The effects of climate change are becoming more evident nowadays,and the environmental stress imposed on crops has become more severe.Farmers around the globe continually seek ways to gain insights into crop health an...The effects of climate change are becoming more evident nowadays,and the environmental stress imposed on crops has become more severe.Farmers around the globe continually seek ways to gain insights into crop health and provide mitigation as early as possible.Phenotyping is a non-destructive method for assessing crop responses to environmental stresses and can be performed using airborne systems.Unmanned Aerial Systems(UAS)have significantly contributed to high-throughput phenotyping andmade the process rapid,efficient,and non-invasive for collecting large-scale agronomic data.Because of the high complexity and cost of specialized equipment used in aerial phenotyping,such as multispectral and hyperspectral cameras as well as lidar,this study proposes a framework for implementing aerial phenotyping where chlorophyll estimation,leaf count,and coverage are determined using the RGB(Red,Green and Blue)camera native to a UAS.Thestudy proposes the Dynamic Coefficient Triangular Greenness Index(DCTGI)for aerial phenotyping.Evaluation of the proposed DCTGI includes the correlation with chlorophyll content estimated using a Soil Plant Analysis Development(SPAD)chlorophyll meter on randomly sampled Liberica coffee seedlings.Analysis revealed a strong relationship between DCTGI values and chlorophyll estimates derived from SPAD measurements,with a Pearson’s correlation coefficient of 0.912.However,the study didn’t implement tissue-level validation and field-scale temporal analysis to assess seasonal variability.In addition,the SPAD meter provided the approximate nitrogen content together with the chlorohyll estimate.展开更多
Canopy nitrogen content(CNC)and canopy phosphorus content(CPC)of vegetation in wetlands are key phys-iological traits,which can be associated with the process of wetland ecosystems.Because of the spectral signals obsc...Canopy nitrogen content(CNC)and canopy phosphorus content(CPC)of vegetation in wetlands are key phys-iological traits,which can be associated with the process of wetland ecosystems.Because of the spectral signals obscured by pigments and water content,it is challenging to accurately estimate CNC and CPC of vegetation species in wetlands using multispectral images.Therefore,we developed the constrained PROSAIL-PRO spectra matching(CPSM)approach to extend multispectral reflectance of unmanned aerial vehicle measurements to 400~2500 nm.We verified the matched accuracy and spectral reliability of CPSM's spectra from two aspects of reflectance and vegetation spectral characteristic based on field-measured spectral data.We proposed a novel hybrid retrieval strategy to achieve the high-precision estimation of CNC and CPC for seven karst wetland vegetation species.Finally,we evaluated the applicability of combining CPSM with our strategy to estimate CNC and CPC for two typical species in mangrove wetlands.Our results proved that CPSM-based spectra had good consistency with original reflectance of UAV images(R^(2)=0.82~0.86),and they could maintain similar spectral characteristics to measured spectra.Besides,this study found that the optimal spectral features of CNC and CPC were distributed near the red edge position and water-absorption valley of vegetation spectra.We obtained high-precision estimation of CNC and CPC in karst wetland using CPSM and our hybrid retrieval strategy(R^(2)=0.60~0.98,MRE=5.91%~26.25%).The approach also showed a better transferring performance in estimating CNC and CPC of mangrove species(R^(2)=0.77~0.89,MRE=9.65%~16.87%).The CPSM approach is effective to achieve high-precision estimation of vegetation CNC and CPC.展开更多
Accurate,non-destructive quantification of leaf nitrogen content(LNC)is crucial for monitoring crop health and growth.Traditional empirical methods require extensive in-situ data for training,while physically-based me...Accurate,non-destructive quantification of leaf nitrogen content(LNC)is crucial for monitoring crop health and growth.Traditional empirical methods require extensive in-situ data for training,while physically-based methods are limited by ill-posed inversion,and hybrid methods suffer from domain shift between in-situ and simulated data.To overcome these limitations,this study introduces DeepSpecN,a novel hybrid method for maize LNC estimation using leaf-scale hyperspectral bidirectional reflectance.Without requiring in-situ data for training,DeepSpecN combines four key components:continuous wavelet transform(CWT)for reducing specular reflection,PROSPECT-PRO for simulating training data,an improved Transformer model for feature learning,and a spectral similarity-based sample selection method for selecting more valuable training samples.DeepSpecN and other methods,including physically-based methods,non-parametric regression based hybrid methods,and parametric regression methods based on vegetation indices(VIs),were validated using bidirectional reflectance data from 1724 maize leaves.The results showed that,when trained on representative samples,DeepSpecN achieved the highest estimation accuracy among all the methods(RMSE=0.247 g/m^(2),R^(2)=0.665).The sample selection strategy mitigated the effects of domain shift by identifying representative training samples with high spectral similarity from the simulated database.Furthermore,the results showed that the Chlorophyll(Chl)-based empirical formulas estimated maize LNC more accurately than those based on leaf protein content.Moreover,the validation results on four different crop species confirm the generalizability of DeepSpecN.Our findings demonstrate the potential of hybrid methods that utilize bidirectional reflectance spectra,developed by addressing the domain shift issue,to improve the LNC estimation accuracy.展开更多
文摘The effects of climate change are becoming more evident nowadays,and the environmental stress imposed on crops has become more severe.Farmers around the globe continually seek ways to gain insights into crop health and provide mitigation as early as possible.Phenotyping is a non-destructive method for assessing crop responses to environmental stresses and can be performed using airborne systems.Unmanned Aerial Systems(UAS)have significantly contributed to high-throughput phenotyping andmade the process rapid,efficient,and non-invasive for collecting large-scale agronomic data.Because of the high complexity and cost of specialized equipment used in aerial phenotyping,such as multispectral and hyperspectral cameras as well as lidar,this study proposes a framework for implementing aerial phenotyping where chlorophyll estimation,leaf count,and coverage are determined using the RGB(Red,Green and Blue)camera native to a UAS.Thestudy proposes the Dynamic Coefficient Triangular Greenness Index(DCTGI)for aerial phenotyping.Evaluation of the proposed DCTGI includes the correlation with chlorophyll content estimated using a Soil Plant Analysis Development(SPAD)chlorophyll meter on randomly sampled Liberica coffee seedlings.Analysis revealed a strong relationship between DCTGI values and chlorophyll estimates derived from SPAD measurements,with a Pearson’s correlation coefficient of 0.912.However,the study didn’t implement tissue-level validation and field-scale temporal analysis to assess seasonal variability.In addition,the SPAD meter provided the approximate nitrogen content together with the chlorohyll estimate.
