Desert locust (Schistocerca gregaria, Forskal) is the most serious insect pest devastating and damagingrnagricultural products of cropland areas and pastureland during the invasion of locust. The main cause ofrndesert locust outbreaks is the trigger of rainfall occurring and the growth of green vegetation expands thernarea and the population of locust density leads to an upsurge and possibly develop plagues. The aim of thernstudy is to assess desert locust infestation using GIS and remote sensing technology. The open-sourcernsatellite data of EVI from MODIS with 250 m, sentinel-1 SAR data from Copernicus 10 m, DEM fromrnSRTM 1 arc seconds, precipitation data from GPM 0.1 degree spatial resolution, and ground survey datarnwere applied to analyze the effect of desert locust environmental variables of rainfall, vegetation, andrndigital elevation model (DEM), damaged vegetation assessment of desert locust and determine whetherrnkriging interpolation correctly predict the unobserved area using the surveyed site. The methodology ofrnthe study was Preprocessing, reclassification, zonal attribute analysis, and geostatistical analysis ofrnkriging, and (IDW) method of interpolation was performed. The distribution of locusts in September andrnOctober 2019 and September 2020 occurred in the Northern part of the study with low vegetation levelsrnand low rainfall amount. However, in November 2019, October, and November 2020 desert locustrninfestations occupied and migrated to the southern part of the study area with high vegetation and rainfall.rnThe lower mean pixel reflectance value of EVI data produced in September 2019 is 0.11 and a higherrnmean pixel value reflectance of 0.23 was produced in October 2020 damaged vegetation of desert locustrninfestation. Whereas the sentinel-1 SAR data value of lower mean pixel backscatter value of damagedrnvegetation (vertical-horizontal (VH -20.86 dB) and (vertical-vertical (VV -14.25 dB) produced Octoberrn2019) and higher mean pixel backscatter value (VH -17.78 dB and (VV -11.48 dB produced in Septemberrn2020). the kriging interpolation was applied to predict un-surveyed areas by the survey team using arnsurveyed site of spherical modeling. the regression value between measured and predicted of 2019 squarernr = 0.24 with p-value = 0.0003 and 2020 square r = 0.0084 and p-value = 0.26. The result indicatesrnkriging interpolation need randomly distributed and accurately measured data.