This paper is devoted to the development and testing of the optimal procedures for retrieving biophysical crop variables by exploiting the spectral information of current multispectral optical satellite Sentinel-2 and...This paper is devoted to the development and testing of the optimal procedures for retrieving biophysical crop variables by exploiting the spectral information of current multispectral optical satellite Sentinel-2 and Venus and in view of the advent of the new Sino-EU hyperspectral satellite(e.g.,PRISMA,EnMAP,and GF-5).Two different methodologies devoted to the estimation of biophysical crop variables Leaf area index(LAI)and Leaf chlorophyll content(Cab)were evaluated:non-kernel-based and kernel-based Machine Learning Regression Algorithms(MLRA);Sentinel-2 and Venus data comparison for the analysis of the durum wheat-growing season.Results show that for Sentinel-2 data,Gaussian Process Regression(GPR)was the best performing algorithm for both LAI(R 2=0.89 and RMSE=0.59)and Cab(R 2=0.70 and RMSE=8.31).Whereas,for PRISMA simulated data the Kernel Ridge Regression(KRR)was the best performing algorithm among all the other MLRA(R 2=0.91 and RMSE=0.51)for LAI and(R 2=0.83 and RMSE=6.09)for Cab,respectively.Results of Sentinel-2 and Venus data for durum wheat-growing season were consistent with ground truth data and confirm also that SWIR bands,which are used as tie-points in the PROSAIL inversion,are extremely useful for an accurate retrieving of crop biophysical parameters.展开更多
The AMEOS(Assimilating Multi-source Earth Observation Satellite data for crop pests and diseases monitoring and forecasting)project aims to bring together cutting edge research to provide pest and disease monitoring a...The AMEOS(Assimilating Multi-source Earth Observation Satellite data for crop pests and diseases monitoring and forecasting)project aims to bring together cutting edge research to provide pest and disease monitoring and forecast information,integrating multi-source information(Earth Observation,meteorological,entomological and plant pathological,etc.)to support decision making in the sustainable management of insect pests and diseases in agriculture.The main objective of the project,that is,improving crop diseases and pests monitoring and forecasting,will be achieved by utilizing EO data,developing new algorithms,and combining new and existing data from multi-source EO sensors to produce high spatial and temporal land surface information.The project foresees the assessment of the possibility of using available satellite images datasets to assess the evolution of diseases on permanent(olive groves,vineyards),or row crops(wheat)in Italy and China.The paper describes the results of the research activity which focused on:①improving the classification of the agricultural areas devoted to winter wheat and olive trees,starting from what has been made available from the Corine Land Cover initiative;②developing an approach suitable to be automated for estimating trees by using Sentinel 2 images;③developing a new index,REDSI(consisting of Red,Re 1,and Re 3 bands),for detecting and monitoring yellow rust infection of winter wheat at the canopy and regional scale.The research activity covers the:Province of Lecce,that is the Italian area strongly affected,since 2015,by the Xylella fastidiosa disease which causes a rapid decline in olive plantations.Province of Anyang,Neihuang county,which was affected by the yellow rust disease in the spring 2017.展开更多
This research investigates the suitability of PRISMA and Sentinel-2 satellite imagery for retrieving topsoil properties such as Organic Matter(OM),Nitrogen(N),Phosphorus(P),Potassium(K),and pH in croplands using diffe...This research investigates the suitability of PRISMA and Sentinel-2 satellite imagery for retrieving topsoil properties such as Organic Matter(OM),Nitrogen(N),Phosphorus(P),Potassium(K),and pH in croplands using different Machine Learning(ML)algorithms and signal pre-treatments.Ninety-five soil samples were collected in Quzhou County,Northeast China.Satellite images captured soil reflectance data when bare soil was visible.For PRISMA data,a Linear Mixture Model(LMM)was used to separate soil and Photosynthetic Vegetation(PV)endmembers,excluding Non-Photosynthetic Vegetation(NPV)using Band Depth values at the 2100 nm absorption peak of cellulose.Sentinel-2 bare soil reflectance spectra were obtained using thresholds based on NDVI and NBR2 indices.Results showed PRISMA data provided slightly better accuracy in retrieving topsoil nutrients than Sentinel-2.While no optimal predictive algorithm was best,absorbance data proved more effective than reflectance.PRISMA results demonstrated potential for predicting soil nutrients in real scenarios.展开更多
基金This paper was supported by European Space Agency(ESA)contract 4000121195-Ministry of Science and Technology(MOST),Dragon 4 cooperation(ID:32275).Specifically,Subproject1-Topic1“Algorithm Development Exploiting Multitemporal and Multi Sensor Satellite Data for Improving Crop Classification,Biophysical and Agronomic Variables Retrieval and Yield Prediction”and by the Italian Space Agency(ASI)project PRISCAV(PRISMA Calibration/Validation).
