and Temperature Condition Index (TCI). Finally, SPTI was validated with four typical Liaoning drought events during 2014, 2015, 2017, and 2018. Results show that: (1) 0.05° precipitation data captures fine spatial details, clearly depicting Liaoning’s southeast-to-northwest precipitation gradient and central plain rainfall zones; (2) SPTI outperforms standalone IMERG-Z—it accurately identifies severe/extreme droughts, mitigates IMERG-Z’s underestimation bias, and reasonably characterizes drought alleviation after heavy rains by integrating TCI, avoiding IMERG-Z’s "abrupt drought-to-waterlogging" misjudgment; (3) SPTI results align well with the Liaoning Meteorological Disaster Bulletin, confirming its suitability for refined monsoon drought monitoring.
[1] Diallo, H.A., 2008. United Nations Convention to Combat Desertification (UNCCD). In Proceedings of The Future of Drylands: International Scientific Conference on Desertification and Drylands Research, Tunis, Tunisia, 19–21 June 2006.
[2] Azam, M., Hunjra, A.I., Bouri, E., et al., 2021. Impact of institutional quality on sustainable development: Evidence from developing countries. Journal of Environmental Management. 298, 113465.
[3] Balti, H., Ben Abbes, A., Mellouli, N., et al., 2020. A review of drought monitoring with big data: Issues, methods, challenges and research directions. Ecological Informatics. 60, 101136.
[4] Allan, R.P., Barlow, M., Byrne, M.P., et al., 2020. Advances in understanding large-scale responses of the water cycle to climate change. Annals of the New York Academy of Sciences. 1472(1), 49–75.
[5] Pal, B.D., Kapoor, S., Saroj, S., et al., 2020. Impact of Laser Land Levelling on Food Production and Farmers’ Income: Evidence from Drought-Prone Semi-Arid Tropics in India. International Food Policy Research Institute: Washington, DC, USA.
[6] Shukla, S., Arsenault, K.R., Hazra, A., et al., 2020. Improving early warning of drought-driven food insecurity in southern Africa using operational hydrological monitoring and forecasting products. Natural Hazards and Earth System Sciences. 20(4), 1187–1201.
[7] Wang, T., She, D., Bao, Z., et al., 2025. Elucidating diverse population exposure to compound drought and heatwave events from two meteorological drought indices (SPI and SPEI). Environmental Research Letters. 20(3), 034027.
[8] Zhang, J., Ding, J., Wang, J., et al., 2023. Remote sensing drought factor integration based on machine learning can improve the estimation of drought in arid and semi-arid regions. Theoretical and Applied Climatology. 151(3), 1753–1770.
[9] Citakoglu, H., Coşkun, Ö., 2022. Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey. Environmental Science and Pollution Research. 29(50), 75487–75511.
[10] Rhee, J., Im, J., Carbone, G.J., 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment. 114(12), 2875–2887.
[11] Wei, W., Zhang, J., Zhou, J., et al., 2021. Monitoring drought dynamics in China using Optimized Meteorological Drought Index (OMDI) based on remote sensing data sets. Journal of Environmental Management. 292, 112733.
[12] Liu, Q., Zhang, S., Zhang, H., et al., 2020. Monitoring drought using composite drought indices based on remote sensing. Science of the Total Environment. 711, 134585.
[13] Zhao, Y., Zhang, J., Bai, Y., et al., 2022. Drought monitoring and performance evaluation based on machine learning fusion of multi-source remote sensing drought factors. Remote Sensing. 14(24), 6398.
[14] Danandeh Mehr, A., Rikhtehgar Ghiasi, A., Yaseen, Z.M., et al., 2023. A novel intelligent deep learning predictive model for meteorological drought forecasting. Journal of Ambient Intelligence and Humanized Computing. 14(8), 10441–10555.
[15] Yang, R., Hu, J., Li, Z., et al., 2024. Interpretable machine learning for weather and climate prediction: A review. Atmospheric Environment. 338, 120797.
[16] Zhang, Q., Shi, R., Xu, C.-Y., et al., 2022. Multisource data-based integrated drought monitoring index: Model development and application. Journal of Hydrology. 615, 128644.
