Diagnosis of Nitrogen Status in Rice Leaves with Canopy Spectral Reflectance

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.

Canopy Spectral Reflectance Characteristics of Rice with Different Cultural Practices and Their Fuzzy Cluster Analysis

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,

Spectral indices measured with proximal sensing using canopy reflectance sensor,chlorophyll meter and leaf color chart for in-season grain yield prediction of basmati rice

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.

Canopy Spectral Reflectance Characteristics of Rice with Different Cultural Practices and Their Fuzzy Cluster Analysis

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,

Assessing canopy nitrogen and carbon content in maize by canopy spectral reflectance and uninformative variable elimination

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.

A Comparison of Two Canopy Radiative Models in Land Surface Processes

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.

Inversion of Biochemical Parameters by Selection of Proper Vegetation Index in Winter Wheat

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.

Cultivar,Nitrogen and Irrigation Influence on Grain Quality and Its Forecasting Methods by In situ Reflected Spectrum of Winter Wheat

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.

Winter wheat geometry identification by bidirectional canopy reflected spectrum

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.

Spectral characteristics of plant communities from salt marshes:A case study from Chongming Dongtan,Yangtze estuary,China

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.

A novel wavelength selection strategy for chlorophyll prediction by MWPLS and GA

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.