文摘This paper is devoted to the development and testing of the optimal procedures for retrieving biophysical crop variables by exploiting the spectral information of current multispectral optical satellite Sentinel-2 and Venus and in view of the advent of the new Sino-EU hyperspectral satellite(e.g.,PRISMA,EnMAP,and GF-5).Two different methodologies devoted to the estimation of biophysical crop variables Leaf area index(LAI)and Leaf chlorophyll content(Cab)were evaluated:non-kernel-based and kernel-based Machine Learning Regression Algorithms(MLRA);Sentinel-2 and Venus data comparison for the analysis of the durum wheat-growing season.Results show that for Sentinel-2 data,Gaussian Process Regression(GPR)was the best performing algorithm for both LAI(R 2=0.89 and RMSE=0.59)and Cab(R 2=0.70 and RMSE=8.31).Whereas,for PRISMA simulated data the Kernel Ridge Regression(KRR)was the best performing algorithm among all the other MLRA(R 2=0.91 and RMSE=0.51)for LAI and(R 2=0.83 and RMSE=6.09)for Cab,respectively.Results of Sentinel-2 and Venus data for durum wheat-growing season were consistent with ground truth data and confirm also that SWIR bands,which are used as tie-points in the PROSAIL inversion,are extremely useful for an accurate retrieving of crop biophysical parameters.
文摘The AMEOS(Assimilating Multi-source Earth Observation Satellite data for crop pests and diseases monitoring and forecasting)project aims to bring together cutting edge research to provide pest and disease monitoring and forecast information,integrating multi-source information(Earth Observation,meteorological,entomological and plant pathological,etc.)to support decision making in the sustainable management of insect pests and diseases in agriculture.The main objective of the project,that is,improving crop diseases and pests monitoring and forecasting,will be achieved by utilizing EO data,developing new algorithms,and combining new and existing data from multi-source EO sensors to produce high spatial and temporal land surface information.The project foresees the assessment of the possibility of using available satellite images datasets to assess the evolution of diseases on permanent(olive groves,vineyards),or row crops(wheat)in Italy and China.The paper describes the results of the research activity which focused on:①improving the classification of the agricultural areas devoted to winter wheat and olive trees,starting from what has been made available from the Corine Land Cover initiative;②developing an approach suitable to be automated for estimating trees by using Sentinel 2 images;③developing a new index,REDSI(consisting of Red,Re 1,and Re 3 bands),for detecting and monitoring yellow rust infection of winter wheat at the canopy and regional scale.The research activity covers the:Province of Lecce,that is the Italian area strongly affected,since 2015,by the Xylella fastidiosa disease which causes a rapid decline in olive plantations.Province of Anyang,Neihuang county,which was affected by the yellow rust disease in the spring 2017.
基金co-funded by the European Space Agency Dragon5 project[ESA Contract number 4000135216/21/I-NB]co-funded by the Italian Space Agency THERA project[grant number DC-UOT-2019-061].
文摘This research investigates the suitability of PRISMA and Sentinel-2 satellite imagery for retrieving topsoil properties such as Organic Matter(OM),Nitrogen(N),Phosphorus(P),Potassium(K),and pH in croplands using different Machine Learning(ML)algorithms and signal pre-treatments.Ninety-five soil samples were collected in Quzhou County,Northeast China.Satellite images captured soil reflectance data when bare soil was visible.For PRISMA data,a Linear Mixture Model(LMM)was used to separate soil and Photosynthetic Vegetation(PV)endmembers,excluding Non-Photosynthetic Vegetation(NPV)using Band Depth values at the 2100 nm absorption peak of cellulose.Sentinel-2 bare soil reflectance spectra were obtained using thresholds based on NDVI and NBR2 indices.Results showed PRISMA data provided slightly better accuracy in retrieving topsoil nutrients than Sentinel-2.While no optimal predictive algorithm was best,absorbance data proved more effective than reflectance.PRISMA results demonstrated potential for predicting soil nutrients in real scenarios.