[17] Blanka-Végi, V., Tobak, Z., Sipos, G., et al., 2025. Estimation of the spatiotemporal variability of surface soil moisture using machine learning methods integrating satellite and ground-based soil moisture and environmental data. Water Resources Management. 39(5), 2317–2334.
[18] Choursi, S.K., Erfanian, M., Abghari, H., et al., 2024. Enhancing drought monitoring through spatial downscaling: A geographically weighted regression approach using TRMM 3B43 precipitation in the Urmia Lake Basin. Earth Science Informatics. 17(4), 2995–3020.
[19] Huang, F., Sun, Y., Xia, J., et al., 2024. Progress on the GNSS-R product from Fengyun-3 missions. In Proceedings of the 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2024), Athens, Greece, 7–12 July 2024.
[20] Huffman, G.J., Bolvin, D.T., Braithwaite, D., et al., 2015. NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD). National Aeronautics and Space Administration: Washington, DC, USA Available from: https://gpm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V4.5.pdf
[21] Ji, P., Wang, Q., 2024. Evaluation of the effectiveness of downscaled precipitation data in drought monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18, 1039–1053.
[22] Yu, C., Shao, H., Hu, D., et al., 2023. Merging precipitation scheme design for improving the accuracy of regional precipitation products by machine learning and geographical deviation correction. Journal of Hydrology. 620, 129560.
[23] Wang, Q., Zhao, L., Wang, M., et al., 2022. A random forest model for drought monitoring and validation for grassland drought based on multi-source remote sensing data. Remote Sensing. 14(19), 4981.
[24] Sun, Y., Deng, K., Ren, K., et al., 2024. Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review. ISPRS Journal of Photogrammetry and Remote Sensing. 208, 14–38.
[25] Mukherjee, R., Liu, D., 2021. Downscaling MODIS spectral bands using deep learning. GIScience & Remote Sensing. 58(8), 1300–1315.
[26] Sun, X.N., Wang, J.C., Zhang, L.W., et al., 2022. Spatial downscaling model combined with the geographically weighted regression and multifractal models for monthly GPM/IMERG precipitation in Hubei Province, China. Atmosphere. 13(3), 476.
[27] Alsafadi, K., Bi, S.B., Bashir, B., et al., 2023. High-resolution precipitation modeling in complex terrains using hybrid interpolation techniques incorporating physiographic and MODIS cloud cover influences. Remote Sensing. 15(9), 2435.
[28] Cao, Y., Lu, J., Li, L., 2021. Analysis of multi-scale drought and flood characteristics in Liaoning Province based on SPEI. Journal of China Institute of Water Resources and Hydropower Research. 19, 210–220. (in Chinese)
[29] Zhu, M., Liu, S., Xia, Z., et al., 2020. Crop growth stage GPP-driven spectral model for evaluation of cultivated land quality using GA-BPNN. Agriculture. 10(8), 318.
[30] Ovando, G., De La Casa, A., Diaz, G., et al., 2025. Evaluating spectral indices from MODIS to predict maize and soybean regional yields. Advances in Space Research. 76(3), 1492–1506.
[31] Lin, M., Liu, Y., Xu, W., et al., 2025. The effects of natural and social factors on surface temperature in a typical cold-region city of the northern temperate zone: A case study of Changchun, China. Sustainability. 17(15), 6840.
[32] Yi, F.L., Cui, R.W., Li, N.Y., et al., 2025. Effects of natural and social factors on the surface temperature of warm and cold seasons in a typical city of northwest China. International Journal of Environmental Science and Technology. 22(12), 11413–11426.
[33] Patil, J., Maithani, S., Sharma, S.K., 2024. Enhancing spatial resolution of Landsat-derived land surface temperature: A novel downscaling approach using an extreme learning machine. Journal of Earth System Science. 134(1), 0003.
[34] Wu, H., Hayes, M.J., Weiss, A., et al., 2001. An evaluation of the standardized precipitation index, the China-Z Index and the statistical Z-score. International Journal of Climatology. 21(6), 745–758.
