{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T17:14:13Z","timestamp":1774545253560,"version":"3.50.1"},"reference-count":95,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,28]],"date-time":"2024-04-28T00:00:00Z","timestamp":1714262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["No. 42090014"],"award-info":[{"award-number":["No. 42090014"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["No. 42271394"],"award-info":[{"award-number":["No. 42271394"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["No. CBAS2023ORP05"],"award-info":[{"award-number":["No. CBAS2023ORP05"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["No. 2020VTA0001"],"award-info":[{"award-number":["No. 2020VTA0001"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["No. G2022055010L"],"award-info":[{"award-number":["No. G2022055010L"]}]},{"name":"the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["No. 42090014"],"award-info":[{"award-number":["No. 42090014"]}]},{"name":"the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["No. 42271394"],"award-info":[{"award-number":["No. 42271394"]}]},{"name":"the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["No. CBAS2023ORP05"],"award-info":[{"award-number":["No. CBAS2023ORP05"]}]},{"name":"the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["No. 2020VTA0001"],"award-info":[{"award-number":["No. 2020VTA0001"]}]},{"name":"the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["No. G2022055010L"],"award-info":[{"award-number":["No. G2022055010L"]}]},{"name":"the Chinese Academy of Sciences President\u2019s International Fellowship Initiative","award":["No. 42090014"],"award-info":[{"award-number":["No. 42090014"]}]},{"name":"the Chinese Academy of Sciences President\u2019s International Fellowship Initiative","award":["No. 42271394"],"award-info":[{"award-number":["No. 42271394"]}]},{"name":"the Chinese Academy of Sciences President\u2019s International Fellowship Initiative","award":["No. CBAS2023ORP05"],"award-info":[{"award-number":["No. CBAS2023ORP05"]}]},{"name":"the Chinese Academy of Sciences President\u2019s International Fellowship Initiative","award":["No. 2020VTA0001"],"award-info":[{"award-number":["No. 2020VTA0001"]}]},{"name":"the Chinese Academy of Sciences President\u2019s International Fellowship Initiative","award":["No. G2022055010L"],"award-info":[{"award-number":["No. G2022055010L"]}]},{"name":"the MOST High Level Foreign Expert Program","award":["No. 42090014"],"award-info":[{"award-number":["No. 42090014"]}]},{"name":"the MOST High Level Foreign Expert Program","award":["No. 42271394"],"award-info":[{"award-number":["No. 42271394"]}]},{"name":"the MOST High Level Foreign Expert Program","award":["No. CBAS2023ORP05"],"award-info":[{"award-number":["No. CBAS2023ORP05"]}]},{"name":"the MOST High Level Foreign Expert Program","award":["No. 2020VTA0001"],"award-info":[{"award-number":["No. 2020VTA0001"]}]},{"name":"the MOST High Level Foreign Expert Program","award":["No. G2022055010L"],"award-info":[{"award-number":["No. G2022055010L"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Flash droughts tend to cause severe damage to agriculture due to their characteristics of sudden onset and rapid intensification. Early detection of the response of vegetation to flash droughts is of utmost importance in mitigating the effects of flash droughts, as it can provide a scientific basis for establishing an early warning system. The commonly used method of determining the response time of vegetation to flash drought, based on the response time index or the correlation between the precipitation anomaly and vegetation growth anomaly, leads to the late detection of irreversible drought effects on vegetation, which may not be sufficient for use in analyzing the response of vegetation to flash drought for early earning. The evapotranspiration-based (ET-based) drought indices are an effective indicator for identifying and monitoring flash drought. This study proposes a novel approach that applies cross-spectral analysis to an ET-based drought index, i.e., Evaporative Stress Anomaly Index (ESAI), as the forcing and a vegetation-based drought index, i.e., Normalized Vegetation Anomaly Index (NVAI), as the response, both from medium-resolution remote sensing data, to estimate the time lag of the response of vegetation vitality status to flash drought. An experiment on the novel method was carried out in North China during March\u2013September for the period of 2001\u20132020 using remote sensing products at 1 km spatial resolution. The results show that the average time lag of the response of vegetation to water availability during flash droughts estimated by the cross-spectral analysis over North China in 2001\u20132020 was 5.9 days, which is shorter than the results measured by the widely used response time index (26.5 days). The main difference between the phase lag from the cross-spectral analysis method and the response time from the response time index method lies in the fundamental processes behind the definitions of the vegetation response in the two methods, i.e., a subtle and dynamic fluctuation signature in the response signal (vegetation-based drought index) that correlates with the fluctuation in the