{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T06:23:33Z","timestamp":1772605413919,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,13]],"date-time":"2021-02-13T00:00:00Z","timestamp":1613174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Fund of Chinese Academy of Meteorological Sciences","award":["2019Z010"],"award-info":[{"award-number":["2019Z010"]}]},{"name":"National Key Research and Development Programs of China","award":["2019YFC1510205"],"award-info":[{"award-number":["2019YFC1510205"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Massive desert locust swarms have been threatening and devouring natural vegetation and agricultural crops in East Africa and West Asia since 2019, and the event developed into a rare and globally concerning locust upsurge in early 2020. The breeding, maturation, concentration and migration of locusts rely on appropriate environmental factors, mainly precipitation, temperature, vegetation coverage and land-surface soil moisture. Remotely sensed images and long-term meteorological observations across the desert locust invasion area were analyzed to explore the complex drivers, vegetation losses and growing trends during the locust upsurge in this study. The results revealed that (1) the intense precipitation events in the Arabian Peninsula during 2018 provided suitable soil moisture and lush vegetation, thus promoting locust breeding, multiplication and gregarization; (2) the regions affected by the heavy rainfall in 2019 shifted from the Arabian Peninsula to West Asia and Northeast Africa, thus driving the vast locust swarms migrating into those regions and causing enormous vegetation loss; (3) the soil moisture and NDVI anomalies corresponded well with the locust swarm movements; and (4) there was a low chance the eastwardly migrating locust swarms would fly into the Indochina Peninsula and Southwest China.<\/jats:p>","DOI":"10.3390\/rs13040680","type":"journal-article","created":{"date-parts":[[2021,2,14]],"date-time":"2021-02-14T05:54:49Z","timestamp":1613282089000},"page":"680","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss"],"prefix":"10.3390","volume":"13","author":[{"given":"Lei","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}]},{"given":"Wen","family":"Zhuo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}]},{"given":"Zhifang","family":"Pei","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Xingyuan","family":"Tong","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1966-446X","authenticated-orcid":false,"given":"Wei","family":"Han","sequence":"additional","affiliation":[{"name":"National Meteorological Center of China, Beijing 100081, China"},{"name":"Numerical Weather Prediction Center of Chinese Meteorological Administration, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-1360","authenticated-orcid":false,"given":"Shibo","family":"Fang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China"},{"name":"Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.earscirev.2013.07.008","article-title":"Climate hazards in drylands: A review","volume":"126","author":"Middleton","year":"2013","journal-title":"Earth-Sci. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1146\/annurev-ento-011118-112500","article-title":"Locust and Grasshopper Management","volume":"64","author":"Zhang","year":"2019","journal-title":"Ann. Rev. Entomol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1111\/j.1570-7458.2006.00517.x","article-title":"Preventing desert locust plagues: Optimizing management interventions","volume":"122","author":"Arnold","year":"2007","journal-title":"Entomol. Experiment. Appl."},{"key":"ref_4","unstructured":"Madeleine, S. (2021, January 01). A Plague of Locusts has Descendedon East Africa. Climate Change May Be to Blame. Available online: https:\/\/www.nationalgeographic.com\/science\/2020\/02\/locust-plague-climate-science-east-africa\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3354\/cr00744","article-title":"Large-scale climatic patterns forcing desert locust upsurges in West Africa","volume":"37","author":"Vallebona","year":"2008","journal-title":"Clim. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1016\/j.scitotenv.2019.02.439","article-title":"Future climate change likely to reduce the Australian plague locust (Chortoicetes terminifera) seasonal outbreaks","volume":"668","author":"Wang","year":"2019","journal-title":"Sci. Total. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"14521","DOI":"10.1073\/pnas.1100189108","article-title":"Reconstruction of a 1,910-y-long locust series reveals consistent associations with climate fluctuations in China","volume":"108","author":"Tian","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3753","DOI":"10.1111\/gcb.15137","article-title":"On the relative role of climate change and management in the current desert locust outbreak in East Africa","volume":"26","author":"Meynard","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3354\/cr00930","article-title":"Desert locust populations, rainfall and climate change: Insights from phenomenological models using gridded monthly data","volume":"43","author":"Tratalos","year":"2010","journal-title":"Clim. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1890\/14-0183.1","article-title":"Modeling spatiotemporal dynamics of outbreaking species: Influence of environment and migration in a locust","volume":"96","author":"Veran","year":"2015","journal-title":"Ecology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1175\/2010JAMC2281.1","article-title":"Evaluating Detection Skills of Satellite Rainfall Estimates over Desert Locust Recession Regions","volume":"49","author":"Dinku","year":"2010","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_12","first-page":"036011","article-title":"Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture","volume":"12","author":"Salvador","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_13","first-page":"140","article-title":"SMOS based high resolution soil moisture estimates for desert locust preventive management","volume":"11","author":"Escorihuela","year":"2018","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_14","first-page":"966","article-title":"Soil moisture from remote sensing to forecast desert locust presence","volume":"45","author":"Piou","year":"2018","journal-title":"J. Appl. Ecol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"075099","DOI":"10.1117\/1.JRS.7.075099","article-title":"Locusts and remote sensing: A review","volume":"7","author":"Latchininsky","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1080\/01431168608948956","article-title":"Assessment of ecological conditions associated with the 1980\/81 desert locust plague upsurge in West Africa using environmental satellite data","volume":"7","author":"Hielkema","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4739","DOI":"10.1111\/gcb.13739","article-title":"Climate-driven geographic distribution of the desert locust during recession periods: Subspecies\u2019 niche differentiation and relative risks under scenarios of climate change","volume":"23","author":"Meynard","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_18","unstructured":"Symmons, P.M., and Cressman, K. (2001). Desert Locust Guidelines, Food Agriculture Organization of the United Nations."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shroder, J.F., and Sivanpillai, R. (2015). Biological and Environmental Hazards, Risks, and Disasters, Elsevier. Chapter 4.","DOI":"10.1016\/B978-0-12-394847-2.00001-2"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1038\/219446a0","article-title":"Environmental and Behavioural Processes in a Desert Locust Outbreak","volume":"219","author":"Roffey","year":"1968","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0065-2806(08)36001-9","article-title":"Locust Phase Polyphenism: An Update","volume":"36","author":"Pener","year":"2009","journal-title":"Adv. Insect Physiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.palaeo.2009.05.001","article-title":"Palynological evidence of climate change and land degradation in the Lake Baringo area, Kenya, East Africa, since AD 1650","volume":"279","author":"Kiage","year":"2009","journal-title":"Palaeogeogr. Palaeoclim. Palaeoecol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s00704-013-0860-x","article-title":"GPCC\u2019s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle","volume":"115","author":"Schneider","year":"2014","journal-title":"Theor. Appl. Climatol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Becker, A., Finger, P., Meyer-Christoffer, A., and Rudolf, B. (2013). A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901 Cpresent. Earth Syst. Sci. Data Discuss., 5.","DOI":"10.5194\/essdd-5-921-2012"},{"key":"ref_25","unstructured":"Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., and Ziese, M. (2019, April 18). GPCC Full Data Monthly Product Version 2018 at 0.25\u00b0: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historical Data. Available online: https:\/\/www.dante-project.org\/datasetPages\/gpcc."},{"key":"ref_26","unstructured":"Ziese, M., Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., and Schneider, U. (2019, April 18). GPCC First Guess Product at 1.0\u00b0: Near Real-Time First Guess monthly Land-Surface Precipitation from Rain-Gauges based on SYNOP Data. Available online: https:\/\/opendata.dwd.de\/climate_environment\/GPCC\/html\/download_gate.html."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Munoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Schepers, D. (2020). The ERA5 global reanalysis. Q. J. R. Meteorol. Soc., 1\u201351.","DOI":"10.1002\/qj.3803"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.rse.2018.02.065","article-title":"Spatially enhanced passive microwave derived soil moisture: Capabilities and opportunities","volume":"209","author":"Sabaghya","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Li, X., Pearson, S., Wu, D., Sun, R., Johnson, S., Wheeler, J., and Fang, S. (2019). Evaluation of Fengyun-3C soil moisture products using in-Situ data from the Chinese Automatic Soil moisture Observation Stations: A case study in Henan Province, China. Water, 11.","DOI":"10.3390\/w11020248"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, L., Fang, S., Pei, Z., Zhu, Y., Dao Nguyen, K., and Han, W. (2020). Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture. Remote Sens., 12.","DOI":"10.3390\/rs12061038"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"16226","DOI":"10.3390\/rs71215825","article-title":"Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition","volume":"7","author":"Brown","year":"2015","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hayakawa, Y.S., Oguchi, T., and Zhou, L. (2008). Comparison of new and existing global digital elevation models: ASTER G-DEM and SRTM-3. Geophys. Res. Lett., 35.","DOI":"10.1029\/2008GL035036"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Danielson, J., and Gesch, D. (2011). Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010), U.S. Geological Survey Open-File Report 2011-1073.","DOI":"10.3133\/ofr20111073"},{"key":"ref_34","unstructured":"Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., and Hanson, C.E. (2007). Food, fibre and forest products. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/680\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:23:48Z","timestamp":1760160228000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/680"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,13]]},"references-count":34,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13040680"],"URL":"https:\/\/doi.org\/10.3390\/rs13040680","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,13]]}}}