Monitoring rice growth by spectral remote sensing technology can provide scientific basis for the high yield and efficient production of rice. Field experiments with different nitrogen application amounts using Tianyo...Monitoring rice growth by spectral remote sensing technology can provide scientific basis for the high yield and efficient production of rice. Field experiments with different nitrogen application amounts using Tianyouhuazhan rice as test sam- ples were set up to study the relationship between rice leaf area index (LAI) and canopy reflectance spectral. The results showed that: the LAI increased with the amount of applied nitrogen; the canopy reflectance spectral showed significant re- sponse characteristics to groups with different nitrogen application levels; the corre- lation coefficient of LAI and canopy spectral reflectance reached the maximum at 720 nm red edge region. The mathematical model was constructed to predict the LAI according to the canopy reflectance spectra of rice.展开更多
The investigation was made on the relationship of seasonal time-course canopy spectral reflectance and ratio index to total leaf nitrogen accumulation (leaf nitrogen content per unit ground area) in rice under differe...The investigation was made on the relationship of seasonal time-course canopy spectral reflectance and ratio index to total leaf nitrogen accumulation (leaf nitrogen content per unit ground area) in rice under different nitrogen treatments. The results showed there was a close correlation between the canopy spectral reflectance and total leaf nitrogen accumulation. Ratio of near infrared to green band (R810/R560) was linearly related with total leaf nitrogen accumulation. independent of nitrogen levels and development stages. Different datasets were used to test the linear regression equation, with average estimation accuracy of 91. 22%, RMSE of 1.09 and average relative error of 0. 026. Thus, the ratio index R810/R560 of canopy spectral reflectance should be useful for non-destructive monitoring and diagnosis of nitrogen status in rice plants.展开更多
A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen(N)topdressing decisions.Algorithms for estimating field-specific fertilizer N needs are based...A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen(N)topdressing decisions.Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field.The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor,soil and plant analyzer development(SPAD)chlorophyll meter,and two different types of leaf color charts(LCCs)for five basmati rice genotypes across different growth stages.Regression analysis was performed using normalized difference vegetation index(NDVI)recorded with GreenSeeker sensor and leaf greenness indices measured with SPAD meter and LCCs developed by Punjab Agricultural University,Ludhiana(India)(PAU-LCC)and the International Rice Research Institute,Philippines(IRRI-LCC).The exponential relationship between NDVI and grain yield exhibited the highest coefficient of determination(R^(2))and minimum normalized root mean square error(NRMSE)at panicle initiation stage and explained 38.3%–76.4%variation in yield using genotype-specific models.Spectral indices pooled for different genotypes were closely related to grain yield at all growth stages and explained53.4%–57.2%variation in grain yield.Normalizing different spectral indices with cumulative growing degree days(CGDD)decreased the accuracy of yield prediction.Normalization with days after transplanting(DAT),however,did not reduce or improve the predictability of yield.The ability of each model to predict grain yield was validated with an independent dataset collected from two experiments conducted at different sites with a range of fertilizer N doses.The NDVI-based genotype-specific models exhibited a robust linear correlation(R^(2)=0.77,NRMSE=7.37%,n=180)between observed and predicted grain yields only at 35 DAT(i.e.,panicle initiation stage),while the SPAD,PAU-LCC,and IRRI-LCC consistently provided reliable predictions(with respective R^(2)of 0.63,0.60,and 0.53 and NRMSE of 10%,10%,and 13.6%)even with genotype invariant models with 900 data points obtained at different growth stages.The study revealed that unnormalized values of spectral indices,namely NDVI,SPAD,PAU-LCC,and IRRI-LCC,can be satisfactorily used for in-season estimation of grain yield for basmati rice.As LCCs are very economical compared with chlorophyll meters and canopy reflectance sensors,they can be preferably used by small farmers in developing countries.展开更多
The influence of major cultural practices including different nitrogen application rates, population densities, transplanting leaf ages of seedling, and water regimes on rice canopy spectral reflectance was investigat...The influence of major cultural practices including different nitrogen application rates, population densities, transplanting leaf ages of seedling, and water regimes on rice canopy spectral reflectance was investigated. Results showed that increased nitrogen rates, water regimes and population densities and decreased seedling ages could enhance reflectance at NIR (near infrared) bands and reduce reflectance at visible bands. Using reflectance of green, red and NIR band and ratio index of 810-560 nm could distinguish the different type of rice by fuzzy cluster analysis,展开更多
