Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and c...Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.展开更多
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
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 study was supported by the National Natural Science Foundation of China(42271396)the Natural Science Foundation of Shandong Province(ZR2022MD017)+1 种基金the Key R&D Project of Hebei Province(22326406D)The European Space Agency(ESA)and Ministry of Science and Technology of China(MOST)Dragon(57457).
文摘Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.
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.
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.