forcing signal (ET-based drought index) versus an irreversible impact indicated by a negative NDVI anomaly. The time lag of the response of vegetation to flash droughts varied with vegetation types and irrigation conditions. The average time lag for rainfed cropland, irrigated cropland, grassland, and forest in North China was 5.4, 5.8, 6.1, and 6.9 days, respectively. Forests have a longer response time to flash droughts than grasses and crops due to their deeper root systems, and irrigation can mitigate the impacts of flash droughts. Our method, based on cross-spectral analysis and the ET-based drought index, is innovative and can provide an earlier warning of impending drought impacts, rather than waiting for the irreversible impacts to occur. The information detected at an earlier stage of flash droughts can help decision makers in developing more effective and timely strategies to mitigate the impact of flash droughts on ecosystems.<\/jats:p>","DOI":"10.3390\/rs16091564","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T04:26:16Z","timestamp":1714364776000},"page":"1564","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Investigating the Response of Vegetation to Flash Droughts by Using Cross-Spectral Analysis and an Evapotranspiration-Based Drought Index"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9854-1638","authenticated-orcid":false,"given":"Peng","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3108-8645","authenticated-orcid":false,"given":"Li","family":"Jia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"given":"Jing","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3510-9829","authenticated-orcid":false,"given":"Min","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Chaolei","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9176-4556","authenticated-orcid":false,"given":"Massimo","family":"Menenti","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2825 CN Delft, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1080\/02508068508686328","article-title":"Understanding: The drought phenomenon: The role of definitions","volume":"10","author":"Wilhite","year":"1985","journal-title":"Water Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1126\/science.abn6301","article-title":"A global transition to flash droughts under climate change","volume":"380","author":"Yuan","year":"2023","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"30571","DOI":"10.1038\/srep30571","article-title":"Increasing flash droughts over China during the recent global warming hiatus","volume":"6","author":"Wang","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1038\/s41467-022-28752-4","article-title":"Accelerating flash droughts induced by the joint influence of soil moisture depletion and atmospheric aridity","volume":"13","author":"Qing","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4661","DOI":"10.1038\/s41467-019-12692-7","article-title":"Anthropogenic shift towards higher risk of flash drought over China","volume":"10","author":"Yuan","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e1714","DOI":"10.1002\/wat2.1714","article-title":"Flash drought: A state of the science review","volume":"11","author":"Christian","year":"2024","journal-title":"Wires Water"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1175\/BAMS-D-17-0149.1","article-title":"FLASH DROUGHTS A Review and Assessment of the Challenges Imposed by Rapid-Onset Droughts in the United States","volume":"99","author":"Otkin","year":"2018","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1175\/JHM-D-12-0144.1","article-title":"Examining Rapid Onset Drought Development Using the Thermal Infrared-Based Evaporative Stress Index","volume":"14","author":"Otkin","year":"2013","journal-title":"J. Hydrometeorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e2022EF002723","DOI":"10.1029\/2022EF002723","article-title":"Flash Drought: Review of Concept, Prediction and the Potential for Machine Learning, Deep Learning Methods","volume":"10","author":"Tyagi","year":"2022","journal-title":"Earths Future"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"E2188","DOI":"10.1175\/BAMS-D-21-0288.1","article-title":"Getting ahead of Flash Drought: From Early Warning to Early Action","volume":"103","author":"Otkin","year":"2022","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e2022JD037152","DOI":"10.1029\/2022JD037152","article-title":"Quantifying Flash Droughts Over China From 1980 to 2017","volume":"127","author":"Fu","year":"2022","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1007\/s00382-023-06980-8","article-title":"The intensification of flash droughts across China from 1981 to 2021","volume":"62","author":"Zhang","year":"2023","journal-title":"Clim. Dyn."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1038\/s41612-023-00468-2","article-title":"Increased risk of flash droughts with raised concurrent hot and dry extremes under global warming","volume":"6","author":"Zeng","year":"2023","journal-title":"Npj Clim. Atmos. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1038\/s43247-023-00826-1","article-title":"Global projections of flash drought show increased risk in a warming climate","volume":"4","author":"Christian","year":"2023","journal-title":"Commun. Earth Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"094078","DOI":"10.1088\/1748-9326\/ab9faf","article-title":"Flash drought development and cascading impacts associated with the 2010 Russian heatwave","volume":"15","author":"Christian","year":"2020","journal-title":"Environ. Res Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"74019","DOI":"10.1088\/1748-9326\/ab22c3","article-title":"Impacts of the 2017 flash drought in the US Northern plains informed by satellite-based evapotranspiration and solar-induced fluorescence","volume":"14","author":"He","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e2023GL105375","DOI":"10.1029\/2023GL105375","article-title":"High Temperature Accelerates Onset Speed of the 2022 Unprecedented Flash Drought Over the Yangtze River Basin","volume":"50","author":"Wang","year":"2023","journal-title":"Geophys. Res. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dabrowska-Zielinska, K., Bochenek, Z., Malinska, A., Bartold, M., Gurdak, R., Lagiewska, M., and Paradowski, K. (2021, January 11\u201316). Drought Assessment Applying Joined Meteorological and Satellite Data. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553739"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dabrowska-Zielinska, K., Malinska, A., Bochenek, Z., Bartold, M., Gurdak, R., Paradowski, K., and Lagiewska, M. (2020). Drought Model DISS Based on the Fusion of Satellite and Meteorological Data under Variable Climatic Conditions. Remote Sens., 12.","DOI":"10.3390\/rs12182944"},{"key":"ref_20","unstructured":"Jia, L., Hu, G., Zhou, J., and Menenti, M. (November, January 29). Assessing the sensitivity of two new indicators of vegetation response to water availability for drought monitoring. Proceedings of the Land Surface Remote Sensing, SPIE, Kyoto, Japan."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/0273-1177(95)00079-T","article-title":"Application of Vegetation Index and Brightness Temperature for Drought Detection","volume":"15","author":"Kogan","year":"1995","journal-title":"Adv. Space Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.agrformet.2019.01.036","article-title":"The impact of the 2009\/2010 drought on vegetation growth and terrestrial carbon balance in Southwest China","volume":"269","author":"Li","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1016\/j.rse.2011.05.018","article-title":"Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems","volume":"115","author":"Caccamo","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/01431161.2015.1093190","article-title":"Agricultural drought monitoring using MODIS-based drought indices over the USA Corn Belt","volume":"36","author":"Wu","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1175\/BAMS-D-11-00213.1","article-title":"A Remotely Sensed Global Terrestrial Drought Severity Index","volume":"94","author":"Mu","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"157535","DOI":"10.1016\/j.scitotenv.2022.157535","article-title":"Microwave-based soil moisture improves estimates of vegetation response to drought in China","volume":"849","author":"Qiu","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_27","first-page":"D10117","article-title":"A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation","volume":"112","author":"Anderson","year":"2007","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.agrformet.2015.12.065","article-title":"Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought","volume":"218","author":"Otkin","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0034-4257(03)00174-3","article-title":"Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices","volume":"87","author":"Ji","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"154970","DOI":"10.1016\/j.scitotenv.2022.154970","article-title":"A multi-metric assessment of drought vulnerability across different vegetation types using high resolution remote sensing","volume":"832","author":"Chen","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1073\/pnas.1207068110","article-title":"Response of vegetation to drought time-scales across global land biomes","volume":"110","author":"Gouveia","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109409","DOI":"10.1016\/j.ecolind.2022.109409","article-title":"Drought-related cumulative and time-lag effects on vegetation dynamics across the Yellow River Basin, China","volume":"143","author":"Zhan","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107767","DOI":"10.1016\/j.ecolind.2021.107767","article-title":"Characteristics of vegetation response to drought in the CONUS based on long-term remote sensing and meteorological data","volume":"127","author":"Zhong","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"129810","DOI":"10.1016\/j.jhydrol.2023.129810","article-title":"Detecting nonlinear information about drought propagation time and rate with nonlinear dynamic system and chaos theory","volume":"623","author":"Zhao","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107544","DOI":"10.1016\/j.agwat.2022.107544","article-title":"A new multi-variable integrated framework for identifying flash drought in the Loess Plateau and Qinling Mountains regions of