The influence of major cultural practices including different nitrogen application rates, population densities, transplanting leaf ages of seedling, and water regimes on rice canopy spectral reflectance was investigat...The influence of major cultural practices including different nitrogen application rates, population densities, transplanting leaf ages of seedling, and water regimes on rice canopy spectral reflectance was investigated. Results showed that increased nitrogen rates, water regimes and population densities and decreased seedling ages could enhance reflectance at NIR (near infrared) bands and reduce reflectance at visible bands. Using reflectance of green, red and NIR band and ratio index of 810-560 nm could distinguish the different type of rice by fuzzy cluster analysis,展开更多
Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen...Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination(UVE). Vegetation indices(VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares(PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy.UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models(UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index(NDVI, based on red edge and NIR wavebands) yielded the highest correlation coefficients(r > 0.88). PLS regression models showed the lowest root mean square error(RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models.UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status.展开更多
To evaluate the temporal patterns of N deficiencies in corn and assess the ability of remote sensing to diagnose N deficiencies during the vegetative growth of corn, three field-scale experiments were conducted with v...To evaluate the temporal patterns of N deficiencies in corn and assess the ability of remote sensing to diagnose N deficiencies during the vegetative growth of corn, three field-scale experiments were conducted with various rates (56, 112, and 168 kg N ha-1 ), timing (early and late applications) and placement (injected into soil and dribbled on soil surface) of N fertilization in a split-plot design. Relationships between canopy reflectance during the growing season and yield data at the end of growing season were studied for different treatments. Results showed significant variation in both grain yields and canopy reflectance among the three cornfields. The N fertilization made in early June resulted in low canopy reflectance in early July, but the differences disappeared as the season progressed. The effect of N rates on canopy reflectance was not significant in early July but it gradually became detectable in mid-July and thereafter. The fertilizer placement had a significant effect on grain yields only in one field but not on canopy reflectance in all three fields. These observations suggest that the deficiency of N developed under field conditions is a dynamic phenomenon, which adds complexity for accurately defining "N deficiency" and effectively developing management strategies for in-season correction. Remote sensing throughout the season helps collect information about important interactions that have not been given enough attention in the past.展开更多
This paper compares the predictions by two radiative transfer models-the two-stream approximation model and the generalized layered model (developed by the authors) in land surface processes -for different canopies ...This paper compares the predictions by two radiative transfer models-the two-stream approximation model and the generalized layered model (developed by the authors) in land surface processes -for different canopies under direct or diffuse radiation conditions. The comparison indicates that there are significant differences between the two models, especially in the near infrared (NIR) band. Results of canopy reflectance from the two-stream model are larger than those from the generalized model. However, results of canopy absorptance from the two-stream model are larger in some cases and smaller in others compared to those from the generalized model, depending on the cases involved. In the visible (VIS) band, canopy reflectance is smaller and canopy absorptance larger from the two-stream model compared to the generalized model when the Leaf Area Index (LAI) is low and soil reflectance is high. In cases of canopies with vertical leaf angles, the differences of reflectance and absorptance in the VIS and NIR bands between the two models are especially large. Two commonly occurring cases, with which the two-stream model cannot deal accurately, are also investigated. One is for a canopy with different adaxial and abaxial leaf optical properties; and the other is for incident sky diffuse radiation with a non-uniform distribution. Comparison of the generalized model within the same canopy for both uniform and non-uniform incident diffuse radiation inputs shows smaller differences in general. However, there is a measurable difference between these radiation inputs for a canopy with high leaf angle. This indicates that the application of the two-stream model to a canopy with different adaxial and abaxial leaf optical properties will introduce non-negligible errors.展开更多
