Agricultural drought dynamics in China during 1982–2020: a depiction with satellite remotely sensed soil moisture

References

• AghaKouchak, A., A. Farahmand, F. S. Melton, J. Teixeira, M. C. Anderson, B. D. Wardlow, and C. R. Hain. 2015. “Remote Sensing of Drought: Progress, Challenges and Opportunities.” Reviews of Geophysics 53 (2): 452–28. https://doi.org/10.1002/2014rg000456.
   Web of Science ®Google Scholar
• Andreadis, K. M., E. A. Clark, A. W. Wood, A. F. Hamlet, and D. P. Lettenmaier. 2005. “Twentieth-Century Drought in the Conterminous United States.” Journal of Hydrometeorology 6 (6): 985–1001. https://doi.org/10.1175/jhm450.1.
   Web of Science ®Google Scholar
• A, Y., G. Wang, P. Hu, X. Lai, B. Xue, and Q. Fang. 2022. “Root-Zone Soil Moisture Estimation Based on Remote Sensing Data and Deep Learning.” Environmental Research 212:113278. https://doi.org/10.1016/j.envres.2022.113278.
   PubMed Web of Science ®Google Scholar
• Babaeian, E., M. Sadeghi, S. B. Jones, C. Montzka, H. Vereecken, and M. Tuller. 2019. “Ground, Proximal, and Satellite Remote Sensing of Soil Moisture.” Reviews of Geophysics 57 (2): 530–616. https://doi.org/10.1029/2018RG000618.
   Web of Science ®Google Scholar
• Bai, J., Q. Cui, D. Chen, H. Yu, X. Mao, L. Meng, and Y. Cai. 2018. “Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China.” Remote Sensing 10 (8): 1302. https://doi.org/10.3390/rs10081302.
   Web of Science ®Google Scholar
• Deng, X., G. Wang, H. Yan, J. Zheng, and X. Li. 2022. “Spatial–Temporal Pattern and Influencing Factors of Drought Impacts on Agriculture in China.” Frontiers in Environmental Science 10:820615. https://doi.org/10.3389/fenvs.2022.820615.
   Google Scholar
• Dorigo, W. A., A. Gruber, R. A. M. De Jeu, W. Wagner, T. Stacke, A. Loew, C. Albergel, et al. 2015. “Evaluation of the ESA CCI Soil Moisture Product Using Ground-Based Observations.” Remote Sensing of Environment 162:380–395. https://doi.org/10.1016/j.rse.2014.07.023.
   Web of Science ®Google Scholar
• Dorigo, W., W. Wagner, C. Albergel, F. Albrecht, G. Balsamo, L. Brocca, D. Chung, et al. 2017. “ESA CCI Soil Moisture for Improved Earth System Understanding: State-Of-The Art and Future Directions.” Remote Sensing of Environment 203:185–215. https://doi.org/10.1016/j.rse.2017.07.001.
   Web of Science ®Google Scholar
• Dorigo, W. A., W. Wagner, R. Hohensinn, S. Hahn, C. Paulik, A. Xaver, A. Gruber, et al. 2011. “The International Soil Moisture Network: A Data Hosting Facility for Global in situ Soil Moisture Measurements.” Hydrology and Earth System Sciences 15 (5): 1675–1698. https://doi.org/10.5194/hess-15-1675-2011.
   Web of Science ®Google Scholar
• Du, J., J. Shu, Y. Wang, Y. Li, L. Zhang, and Y. Guo. 2014. “Comparison of GIMMS and MODIS Normalized Vegetation Index Composite Data for Qing-Hai-Tibet Plateau.” Chinese Journal of Applied Ecology 25 (2): 533–544.
   Google Scholar
• Dutra, E., P. Viterbo, and P. M. A. Miranda. 2008. “ERA-40 Reanalysis Hydrological Applications in the Characterization of Regional Drought.” Geophysical Research Letters 35 (19): L19402. https://doi.org/10.1029/2008GL035381.
