Due to the coarse scale of soil moisture products retrieved from passive microwave observations(SMPMW),several downscaling methods have been developed to enable regional scale applications.However,it can be challengin...Due to the coarse scale of soil moisture products retrieved from passive microwave observations(SMPMW),several downscaling methods have been developed to enable regional scale applications.However,it can be challenging for users to access final data products and algorithms,as well as managing different data sources and formats,various data processing methods,and the complexity of the workflows from raw data to information products.Here,the Google Earth Engine(GEE),which as of late offers SMPMW,is used to implement a workflow for retrieving 1 km SM at a depth of 0-5 cm using MODIS optical/thermal measurements,the SM_(PMW)coarse scale product,and a random forest regression.The proposed method was implemented on the African continent to estimate weekly SM maps.The results of this study were evaluated against in-situ measurements of three validation networks.Overall,in comparison to the original SM_(PMW)product,which was limited by a spatial resolution of only 9 km,this method is able to estimate SM at 1 km spatial resolution with acceptable accuracy(an average correlation coefficient of 0.64 and a ubRMSD of 0.069 m^(3)/m^(3)).The results show that the proposed method in GEE provides a precise estimation of SM with a higher spatial resolution across the entire continent.展开更多
Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content.However,dedicated efforts are still required t...Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content.However,dedicated efforts are still required to develop workflows,executable on cloud-based platforms,for ingesting the satellite data,performing the targeted processes,and generating the desired products.In this study,an operational workflow is proposed,based on monthly Evaporative Stress Index(ESI)anomaly,and implemented in cloud-based online Virtual Earth Laboratory(VLab)platform,as a demonstration,to monitor European agricultural water stress.To this end,daily time-series of actual and reference evapotranspiration(ETa and ET0),from the Spinning Enhanced Visible and Infrared Imager(SEVIRI)sensor,were used to execute the proposed workflow successfully on VLab.The execution of the workflow resulted in obtaining one decade(2011–2020)of European monthly agricultural water stress maps at 0.04˚spatial resolution and corresponding stress reports for each country.To support open science,all the workflow outputs are stored in GeoServer,documented in GeoNetwork,and made available through MapStore.This enables creating a dashboard for better visualization of the results for end-users.The results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data.展开更多
基金funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)-SFB 1502/1-2022-project number:450058266.
文摘Due to the coarse scale of soil moisture products retrieved from passive microwave observations(SMPMW),several downscaling methods have been developed to enable regional scale applications.However,it can be challenging for users to access final data products and algorithms,as well as managing different data sources and formats,various data processing methods,and the complexity of the workflows from raw data to information products.Here,the Google Earth Engine(GEE),which as of late offers SMPMW,is used to implement a workflow for retrieving 1 km SM at a depth of 0-5 cm using MODIS optical/thermal measurements,the SM_(PMW)coarse scale product,and a random forest regression.The proposed method was implemented on the African continent to estimate weekly SM maps.The results of this study were evaluated against in-situ measurements of three validation networks.Overall,in comparison to the original SM_(PMW)product,which was limited by a spatial resolution of only 9 km,this method is able to estimate SM at 1 km spatial resolution with acceptable accuracy(an average correlation coefficient of 0.64 and a ubRMSD of 0.069 m^(3)/m^(3)).The results show that the proposed method in GEE provides a precise estimation of SM with a higher spatial resolution across the entire continent.
基金supported by The European Commission HORIZON 2020 Program ERA-PLANET/GEOEssential project[grant number 689443].
文摘Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content.However,dedicated efforts are still required to develop workflows,executable on cloud-based platforms,for ingesting the satellite data,performing the targeted processes,and generating the desired products.In this study,an operational workflow is proposed,based on monthly Evaporative Stress Index(ESI)anomaly,and implemented in cloud-based online Virtual Earth Laboratory(VLab)platform,as a demonstration,to monitor European agricultural water stress.To this end,daily time-series of actual and reference evapotranspiration(ETa and ET0),from the Spinning Enhanced Visible and Infrared Imager(SEVIRI)sensor,were used to execute the proposed workflow successfully on VLab.The execution of the workflow resulted in obtaining one decade(2011–2020)of European monthly agricultural water stress maps at 0.04˚spatial resolution and corresponding stress reports for each country.To support open science,all the workflow outputs are stored in GeoServer,documented in GeoNetwork,and made available through MapStore.This enables creating a dashboard for better visualization of the results for end-users.The results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data.