Two field experiments were conducted in Jiashan and Yuhang towns of Zhejiang Province, China, to study the feasibility of predicting N status of rice using canopy spectral reflectance. The canopy spectral reflectance ...Two field experiments were conducted in Jiashan and Yuhang towns of Zhejiang Province, China, to study the feasibility of predicting N status of rice using canopy spectral reflectance. The canopy spectral reflectance of rice grown with different levels of N inputs was determined at several important growth stages. Statistical analyses showed that as a result of the different levels of N supply, there were significant differences in the N concentrations of canopy leaves at different growth stages. Since spectral reflectance measurements showed that the N status of rice was related to reflectance in the visible and NIR (near-infrared) ranges, observations for rice in 1 nm bandwidths were then converted to bandwidths in the visible and NIR spectral regions with IKONOS (space imaging) bandwidths and vegetation indices being used to predict the N status of rice. The results indicated that canopy reflectance measurements converted to ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) for simulated IKONOS bands provided a better prediction of rice N status than the reflectance measurements in the simulated IKONOS bands themselves. The precision of the developed regression models using RVI and NDVI proved to be very high with R2 ranging from 0.82 to 0.94, and when validated with experimental data from a different site, the results were satisfactory with R2 ranging from 0.55 to 0.70. Thus, the results showed that theoretically it should be possible to monitor N status using remotely sensed data.展开更多
Recent studies have demonstrated the application of vegetation indices from canopy reflectedspectrum for inversion of chlorophyll concentration. Some indices are both response tovariations of vegetation and environmen...Recent studies have demonstrated the application of vegetation indices from canopy reflectedspectrum for inversion of chlorophyll concentration. Some indices are both response tovariations of vegetation and environmental factors. Canopy chlorophyll concentration, anindicator of photosynthesis activity, is related to nitrogen concentration in green vegetationand serves as an indicator of the crop response to soil nitrogen fertilizer application. Thecombination of normalized difference vegetation index (NDVI) and photochemical reflectanceindex (PRI) can reduce the effect of leaf area index (LAI) and soil background. The canopychlorophyll inversion index (CCII) was proved to be sensitive to chlorophyll concentration andvery resistant to the other variations. This paper introduced the ratio of TCARI/OSAVI to makeaccurate predictions of winter wheat chlorophyll concentration under different cultivars. Itindicated that canopy chlorophyll concentration could be evaluated by some combined vegetationindices.展开更多
Field experiments were conducted to examine the influence factors of cultivar, nitrogen application and irrigation on grain protein content, gluten content and grain hardness in three winter wheat cultivars under fo...Field experiments were conducted to examine the influence factors of cultivar, nitrogen application and irrigation on grain protein content, gluten content and grain hardness in three winter wheat cultivars under four levels of nitrogen and irrigation treatments. Firstly, the influence of cultivars and environment factors on grain quality were studied, the effective factors were cultivars, irrigation, fertilization, etc. Secondly, total nitrogen content around winter wheat anthesis stage was proved to be significantly correlative with grain protein content, and spectral vegetation index significantly correlated to total nitrogen content around anthesis stage were the potential indicators for grain protein content. Accumulation of total nitrogen content and its transfer to grain is the physical link to produce the final grain protein, and total nitrogen content at anthesis stage was proved to be an indicator of final grain protein content. The selected normalized photochemical reflectance index (NPRI) was proved to be able to predict grain protein content on the close correlation between the ratio of total carotenoid to chlorophyll a and total nitrogen content. The method contributes towards developing optimal procedures for predicting wheat grain quality through analysis of their canopy reflected spectrum at anthesis stage. Regression equations were established to forecast grain protein and dry gluten content by total nitrogen content at anthesis stage, so it is feasible for forecasting grain quality by establishing correlation equations between biochemical constitutes and canopy reflected spectrum.展开更多
Effect of crop leaf angle on canopy reflected spectrum cannot be ignored in the inversion of leaf area index(LAI)and the monitoring of the crop growth condition using remote sensing technology.In this study,experiment...Effect of crop leaf angle on canopy reflected spectrum cannot be ignored in the inversion of leaf area index(LAI)and the monitoring of the crop growth condition using remote sensing technology.In this study,experiments on winter wheat(Triticum aestivum L.)were conducted to identify crop leaf angle distribution(LAD)by bidirectional canopy reflected spectrum.Canopy reflected spectrum has significant differences among erectophile,planophile and horizontal geometry varieties at essentially the same LAI value.Canopy reflectance value at near infrared of the erectophile variety was lower than that of the horizontal variety.The effects of LAI and crop LAD on canopy reflectance were studied among erectophile,planophile and horizontal LAD varieties.The Standard Deviation(STDEV)of canopy reflectance at the near infrared bands(800 nm and 1100 nm)was more significant than those of visible bands(450 nm,550 nm,680 nm).It indicates that near infrared bands could be used for different LAD wheat varieties