   Web of Science ®Google Scholar
• Eswar, R., N. N. Das, C. Poulsen, A. Behrangi, J. Swigart, M. Svoboda, D. Entekhabi, S. Yueh, B. Doorn, and J. Entin. 2018. “SMAP Soil Moisture Change as an Indicator of Drought Conditions.” Remote Sensing 10 (5): 788. https://doi.org/10.3390/rs10050788.
   Web of Science ®Google Scholar
• Fensholt, R., and S. R. Proud. 2012. “Evaluation of Earth Observation Based Global Long Term Vegetation Trends — Comparing GIMMS and MODIS Global NDVI Time Series.” Remote Sensing of Environment 119:131–147. https://doi.org/10.1016/j.rse.2011.12.015.
   Web of Science ®Google Scholar
• Gallo, K., J. Lei, B. Reed, J. Eidenshink, and J. Dwyer. 2005. “Multi-Platform Comparisons of MODIS and AVHRR Normalized Difference Vegetation Index Data.” Remote Sensing of Environment 99 (3): 221–231. https://doi.org/10.1016/j.rse.2005.08.014.
   Web of Science ®Google Scholar
• Grinsztajn, L., E. Oyallon, and G. Varoquaux. 2022. “Why Do Tree-Based Models Still Outperform Deep Learning on Tabular Data?” ArXiv abs/2207.08815. https://doi.org/10.48550/arXiv.2207.08815.
   Google Scholar
• Gruber, A., T. Scanlon, R. van der Schalie, W. Wagner, and W. Dorigo. 2019. “Evolution of the ESA CCI Soil Moisture Climate Data Records and Their Underlying Merging Methodology.” Earth System Science Data 11 (2): 717–739. https://doi.org/10.5194/essd-11-717-2019.
   Web of Science ®Google Scholar
• Hao, Z., and A. AghaKouchak. 2013. “Multivariate Standardized Drought Index: A Parametric Multi-Index Model.” Advances in Water Resources 57:12–18. https://doi.org/10.1016/j.advwatres.2013.03.009.
   Web of Science ®Google Scholar
• IPCC. 2023. “Summary for Policymakers.” In Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, edited by Lee, H. and J. Romero, 1–34. Geneva: IPCC. https://doi.org/10.59327/IPCC/AR6-9789291691647.001.
   Google Scholar
• Liu, Y. Y., R. M. Parinussa, W. A. Dorigo, R. A. M. De Jeu, W. Wagner, A. I. J. M. van Dijk, M. F. McCabe, and J. P. Evans. 2011. “Developing an Improved Soil Moisture Dataset by Blending Passive and Active Microwave Satellite-Based Retrievals.” Hydrology and Earth System Sciences 15 (2): 425–436. https://doi.org/10.5194/hess-15-425-2011.
   Web of Science ®Google Scholar
• Liu, X., X. Zhu, Y. Pan, S. Li, Y. Liu, and Y. Ma. 2016. “Agricultural Drought Monitoring: Progress, Challenges, and Prospects.” Journal of Geographical Sciences 26 (6): 750–767. https://doi.org/10.1007/s11442-016-1297-9.
   Web of Science ®Google Scholar
• Lloyd-Hughes, B. 2012. “A Spatio-Temporal Structure-Based Approach to Drought Characterisation.” International Journal of Climatology 32 (3): 406–418. https://doi.org/10.1002/joc.2280.
   Web of Science ®Google Scholar
• Martínez-Fernández, J., A. González-Zamora, N. Sánchez, A. Gumuzzio, and C. M. Herrero-Jiménez. 2016. “Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of the SMOS Derived Soil Water Deficit Index.” Remote Sensing of Environment 177:277–286. https://doi.org/10.1016/j.rse.2016.02.064.
   Web of Science ®Google Scholar
• Mishra, A., T. Vu, A. V. Veettil, and D. Entekhabi. 2017. “Drought Monitoring with Soil Moisture Active Passive (SMAP) Measurements.” Journal of Hydrology 552:620–632. https://doi.org/10.1016/j.jhydrol.2017.07.033.