China","volume":"265","author":"Zhang","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e2021WR030028","DOI":"10.1029\/2021WR030028","article-title":"Investigating the Propagation From Meteorological to Hydrological Drought by Introducing the Nonlinear Dependence With Directed Information Transfer Index","volume":"57","author":"Zhou","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5579","DOI":"10.5194\/hess-24-5579-2020","article-title":"Rapid reduction in ecosystem productivity caused by flash droughts based on decade-long FLUXNET observations","volume":"24","author":"Zhang","year":"2020","journal-title":"Hydrol. Earth Syst. Sc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s13021-020-00156-1","article-title":"Remote sensing of the impact of flash drought events on terrestrial carbon dynamics over China","volume":"15","author":"Zhang","year":"2020","journal-title":"Carbon Balance Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"127402","DOI":"10.1016\/j.jhydrol.2021.127402","article-title":"Terrestrial ecosystem response to flash droughts over India","volume":"605","author":"Poonia","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"014028","DOI":"10.1088\/1748-9326\/acae3a","article-title":"Flash drought drives rapid vegetation stress in arid regions in Europe","volume":"18","author":"Sungmin","year":"2023","journal-title":"Environ. Res. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e2021WR031464","DOI":"10.1029\/2021WR031464","article-title":"Flash Droughts Identification Based on an Improved Framework and Their Contrasting Impacts on Vegetation Over the Loess Plateau, China","volume":"58","author":"Zheng","year":"2022","journal-title":"Water Resour. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"109545","DOI":"10.1016\/j.agrformet.2023.109545","article-title":"Assessing the response of vegetation photosynthesis to flash drought events based on a new identification framework","volume":"339","author":"Yang","year":"2023","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"126532","DOI":"10.1016\/j.jhydrol.2021.126532","article-title":"Time-lag effects of climatic change and drought on vegetation dynamics in an alpine river basin of the Tibet Plateau, China","volume":"600","author":"Zuo","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"110214","DOI":"10.1016\/j.jenvman.2020.110214","article-title":"Evaluating the cumulative and time-lag effects of drought on grassland vegetation: A case study in the Chinese Loess Plateau","volume":"261","author":"Zhao","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3520","DOI":"10.1111\/gcb.12945","article-title":"Time-lag effects of global vegetation responses to climate change","volume":"21","author":"Wu","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"114879","DOI":"10.1016\/j.jenvman.2022.114879","article-title":"Assessing the responses of vegetation to meteorological drought and its influencing factors with partial wavelet coherence analysis","volume":"311","author":"Zhou","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"van Hoek, M., Jia, L., Zhou, J., Zheng, C.L., and Menenti, M. (2016). Early Drought Detection by Spectral Analysis of Satellite Time Series of Precipitation and Normalized Difference Vegetation Index (NDVI). Remote Sens., 8.","DOI":"10.3390\/rs8050422"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4198","DOI":"10.2166\/wcc.2023.584","article-title":"Spatial and temporal analysis of drought resistance of different vegetation in the Ta-pieh Mountains based on multi-source data","volume":"14","author":"Li","year":"2023","journal-title":"J. Water Clim. Chang."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1038\/s41558-020-0709-0","article-title":"Flash droughts present a new challenge for subseasonal-to-seasonal prediction","volume":"10","author":"Pendergrass","year":"2020","journal-title":"Nat. Clim. Chang."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.rse.2015.11.034","article-title":"The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts","volume":"174","author":"Anderson","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e2022EF002660","DOI":"10.1029\/2022EF002660","article-title":"Global Flash Drought Analysis: Uncertainties From Indicators and Datasets","volume":"10","author":"Mukherjee","year":"2022","journal-title":"Earths Future"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.rse.2018.10.020","article-title":"Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales","volume":"219","author":"Joiner","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Li, P., Jia, L., Lu, J., Jiang, M., and Zheng, C. (2024). A New Evapotranspiration-Based Drought Index for Flash Drought Identification and Monitoring. Remote Sens., 16.","DOI":"10.3390\/rs16050780"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1016\/j.rse.2011.02.019","article-title":"Improvements to a MODIS global terrestrial evapotranspiration algorithm","volume":"115","author":"Mu","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"128444","DOI":"10.1016\/j.jhydrol.2022.128444","article-title":"Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations","volume":"613","author":"Zheng","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3056","DOI":"10.3390\/rs70303056","article-title":"Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.5194\/hess-24-1565-2020","article-title":"Can