identification.The method for identification of crop geometry parameters was by the bidirectional canopy reflectance at different wave bands and view angles.The bidirectional reflectance of visible and near infrared bands at 15°,30°and 45°field of view for the main viewing plane could be used for identification of erectophile,planophile and horizontal LAD varieties based on bidirectional data.For erectophile varieties,the bidirectional canopy reflectance at near infrared was f45°>f15°>f30°(f45°,f15°and f30°mean the canopy reflectance at 45°,15°,and 30°,respectively),in the visible band it was f45°>f15°≈f30°.For planophile varieties,the bidirectional canopy reflectance in the near infrared and visible band was f15°>f45°>f30°.For horizontal varieties,the bidirectional canopy reflectance in the near infrared and visible band was f45°>f30°>f15°.So,it is feasible to identify erectophile,planophile and horizontal varieties of wheat by bidirectional canopy reflected spectrum.展开更多
The spectral reflectance of recently formed salt marshes at the mouth of the Yangtze River,which are undergoing invasion by Spartina alterniflora,were assessed to determine the potential utility of remotely sensed dat...The spectral reflectance of recently formed salt marshes at the mouth of the Yangtze River,which are undergoing invasion by Spartina alterniflora,were assessed to determine the potential utility of remotely sensed data in assessing future invasion and changes in species composition.Following a review of published research on remote sensing of salt marshes,53 locations along three transects were sampled for paired data on plant species composition and spectral reflectance using a FieldSpecTM Pro JR Field Portable Spectroradiometer.Spectral data were processed concerning reflectance,and the averaged reflectance values for each sample were reanalysed to correspond to a 12-waveband bandset of the Compact Airborne Spectral Imager.The spectral data were summarised using principal components analysis(PCA)and the relationships between the vegetation composition,and the PCA axes of spectral data were examined.The first PCA axis of the reflectance data showed a strong correlation with variability in near infrared reflectance and‘brightness’,while the second axis was correlated with visible reflectance and‘greenness’.Total vegetation cover,vegetation height,and mudflat cover were all significantly related to the first axis.The implications of this in terms of the ability of remote sensing to distinguish the various salt marsh species and in particular the invasive species S.alterniflora were discussed.Major differences in species with various physiognomies could be recognised but problems occurred in separating early colonising S.alterniflora from other species at that stage.Further work using multi-seasonal hyperspectral data might assist in solving these problems.展开更多
The research proposed a novel wavelength selection strategy by the combination of moving window partial least squares(MWPLS)and genetic algorithm(GA)for the chlorophyll content detection of winter wheat canopy using s...The research proposed a novel wavelength selection strategy by the combination of moving window partial least squares(MWPLS)and genetic algorithm(GA)for the chlorophyll content detection of winter wheat canopy using spectroscopy technology.Firstly,the original spectral dataset was pre-processed by wavelet denosing,multiple scatter correction.Then,abnormal data samples were removed by Pauta Criterion and the dataset was divided into modeling set and validation set by SPXY.Finally,the sensitive wavebands were selected using MWPLS method and MWPLS+GA respectively and partial least squares(PLS)models were established for chlorophyll content prediction.For the model established by using all the wavebands in the region of 400-900 nm,its R_(c)^(2) and R_(v)^(2) were 0.4468 and 0.3821 respectively;its modeling root mean square error(RMSEM)and verification root mean square error(RMSEV)were 2.9057 and 1.7589 respectively.For the model established by using 151 wavebands selected by MWPLS,its R_(c)^(2) and R_(v)^(2) were 0.6210 and 0.5901 respectively;its RMSEM and RMSEV were 2.4007 and 1.6408 respectively.For the model established by using 36 wavebands selected by MWPLS+GA,its R_(c)^(2) and R_(v)^(2) were 0.7805 and 0.7497 respectively;its RMSEM and RMSEV were 1.8504 and 1.1315 respectively.The results show that wavelength selection can remove redundant information and improve model performance.The strategy of combining MWPLS with GA has also been proved to work well in selecting sensitive wavebands for chlorophyll content prediction.展开更多
基金Supported by the National Natural Science Foundation of China(31160252)~~
文摘Monitoring rice growth by spectral remote sensing technology can provide scientific basis for the high yield and efficient production of rice. Field experiments with different nitrogen application amounts using Tianyouhuazhan rice as test sam- ples were set up to study the relationship between rice leaf area index (LAI) and canopy reflectance spectral. The results showed that: the LAI increased with the amount of applied nitrogen; the canopy reflectance spectral showed significant re- sponse characteristics to groups with different nitrogen application levels; the corre- lation coefficient of LAI and canopy spectral reflectance reached the maximum at 720 nm red edge region. The mathematical model was constructed to predict the LAI according to the canopy reflectance spectra of rice.