基金This research was supported by the National Natural Science Foundation of China(Grant number 42371341)the Natural Science Foundation of Guangxi Zhuang Autonomous Region(CN)(Grant number 2025GXNSFFA069008+2 种基金2024GXNSFAA010351)Key Laboratory of Tropical Marine Ecosystem and Bioresource Ministry of Natural Resources(Grant number 2023ZD02)Zhejiang Province"Pioneering Soldier"and"Leading Goose"R&D Project(Grant number 2023C01027).
文摘Canopy nitrogen content(CNC)and canopy phosphorus content(CPC)of vegetation in wetlands are key phys-iological traits,which can be associated with the process of wetland ecosystems.Because of the spectral signals obscured by pigments and water content,it is challenging to accurately estimate CNC and CPC of vegetation species in wetlands using multispectral images.Therefore,we developed the constrained PROSAIL-PRO spectra matching(CPSM)approach to extend multispectral reflectance of unmanned aerial vehicle measurements to 400~2500 nm.We verified the matched accuracy and spectral reliability of CPSM's spectra from two aspects of reflectance and vegetation spectral characteristic based on field-measured spectral data.We proposed a novel hybrid retrieval strategy to achieve the high-precision estimation of CNC and CPC for seven karst wetland vegetation species.Finally,we evaluated the applicability of combining CPSM with our strategy to estimate CNC and CPC for two typical species in mangrove wetlands.Our results proved that CPSM-based spectra had good consistency with original reflectance of UAV images(R^(2)=0.82~0.86),and they could maintain similar spectral characteristics to measured spectra.Besides,this study found that the optimal spectral features of CNC and CPC were distributed near the red edge position and water-absorption valley of vegetation spectra.We obtained high-precision estimation of CNC and CPC in karst wetland using CPSM and our hybrid retrieval strategy(R^(2)=0.60~0.98,MRE=5.91%~26.25%).The approach also showed a better transferring performance in estimating CNC and CPC of mangrove species(R^(2)=0.77~0.89,MRE=9.65%~16.87%).The CPSM approach is effective to achieve high-precision estimation of vegetation CNC and CPC.
基金supported by the‘AmAIzed’project funded by the AgroMissionHub,the National Natural Science Foundation of China(grant numbers 32373186)the CAU-TUM joint PhD training program(2023-2025)funded by China Scholarship Council.
文摘Accurate,non-destructive quantification of leaf nitrogen content(LNC)is crucial for monitoring crop health and growth.Traditional empirical methods require extensive in-situ data for training,while physically-based methods are limited by ill-posed inversion,and hybrid methods suffer from domain shift between in-situ and simulated data.To overcome these limitations,this study introduces DeepSpecN,a novel hybrid method for maize LNC estimation using leaf-scale hyperspectral bidirectional reflectance.Without requiring in-situ data for training,DeepSpecN combines four key components:continuous wavelet transform(CWT)for reducing specular reflection,PROSPECT-PRO for simulating training data,an improved Transformer model for feature learning,and a spectral similarity-based sample selection method for selecting more valuable training samples.DeepSpecN and other methods,including physically-based methods,non-parametric regression based hybrid methods,and parametric regression methods based on vegetation indices(VIs),were validated using bidirectional reflectance data from 1724 maize leaves.The results showed that,when trained on representative samples,DeepSpecN achieved the highest estimation accuracy among all the methods(RMSE=0.247 g/m^(2),R^(2)=0.665).The sample selection strategy mitigated the effects of domain shift by identifying representative training samples with high spectral similarity from the simulated database.Furthermore,the results showed that the Chlorophyll(Chl)-based empirical formulas estimated maize LNC more accurately than those based on leaf protein content.Moreover,the validation results on four different crop species confirm the generalizability of DeepSpecN.Our findings demonstrate the potential of hybrid methods that utilize bidirectional reflectance spectra,developed by addressing the domain shift issue,to improve the LNC estimation accuracy.
