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Classification

444 Water Resources - 544.1 Copper - 912.2 Management - 951 Materials Science

Type

Abstract:　Remote　sensing　technology　has　the　unique　advantages　of　real-time,　fast　and　spatio-temporal　continuity　and　wide　coverage　scale.　Under　the　background　of　global　climate　deterioration,　drought　monitoring　methods　based　on　remote　sensing　can　provide　more　real-time,　accurate,　and　stable　drought　information　and　better　assist　scientific　decision　making　than　traditional　ground　monitoring　methods.　(Methods)　Most　of　the　existing　drought　monitoring　methods　based　on　remote　sensing　rely　on　global　mathematical　models　that　assume　the　spatial　stability　of　drought　events;　hence,　an　accurate　representation　of　local　difference　characteristics　is　difficult　to　achieve.　In　the　current　study,　a　geographic　weighted　regression　(GWR)　model　is　proposed　to　optimize　the　traditional　global　drought　monitoring　model　by　considering　the　spatial　non-stationary　characteristics　of　drought　events　and　synthesizing　various　remote　sensing　drought　indices.　(Results)　This　study,　which　was　conducted　in　mainland　United　States,　focused　on　drought　monitoring　over　a　ten-year　period　(2002–2011).　The　results　indicate　that　the　GWR　model　can　provide　the　best　model　parameters　for　the　local　estimation　of　spatial　variations.　Moreover,　the　monitoring　results　are　consistent　with　the　standard　verification　data　of　the　United　States　Drought　Monitor.　The　highest　correlation　coefficient　R　between　the　GWR　model　and　the　measured　data　is　0.8552.　The　RMSE　is　0.972,　which　is　significantly　superior　to　other　remote　sensing　drought　monitoring　models.　(Conclusion)　The　GWR　model　has　the　advantage　of　spatial　non-stationary　detection　and　can　realize　local　fine　detection　in　drought　modeling.　Moreover,　the　GWR　model　can　significantly　improve　the　precision　of　remote　sensing　drought　monitoring　and　thus　has　a　good　application　prospect.　Key　words:　drought　monitoring,　remote　sensing,　geographically　weighted　regression,　spatial　non-stationarity,　local　optimizing,　CUS　Supported　by　National　Key　R&D　Program　on　Monitoring,　Early　Warning　and　Prevention　of　Major　National　Disaster　(No.　2017YFC1502802);　National　Natural　Science　Foundation　of　China　(No.　41501394,　41571330);　Youth　Innovation　Promotion　Association　of　Chinese　Academy　of　Sciences　(No.　2017086);　Natural　Science　Foundation　of　Fujian　Province,　China　(No.　2017J01657)

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Project No.

Access Number

EI:20192507062934

Language

Chinese

Corresponding s email

zhanglf@radi.ac.cn

Author Info