基金supported by the National Natural Science Fundation of China(30030090)National Major Basic Research Proiect(G2000077900).
文摘The investigation was made on the relationship of seasonal time-course canopy spectral reflectance and ratio index to total leaf nitrogen accumulation (leaf nitrogen content per unit ground area) in rice under different nitrogen treatments. The results showed there was a close correlation between the canopy spectral reflectance and total leaf nitrogen accumulation. Ratio of near infrared to green band (R810/R560) was linearly related with total leaf nitrogen accumulation. independent of nitrogen levels and development stages. Different datasets were used to test the linear regression equation, with average estimation accuracy of 91. 22%, RMSE of 1.09 and average relative error of 0. 026. Thus, the ratio index R810/R560 of canopy spectral reflectance should be useful for non-destructive monitoring and diagnosis of nitrogen status in rice plants.
基金funded by the Department of Biotechnology(DBT)Government of India(No.BT/IN/UKVNC/42/RG/2014-15)the Biotechnology and Biological Sciences Research Council(BBSRC)under the international multi-institutional collaborative research project entitled Cambridge-India Network for Translational Research in Nitrogen(CINTRIN)(No.BB/N013441/1)。
文摘A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen(N)topdressing decisions.Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field.The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor,soil and plant analyzer development(SPAD)chlorophyll meter,and two different types of leaf color charts(LCCs)for five basmati rice genotypes across different growth stages.Regression analysis was performed using normalized difference vegetation index(NDVI)recorded with GreenSeeker sensor and leaf greenness indices measured with SPAD meter and LCCs developed by Punjab Agricultural University,Ludhiana(India)(PAU-LCC)and the International Rice Research Institute,Philippines(IRRI-LCC).The exponential relationship between NDVI and grain yield exhibited the highest coefficient of determination(R^(2))and minimum normalized root mean square error(NRMSE)at panicle initiation stage and explained 38.3%–76.4%variation in yield using genotype-specific models.Spectral indices pooled for different genotypes were closely related to grain yield at all growth stages and explained53.4%–57.2%variation in grain yield.Normalizing different spectral indices with cumulative growing degree days(CGDD)decreased the accuracy of yield prediction.Normalization with days after transplanting(DAT),however,did not reduce or improve the predictability of yield.The ability of each model to predict grain yield was validated with an independent dataset collected from two experiments conducted at different sites with a range of fertilizer N doses.The NDVI-based genotype-specific models exhibited a robust linear correlation(R^(2)=0.77,NRMSE=7.37%,n=180)between observed and predicted grain yields only at 35 DAT(i.e.,panicle initiation stage),while the SPAD,PAU-LCC,and IRRI-LCC consistently provided reliable predictions(with respective R^(2)of 0.63,0.60,and 0.53 and NRMSE of 10%,10%,and 13.6%)even with genotype invariant models with 900 data points obtained at different growth stages.The study revealed that unnormalized values of spectral indices,namely NDVI,SPAD,PAU-LCC,and IRRI-LCC,can be satisfactorily used for in-season estimation of grain yield for basmati rice.As LCCs are very economical compared with chlorophyll meters and canopy reflectance sensors,they can be preferably used by small farmers in developing countries.
