Soil Organic Carbon(SOC)is the most important indicator of soil health and determines long-term crop productivity.Here,we applied the Random Forest regression model to soil hyperspectral data to determine the importan...Soil Organic Carbon(SOC)is the most important indicator of soil health and determines long-term crop productivity.Here,we applied the Random Forest regression model to soil hyperspectral data to determine the important spectral bands and regions for SOC retrieval.Multiple existing studies already identified specific wavelength bands that could be good indicators of SOC.However,there is no hyperspectral-based method that is currently available to simultaneously investigate these identified specific wavelength regions for SOC.To help fill this gap,we developed the Perimeter-Area Soil Carbon Index(PASCI)that utilized optimal SOC spectral bands and then evaluated its robustness for SOC prediction and retrieval against other existing indices.The results of regression analysis between SOC and PASCI values showed a significant relationship(r^(2)=0.76;p<0.05).A significant statistical relationship(r^(2)=0.73)was also observed between SOC and the sum indices.The results from this study have advanced our understanding of the optimal spectral bands for SOC.Finally,the PASCI could be applied to hyperspectral and multispectral images to remotely quantify,predict,and map SOC.展开更多
Researchers continually demonstrated through published literature how LiDAR could create unparalleled measurements of ecosystem structure and forest height.There are a number of studies conducted utilizing waveform Li...Researchers continually demonstrated through published literature how LiDAR could create unparalleled measurements of ecosystem structure and forest height.There are a number of studies conducted utilizing waveform LiDAR products for terrestrial monitoring,but those that deal specifically with the assessment of space-borne waveform LiDAR for monitoring and modeling of phenology is very limited.This review highlights the waveform LiDAR system and looks into satellite sensors that could link waveform LiDAR and vegetation phenology,such as the proposed NASA’s Global Ecosystem Dynamics Investigation(GEDI)and the Japanese Experimental Module(JEM)-borne LiDAR sensor named MOLI(Multi-footprint Observation LIDAR and Imager).Further,this work examines the richness and utility of the waveform returns and proposes a spline-function-derived model that could be exploited for estimating the leaf-shooting date.The new approach may be utilized for ecosystem-level phenological studies.展开更多
基金supported by the National Aeronautics and Space Administration(Grant number 80NSSC17K0653 P00001)the joint NASA and Indian Space Research Organization AVIRIS-NG Campaign in Indiasupported by NIFA/USDA through Central State University Evans-Allen Research Program(Grant Number NI201445×XXXG018-0001).
文摘Soil Organic Carbon(SOC)is the most important indicator of soil health and determines long-term crop productivity.Here,we applied the Random Forest regression model to soil hyperspectral data to determine the important spectral bands and regions for SOC retrieval.Multiple existing studies already identified specific wavelength bands that could be good indicators of SOC.However,there is no hyperspectral-based method that is currently available to simultaneously investigate these identified specific wavelength regions for SOC.To help fill this gap,we developed the Perimeter-Area Soil Carbon Index(PASCI)that utilized optimal SOC spectral bands and then evaluated its robustness for SOC prediction and retrieval against other existing indices.The results of regression analysis between SOC and PASCI values showed a significant relationship(r^(2)=0.76;p<0.05).A significant statistical relationship(r^(2)=0.73)was also observed between SOC and the sum indices.The results from this study have advanced our understanding of the optimal spectral bands for SOC.Finally,the PASCI could be applied to hyperspectral and multispectral images to remotely quantify,predict,and map SOC.
文摘Researchers continually demonstrated through published literature how LiDAR could create unparalleled measurements of ecosystem structure and forest height.There are a number of studies conducted utilizing waveform LiDAR products for terrestrial monitoring,but those that deal specifically with the assessment of space-borne waveform LiDAR for monitoring and modeling of phenology is very limited.This review highlights the waveform LiDAR system and looks into satellite sensors that could link waveform LiDAR and vegetation phenology,such as the proposed NASA’s Global Ecosystem Dynamics Investigation(GEDI)and the Japanese Experimental Module(JEM)-borne LiDAR sensor named MOLI(Multi-footprint Observation LIDAR and Imager).Further,this work examines the richness and utility of the waveform returns and proposes a spline-function-derived model that could be exploited for estimating the leaf-shooting date.The new approach may be utilized for ecosystem-level phenological studies.