   Web of Science ®Google Scholar
• Sheffield, J., G. Goteti, F. Wen, and E. F. Wood. 2004. “A Simulated Soil Moisture Based Drought Analysis for the United States.” Journal of Geophysical Research: Atmospheres 109:D24108. https://doi.org/10.1029/2004JD005182.
   Web of Science ®Google Scholar
• Song, P., Y. Zhang, P. Yao, and T. Zhao. 2021. “Daily All Weather Surface Soil Moisture Data Set with 1 Km Resolution in China (2003-2022).” National Tibetan Plateau Data Center. https://doi.org/10.11888/Hydro.tpdc.271762.
   Google Scholar
• Souza, A. G. S. S., A. R. Neto, and L. L. d. Souza. 2021. “Soil Moisture-Based Index for Agricultural Drought Assessment: SMADI Application in Pernambuco State-Brazil.” Remote Sensing of Environment 252:112124. https://doi.org/10.1016/j.rse.2020.112124.
   Web of Science ®Google Scholar
• Sun, H., C. Cai, H. Liu, and B. Yang. 2019. “Microwave and Meteorological Fusion: A Method of Spatial Downscaling of Remotely Sensed Soil Moisture.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (4): 1107–1119. https://doi.org/10.1109/JSTARS.2019.2901921.
   Web of Science ®Google Scholar
• Sun, H., and Y. Cui. 2021. “Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method.” Remote Sensing 13 (1): 133. https://doi.org/10.3390/rs13010133.
   Web of Science ®Google Scholar
• Sun, H., and J. Gao. 2023. “A Pixel-Wise Calculation of Soil Evaporative Efficiency with Thermal/Optical Remote Sensing and Meteorological Reanalysis Data for Downscaling Microwave Soil Moisture.” Agricultural Water Management 276: 108063. https://doi.org/10.1016/j.agwat.2022.108063.
   Web of Science ®Google Scholar
• Sun, H., W. H. Liu, Y. M. Wang, and S. Y. Yuan. 2017. “Evaluation of Typical Spectral Vegetation Indices for Drought Monitoring in Cropland of the North China Plain.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (12): 5404–5411. https://doi.org/10.1109/jstars.2017.2734800.
   Web of Science ®Google Scholar
• Sun, H., and X. Qian. 2021. “Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-Filling of Monthly ESA CCI Soil Moisture in China.” Remote Sensing 13 (14): 2848. https://doi.org/10.3390/rs13142848.
   Web of Science ®Google Scholar
• Sun, H., L. Wu, J. Hu, L. Ma, H. Li, and D. Wu. 2021. “Evaluating Eco-Environment in Urban Agglomeration from a Vegetation-Impervious Surface-Soil-Air Framework: An Example in Ningxia, China.” Journal of Applied Remote Sensing 15 (1): 014518. https://doi.org/10.1117/1.JRS.15.014518.
   Web of Science ®Google Scholar
• Sun, H., X. Zhang, and X. Zhao. 2022. “Series or Parallel? An Exploration in Coupling Physical Model and Machine Learning Method for Disaggregating Satellite Microwave Soil Moisture.” IEEE Transactions on Geoscience and Remote Sensing 60:4415015. https://doi.org/10.1109/TGRS.2022.3216343.
   Web of Science ®Google Scholar
• Su, Z., J. Wen, L. Dente, R. van der Velde, L. Wang, Y. Ma, K. Yang, and Z. Hu. 2011. “The Tibetan Plateau Observatory of Plateau Scale Soil Moisture and Soil Temperature (Tibet-Obs) for Quantifying Uncertainties in Coarse Resolution Satellite and Model Products.” Hydrology and Earth System Sciences 15 (7): 2303–2316. https://doi.org/10.5194/hess-15-2303-2011.
   Web of Science ®Google Scholar
• Wang, H., A. Chen, Q. Wang, and B. He. 2015. “Drought Dynamics and Impacts on Vegetation in China from 1982 to 2011.” Ecological Engineering 75:303–307. https://doi.org/10.1016/j.ecoleng.2014.11.063.