文摘The influence of major cultural practices including different nitrogen application rates, population densities, transplanting leaf ages of seedling, and water regimes on rice canopy spectral reflectance was investigated. Results showed that increased nitrogen rates, water regimes and population densities and decreased seedling ages could enhance reflectance at NIR (near infrared) bands and reduce reflectance at visible bands. Using reflectance of green, red and NIR band and ratio index of 810-560 nm could distinguish the different type of rice by fuzzy cluster analysis,
文摘The influence of major cultural practices including different nitrogen application rates, population densities, transplanting leaf ages of seedling, and water regimes on rice canopy spectral reflectance was investigated. Results showed that increased nitrogen rates, water regimes and population densities and decreased seedling ages could enhance reflectance at NIR (near infrared) bands and reduce reflectance at visible bands. Using reflectance of green, red and NIR band and ratio index of 810-560 nm could distinguish the different type of rice by fuzzy cluster analysis,
基金supported by the National Key Research and Development Program of China (2016YFD0300602)China Agricultural Research System (CARS-04-PS19)Chengdu Science and Technology Project (2020-YF09-00033-SN)。
文摘Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination(UVE). Vegetation indices(VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares(PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy.UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models(UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index(NDVI, based on red edge and NIR wavebands) yielded the highest correlation coefficients(r > 0.88). PLS regression models showed the lowest root mean square error(RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models.UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status.
基金Project supported by the Iowa Soybean Association On-Farm Network R, USA
文摘To evaluate the temporal patterns of N deficiencies in corn and assess the ability of remote sensing to diagnose N deficiencies during the vegetative growth of corn, three field-scale experiments were conducted with various rates (56, 112, and 168 kg N ha-1 ), timing (early and late applications) and placement (injected into soil and dribbled on soil surface) of N fertilization in a split-plot design. Relationships between canopy reflectance during the growing season and yield data at the end of growing season were studied for different treatments. Results showed significant variation in both grain yields and canopy reflectance among the three cornfields. The N fertilization made in early June resulted in low canopy reflectance in early July, but the differences disappeared as the season progressed. The effect of N rates on canopy reflectance was not significant in early July but it gradually became detectable in mid-July and thereafter. The fertilizer placement had a significant effect on grain yields only in one field but not on canopy reflectance in all three fields. These observations suggest that the deficiency of N developed under field conditions is a dynamic phenomenon, which adds complexity for accurately defining "N deficiency" and effectively developing management strategies for in-season correction. Remote sensing throughout the season helps collect information about important interactions that have not been given enough attention in the past.
基金supported by the National Natural Science Foundation of China under Grant Nos.40233034,40605024,40575043,and 40305011.
文摘This paper compares the predictions by two radiative transfer models-the two-stream approximation model and the generalized layered model (developed by the authors) in land surface processes -for different canopies under direct or diffuse radiation conditions. The comparison indicates that there are significant differences between the two models, especially in the near infrared (NIR) band. Results of canopy reflectance from the two-stream model are larger than those from the generalized model. However, results of canopy absorptance from the two-stream model are larger in some cases and smaller in others compared to those from the generalized model, depending on the cases involved. In the visible (VIS) band, canopy reflectance is smaller and canopy absorptance larger from the two-stream model compared to the generalized model when the Leaf Area Index (LAI) is low and soil reflectance is high. In cases of canopies with vertical leaf angles, the differences of reflectance and absorptance in the VIS and NIR bands between the two models are especially large. Two commonly occurring cases, with which the two-stream model cannot deal accurately, are also investigated. One is for a canopy with different adaxial and abaxial leaf optical properties; and the other is for incident sky diffuse radiation with a non-uniform distribution. Comparison of the generalized model within the same canopy for both uniform and non-uniform incident diffuse radiation inputs shows smaller differences in general. However, there is a measurable difference between these radiation inputs for a canopy with high leaf angle. This indicates that the application of the two-stream model to a canopy with different adaxial and abaxial leaf optical properties will introduce non-negligible errors.
基金Project supported by the National Natural Science Foundation of China (Nos. 30070444 and 40201021)the British Council (No. SHA/992/308)the Doctor Foundation of Qingdao University of Science and Technology.