[35] Jain, V.K., Pandey, R.P., Jain, M.K., et al., 2015. Comparison of drought indices for appraisal of drought characteristics in the Ken River Basin. Weather and Climate Extremes. 8, 1–11.
[36] Yuan, W.-P., Zhou, G.-S., 2004. Comparison between standardized precipitation index and Z-index in China. Chinese Journal of Plant Ecology. 28(4), 523–529. (in Chinese)
[37] Karl, T.R., 1986. The sensitivity of the Palmer Drought Severity Index and Palmer’s Z-index to their calibration coefficients including potential evapotranspiration. Journal of Climate and Applied Meteorology. 25, 77–86.
[38] Singh, U., Agarwal, P., Sharma, P.K., 2022. Meteorological drought analysis with different indices for the Betwa River Basin, India. Theoretical and Applied Climatology. 148(3), 1741–1754.
[39] McKee, T.B., Doesken, N.J., Kleist, J., 1993. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, Anaheim, CA, USA, 17–22 January 1993.
[40] Zhang, Y., Li, Z., 2020. Uncertainty analysis of standardized precipitation index due to the effects of probability distributions and parameter errors. Frontiers in Earth Science. 8, 76.
[41] Pande, C.B., Al-Ansari, N., Kushwaha, N.L., et al., 2022. Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree. Land. 11, 2040. DOI: https://doi.org/10.3390/land11112040
[42] Xia, H., Sha, Y., Zhao, X., et al., 2024. HSPEI: A 1-km spatial resolution SPEI dataset across the Chinese mainland from 2001 to 2022. Geoscience Data Journal. 11(4), 479–494.
[43] Tao, H., Mao, W., Huang, J., et al., 2014. Drought and wetness variability in the Tarim River Basin and possible associations with large-scale circulation. Advances in Water Science. 25(1), 45–52. (in Chinese)
[44] Wan, L., Bento, V.A., Qu, Y., et al., 2023. Drought characteristics and dominant factors across China: Insights from high-resolution daily SPEI dataset between 1979 and 2018. Science of The Total Environment. 901, 166362.
[45] Shi, B., Zhu, X., Hu, Y., et al., 2017. Drought characteristics of Henan Province during 1961–2013 based on the standardized precipitation evapotranspiration index. Journal of Geographical Sciences. 27(3), 311–325.
[46] Kogan, F.N., 1995. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research. 15(11), 91–100.
[47] Zhang, L., Xin, Z., Zhang, C., et al., 2022. Exploring the potential of satellite precipitation after bias correction in streamflow simulation in a semi-arid watershed in northeastern China. Journal of Hydrology: Regional Studies. 43, 101192.
[48] Du, Y., Lin, Z., Zhuang, S., et al., 2023. Spatial downscaling of GPM satellite precipitation products in the Yangtze River Basin, China. Remote Sensing Technology and Application. 38(3), 697–707. (in Chinese)
[49] Tu, L.H., Duan, L.M., 2024. Spatial downscaling analysis of GPM IMERG precipitation dataset based on multiscale geographically weighted regression model: A case study of the Inner Mongolia Reach of the Yellow River Basin. Frontiers in Environmental Science. 12, 1389587.
[50] Baig, M.H.A., Abid, M., Khan, M.R., et al., 2020. Assessing meteorological and agricultural drought in the Chitral Kabul River Basin using multiple drought indices. Remote Sensing. 12(9), 1417.
[51] Zhang, S.J., Wang, P., Wang, D.N., et al., 2022. Identification and risk characteristics of agricultural drought disaster events based on the Copula function in Northeast China. Atmosphere. 13(8), 1234.
[52] Liu, X.T., Cai, L., Li, M.Y., et al., 2024. Why does afforestation policy lead to a drying trend in soil moisture on the Loess Plateau? Science of the Total Environment. 953, 175912.
[53] Brocca, L., Gaona, J., Bavera, D., et al., 2024. Exploring the actual spatial resolution of 1 km satellite soil moisture products. Science of the Total Environment. 945, 174087.
Copyright © 2025 Bihao Gao, Songshi Zhao, Xiaolei Yang, Wei Xu, Dali Guo, Hongyu Zhang, Maosen Lin
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