   Web of Science ®Google Scholar
• Wang, S., S. Liu, and Z. Lin. 2016. “Validation and Trend Analysis of ECV Soil Moisture Data on Cropland in North China Plain During 1981–2010.” International Journal of Applied Earth Observation and Geoinformation 48:110–121. https://doi.org/10.1016/j.jag.2015.10.010.
   Web of Science ®Google Scholar
• West, H., N. Quinn, and M. Horswell. 2019. “Remote Sensing for Drought Monitoring & Impact Assessment: Progress, Past Challenges and Future Opportunities.” Remote Sensing of Environment 232:111291. https://doi.org/10.1016/j.rse.2019.111291.
   Web of Science ®Google Scholar
• WMO. 2021. Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970-2019), WMO-No. 1267. Geneva, Switzerland: World Meteorological Organization.
   Google Scholar
• Xu, K., D. Yang, H. Yang, L. Zhe, Y. Qin, and Y. Shen. 2015. “Spatio-Temporal Variation of Drought in China During 1961–2012: A Climatic Perspective.” Journal of Hydrology 526:253–264. https://doi.org/10.1016/j.jhydrol.2014.09.047.
   Web of Science ®Google Scholar
• Yang, K., J. Qin, L. Zhao, Y. Chen, W. Tang, M. Han, Lazhu, et al. 2013. “A Multiscale Soil Moisture and Freeze–Thaw Monitoring Network on the Third Pole.” Bulletin of the American Meteorological Society 94 (12): 1907–1916. https://doi.org/10.1175/BAMS-D-12-00203.1.
   Web of Science ®Google Scholar
• Yu, M., Q. Li, M. J. Hayes, M. D. Svoboda, and R. R. Heim. 2014. “Are Droughts Becoming More Frequent or Severe in China Based on the Standardized Precipitation Evapotranspiration Index: 1951–2010?” International Journal of Climatology 34 (3): 545–558. https://doi.org/10.1002/joc.3701.
   Web of Science ®Google Scholar
• Zeng, J., R. Zhang, Y. Lin, X. Wu, J. Tang, P. Guo, J. Li, and Q. Wang. 2020. “Drought Frequency Characteristics of China, 1981-2019, Based on the Vegetation Health Index.” Climate Research 81:131–147. https://doi.org/10.3354/cr01616.
   Web of Science ®Google Scholar
• Zhang, X., N. Chen, L. Jizhen, Z. Chen, and D. Niyogi. 2017. “Multi-Sensor Integrated Framework and Index for Agricultural Drought Monitoring.” Remote Sensing of Environment 188:141–163. https://doi.org/10.1016/j.rse.2016.10.045.
   Web of Science ®Google Scholar
• Zhao, T., J. Shi, L. Liqing, X. Hongxin, D. Chen, Q. Cui, T. J. Jackson, et al. 2020. “Soil Moisture Experiment in the Luan River Supporting New Satellite Mission Opportunities.” Remote Sensing of Environment 240:111680. https://doi.org/10.1016/j.rse.2020.111680.
   Web of Science ®Google Scholar
• Zheng, J. Y., T. J. Zhao, H. S. Lu, J. C. Shi, M. H. Cosh, D. B. Ji, L. M. Jiang, et al. 2022. “Assessment of 24 Soil Moisture Datasets Using a New in situ Network in the Shandian River Basin of China.” Remote Sensing of Environment 271:112891. https://doi.org/10.1016/j.rse.2022.112891.
   Web of Science ®Google Scholar
• Zhou, L., W. Jianjun, M. Xinyu, H. Zhou, C. Diao, Q. Wang, Y. Chen, and F. Zhang. 2017. “Quantitative and Detailed Spatiotemporal Patterns of Drought in China During 2001–2013.” Science of the Total Environment 589:136–145. https://doi.org/10.1016/j.scitotenv.2017.02.202.
   PubMed Web of Science ®Google Scholar