文摘Two field experiments were conducted in Jiashan and Yuhang towns of Zhejiang Province, China, to study the feasibility of predicting N status of rice using canopy spectral reflectance. The canopy spectral reflectance of rice grown with different levels of N inputs was determined at several important growth stages. Statistical analyses showed that as a result of the different levels of N supply, there were significant differences in the N concentrations of canopy leaves at different growth stages. Since spectral reflectance measurements showed that the N status of rice was related to reflectance in the visible and NIR (near-infrared) ranges, observations for rice in 1 nm bandwidths were then converted to bandwidths in the visible and NIR spectral regions with IKONOS (space imaging) bandwidths and vegetation indices being used to predict the N status of rice. The results indicated that canopy reflectance measurements converted to ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) for simulated IKONOS bands provided a better prediction of rice N status than the reflectance measurements in the simulated IKONOS bands themselves. The precision of the developed regression models using RVI and NDVI proved to be very high with R2 ranging from 0.82 to 0.94, and when validated with experimental data from a different site, the results were satisfactory with R2 ranging from 0.55 to 0.70. Thus, the results showed that theoretically it should be possible to monitor N status using remotely sensed data.
基金support provided for this research by the Special Funds for Major State Basic Research Project(G20000779)the 863 National Project(2002AA243011,2003AA209010 and H020821020130)
文摘Recent studies have demonstrated the application of vegetation indices from canopy reflectedspectrum for inversion of chlorophyll concentration. Some indices are both response tovariations of vegetation and environmental factors. Canopy chlorophyll concentration, anindicator of photosynthesis activity, is related to nitrogen concentration in green vegetationand serves as an indicator of the crop response to soil nitrogen fertilizer application. Thecombination of normalized difference vegetation index (NDVI) and photochemical reflectanceindex (PRI) can reduce the effect of leaf area index (LAI) and soil background. The canopychlorophyll inversion index (CCII) was proved to be sensitive to chlorophyll concentration andvery resistant to the other variations. This paper introduced the ratio of TCARI/OSAVI to makeaccurate predictions of winter wheat chlorophyll concentration under different cultivars. Itindicated that canopy chlorophyll concentration could be evaluated by some combined vegetationindices.
基金financially supported by the Special Funds for Major State Basic Research Project,China(G20000779)the China National High Tech R&D Program(2002AA243011,2003AA209010,H020821020130).
文摘Field experiments were conducted to examine the influence factors of cultivar, nitrogen application and irrigation on grain protein content, gluten content and grain hardness in three winter wheat cultivars under four levels of nitrogen and irrigation treatments. Firstly, the influence of cultivars and environment factors on grain quality were studied, the effective factors were cultivars, irrigation, fertilization, etc. Secondly, total nitrogen content around winter wheat anthesis stage was proved to be significantly correlative with grain protein content, and spectral vegetation index significantly correlated to total nitrogen content around anthesis stage were the potential indicators for grain protein content. Accumulation of total nitrogen content and its transfer to grain is the physical link to produce the final grain protein, and total nitrogen content at anthesis stage was proved to be an indicator of final grain protein content. The selected normalized photochemical reflectance index (NPRI) was proved to be able to predict grain protein content on the close correlation between the ratio of total carotenoid to chlorophyll a and total nitrogen content. The method contributes towards developing optimal procedures for predicting wheat grain quality through analysis of their canopy reflected spectrum at anthesis stage. Regression equations were established to forecast grain protein and dry gluten content by total nitrogen content at anthesis stage, so it is feasible for forecasting grain quality by establishing correlation equations between biochemical constitutes and canopy reflected spectrum.