we trust remote sensing evapotranspiration products over Africa?","volume":"24","author":"Weerasinghe","year":"2020","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"100709","DOI":"10.1016\/j.ejrh.2020.100709","article-title":"Validation of seven global remotely sensed ET products across Thailand using water balance measurements and land use classifications","volume":"30","author":"Sriwongsitanon","year":"2020","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2894","DOI":"10.1038\/s41467-018-05252-y","article-title":"North China Plain threatened by deadly heatwaves due to climate change and irrigation","volume":"9","author":"Kang","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.agrformet.2019.02.017","article-title":"A numerical analysis of aggregation error in evapotranspiration estimates due to heterogeneity of soil moisture and leaf area index","volume":"269","author":"Chen","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"110182","DOI":"10.1016\/j.ecolmodel.2022.110182","article-title":"A data-driven high spatial resolution model of biomass accumulation and crop yield: Application to a fragmented desert-oasis agroecosystem","volume":"475","author":"Chen","year":"2023","journal-title":"Ecol. Model."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"101370","DOI":"10.1016\/j.ejrh.2023.101370","article-title":"Assessing impacts of climate variability and land use\/land cover change on the water balance components in the Sahel using Earth observations and hydrological modelling","volume":"47","author":"Bennour","year":"2023","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_63","first-page":"D05190","article-title":"Crop Evapotranspiration-Guidelines For Computing Crop Water Requirements","volume":"56","author":"Allen","year":"1998","journal-title":"FAO Irrig. Drain."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1038\/s41597-022-01522-z","article-title":"Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products","volume":"9","author":"Zhang","year":"2022","journal-title":"Sci. Data"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"113184","DOI":"10.1016\/j.rse.2022.113184","article-title":"IrriMap_CN: Annual irrigation maps across China in 2000-2019 based on satellite observations, environmental variables, and machine learning","volume":"280","author":"Zhang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhou, J., Jia, L., Menenti, M., and Liu, X. (2021). Optimal Estimate of Global Biome-Specific Parameter Settings to Reconstruct NDVI Time Series with the Harmonic ANalysis of Time Series (HANTS) Method. Remote Sens., 13.","DOI":"10.3390\/rs13214251"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1080\/17538947.2023.2192004","article-title":"A scalable software package for time series reconstruction of remote sensing datasets on the Google Earth Engine platform","volume":"16","author":"Zhou","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_68","first-page":"164","article-title":"Analysis of vegetation response to climate variability using extended time series of multispectral satellite images","volume":"131","author":"Menenti","year":"2010","journal-title":"Remote Sens. Opt. Obs. Veg. Prop."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1080\/014311600209814","article-title":"Reconstructing cloudfree NDVI composites using Fourier analysis of time series","volume":"21","author":"Roerink","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/0273-1177(93)90550-U","article-title":"Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images","volume":"13","author":"Menenti","year":"1993","journal-title":"Adv. Space Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1080\/01431160701355223","article-title":"Impact of rainfall anomalies on Fourier parameters of NDVI time series of northwestern Argentina","volume":"29","author":"Loyarte","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_72","first-page":"9","article-title":"Mapping isogrowth zones on continental scale using temporal Fourier analysis of AVHRR-NDVI data","volume":"1","author":"Azzali","year":"1999","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1175\/JHM-D-18-0198.1","article-title":"A Methodology for Flash Drought Identification: Application of Flash Drought Frequency across the United States","volume":"20","author":"Christian","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Percival, D.B., and Walden, A.T. (1993). Spectral Analysis for Physical Applications, Cambridge University Press.","DOI":"10.1017\/CBO9780511622762"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1109\/18.53742","article-title":"Cross Spectral-Analysis of Nonstationary Processes","volume":"36","author":"White","year":"1990","journal-title":"IEEE Trans. Inform. Theory"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"112108","DOI":"10.1016\/j.rse.2020.112108","article-title":"Characterizing vegetation response to rainfall at multiple temporal scales in the Sahel-Sudano-Guinean region using transfer function analysis","volume":"252","author":"Zhou","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"102860","DOI":"10.1016\/j.pce.2020.102860","article-title":"A comprehensive index for assessing regional dry-hot wind events in Huang-Huai-Hai Region, China","volume":"116","author":"Li","year":"2020","journal-title":"Phys. Chem. Earth"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"127224","DOI":"10.1016\/j.jhydrol.2021.127224","article-title":"Application of an improved spatio-temporal identification method of flash droughts","volume":"604","author":"Gou","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1007\/s00376-019-9061-6","article-title":"Assessment of an Evapotranspiration Deficit Drought Index in Relation to Impacts on Ecosystems","volume":"36","author":"Zhang","year":"2019","journal-title":"Adv. Atmos. Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1038\/s43247-024-01247-4","article-title":"Global ecosystem responses to flash droughts are modulated by background climate and vegetation conditions","volume":"5","author":"Sungmin","year":"2024","journal-title":"Commun. Earth Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"114947","DOI":"10.1016\/j.jenvman.2022.114947","article-title":"Response of vegetation ecosystems to flash drought with solar-induced chlorophyll fluorescence over the Hai River Basin, China during 2001\u20132019","volume":"313","author":"Yao","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"e2023WR034994","DOI":"10.1029\/2023WR034994","article-title":"Comparing Agriculture-Related Characteristics of Flash and Normal Drought Reveals Heterogeneous Crop Response","volume":"59","author":"Ho","year":"2023","journal-title":"Water Resour. Res."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"129874","DOI":"10.1016\/j.jhydrol.2023.129874","article-title":"The effects of flash drought on the terrestrial ecosystem in Korea","volume":"624","author":"Kang","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"109428","DOI":"10.1016\/j.ecolind.2022.109428","article-title":"Effects of different types of drought on vegetation in Huang-Huai-Hai River Basin, China","volume":"144","author":"Shi","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"111813","DOI":"10.1016\/j.rse.2020.111813","article-title":"Agricultural drought mitigating indices derived from the changes in drought characteristics","volume":"244","author":"Wu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1175\/JHM-D-18-0171.1","article-title":"Assessing the Evolution of Soil Moisture and Vegetation Conditions during a Flash Drought-Flash Recovery Sequence over the South-Central United States","volume":"20","author":"Otkin","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"156021","DOI":"10.1016\/j.scitotenv.2022.156021","article-title":"Drought propagation under global warming: Characteristics, approaches, processes, and controlling factors","volume":"838","author":"Zhang","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"561","DOI":"10.5194\/npg-11-561-2004","article-title":"Application of the cross wavelet transform and wavelet coherence to geophysical time series","volume":"11","author":"Grinsted","year":"2004","journal-title":"Nonlinear Process. Geophys."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Otkin, J.A., Zhong, Y.F., Hunt, E.D., Christian, J.I., Basara, J.B., Nguyen, H., Wheeler, M.C., Ford, T.W., Hoell, A., and Svoboda, M. (2021). Development of a Flash Drought Intensity Index. Atmosphere, 12.","DOI":"10.5194\/egusphere-egu21-1418"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Bartold, M., and Kluczek, M. (2023). A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands. Remote Sens., 15.","DOI":"10.3390\/rs15092392"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Jing, X., Zou, Q., Yan, J.M., Dong, Y.Y., and Li, B.Y. (2022). Remote Sensing Monitoring of Winter Wheat Stripe Rust Based on mRMR-XGBoost Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14030756"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhao, L., Wang, M.L., Wu, J.J., Zhou, W., Zhang, Q.P., and Deng, M.E. (2022). A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data. Remote Sens., 14.","DOI":"10.3390\/rs14194981"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Park, H., Kim, K., and Lee, D.K. (2019). Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data. Water, 11.","DOI":"10.3390\/w11040705"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Zhao, Y.Y., Zhang, J.H., Bai, Y., Zhang, S., Yang, S.S., Henchiri, M., Seka, A.M., and Nanzad, L. (2022). Drought Monitoring and Performance Evaluation Based on Machine Learning Fusion of Multi-Source Remote Sensing Drought Factors. Remote Sens., 14.","DOI":"10.3390\/rs14246398"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Chen, Z.X., Wang, G.J., Wei, X.K., Liu, Y., Duan, Z., Hu, Y.F., and Jiang, H.Y. (2024). Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China. Atmosphere, 15.","DOI":"10.3390\/atmos15020155"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1564\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:35:14Z","timestamp":1760106914000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1564"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,28]]},"references-count":95,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16091564"],"URL":"https:\/\/doi.org\/10.3390\/rs16091564","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,28]]}}}