基金National Natural Science Foundation of China(40701119)the National High Tech R&D Program of China(2007AA10Z203,2007AA10Z201)Program from Ministry of Agriculture(200803037)。
文摘Effect of crop leaf angle on canopy reflected spectrum cannot be ignored in the inversion of leaf area index(LAI)and the monitoring of the crop growth condition using remote sensing technology.In this study,experiments on winter wheat(Triticum aestivum L.)were conducted to identify crop leaf angle distribution(LAD)by bidirectional canopy reflected spectrum.Canopy reflected spectrum has significant differences among erectophile,planophile and horizontal geometry varieties at essentially the same LAI value.Canopy reflectance value at near infrared of the erectophile variety was lower than that of the horizontal variety.The effects of LAI and crop LAD on canopy reflectance were studied among erectophile,planophile and horizontal LAD varieties.The Standard Deviation(STDEV)of canopy reflectance at the near infrared bands(800 nm and 1100 nm)was more significant than those of visible bands(450 nm,550 nm,680 nm).It indicates that near infrared bands could be used for different LAD wheat varieties identification.The method for identification of crop geometry parameters was by the bidirectional canopy reflectance at different wave bands and view angles.The bidirectional reflectance of visible and near infrared bands at 15°,30°and 45°field of view for the main viewing plane could be used for identification of erectophile,planophile and horizontal LAD varieties based on bidirectional data.For erectophile varieties,the bidirectional canopy reflectance at near infrared was f45°>f15°>f30°(f45°,f15°and f30°mean the canopy reflectance at 45°,15°,and 30°,respectively),in the visible band it was f45°>f15°≈f30°.For planophile varieties,the bidirectional canopy reflectance in the near infrared and visible band was f15°>f45°>f30°.For horizontal varieties,the bidirectional canopy reflectance in the near infrared and visible band was f45°>f30°>f15°.So,it is feasible to identify erectophile,planophile and horizontal varieties of wheat by bidirectional canopy reflected spectrum.
基金This research was funded by the Key Project of the Shanghai Science and Technology Committee(Grant No.06DZ12302)National Basic Research Program of China(Grant No.2004CB720505).
文摘The spectral reflectance of recently formed salt marshes at the mouth of the Yangtze River,which are undergoing invasion by Spartina alterniflora,were assessed to determine the potential utility of remotely sensed data in assessing future invasion and changes in species composition.Following a review of published research on remote sensing of salt marshes,53 locations along three transects were sampled for paired data on plant species composition and spectral reflectance using a FieldSpecTM Pro JR Field Portable Spectroradiometer.Spectral data were processed concerning reflectance,and the averaged reflectance values for each sample were reanalysed to correspond to a 12-waveband bandset of the Compact Airborne Spectral Imager.The spectral data were summarised using principal components analysis(PCA)and the relationships between the vegetation composition,and the PCA axes of spectral data were examined.The first PCA axis of the reflectance data showed a strong correlation with variability in near infrared reflectance and‘brightness’,while the second axis was correlated with visible reflectance and‘greenness’.Total vegetation cover,vegetation height,and mudflat cover were all significantly related to the first axis.The implications of this in terms of the ability of remote sensing to distinguish the various salt marsh species and in particular the invasive species S.alterniflora were discussed.Major differences in species with various physiognomies could be recognised but problems occurred in separating early colonising S.alterniflora from other species at that stage.Further work using multi-seasonal hyperspectral data might assist in solving these problems.
基金supported by the National Key Research and Development Program(2016YFD0200600-2016YFD0200602)National Natural Science Fund(Grant No.31501219)the graduate training project of China agricultural university(ZYXW037,HJ2019029,YW2019018).
文摘The research proposed a novel wavelength selection strategy by the combination of moving window partial least squares(MWPLS)and genetic algorithm(GA)for the chlorophyll content detection of winter wheat canopy using spectroscopy technology.Firstly,the original spectral dataset was pre-processed by wavelet denosing,multiple scatter correction.Then,abnormal data samples were removed by Pauta Criterion and the dataset was divided into modeling set and validation set by SPXY.Finally,the sensitive wavebands were selected using MWPLS method and MWPLS+GA respectively and partial least squares(PLS)models were established for chlorophyll content prediction.For the model established by using all the wavebands in the region of 400-900 nm,its R_(c)^(2) and R_(v)^(2) were 0.4468 and 0.3821 respectively;its modeling root mean square error(RMSEM)and verification root mean square error(RMSEV)were 2.9057 and 1.7589 respectively.For the model established by using 151 wavebands selected by MWPLS,its R_(c)^(2) and R_(v)^(2) were 0.6210 and 0.5901 respectively;its RMSEM and RMSEV were 2.4007 and 1.6408 respectively.For the model established by using 36 wavebands selected by MWPLS+GA,its R_(c)^(2) and R_(v)^(2) were 0.7805 and 0.7497 respectively;its RMSEM and RMSEV were 1.8504 and 1.1315 respectively.The results show that wavelength selection can remove redundant information and improve model performance.The strategy of combining MWPLS with GA has also been proved to work well in selecting sensitive wavebands for chlorophyll content prediction.