Solar-induced chlorophyll fluorescence (SIF) is a direct proxy for photosynthetic activity, yet existing satellite SIF products are constrained by coarse spatial resolution, limiting their application in ecological and agricultural studies. In this work, we propose a One-Step Learned Spatial Redistribution Convolutional Neural Network (OSRNet) that downscales 0.05° TROPOMI SIF to 0.005° by learning spatially adaptive redistribution fields from high-resolution drivers, which allocate coarse-resolution satellite SIF into fine-resolution grids. Based on this framework, we generate RSIF, a global 16-day 0.005° SIF dataset for 2018–2020. Comprehensive evaluation against both satellite and tower-based SIF shows that RSIF maintains strong consistency with TROPOMI observations (R² = 0.976, RMSE = 0.036) while recovering fine-scale spatial details. OSRNet substantially outperforms established direct prediction methods such as RF and SIFNet, and, compared with post hoc corrected RF approach from prior studies, achieves the highest R² across all tower sites and generally the lowest RMSE, enabling more accurate representation of seasonal dynamics with improved spatial fidelity.

Solar-induced chlorophyll fluorescence (SIF) has a high correlation with gross primary production (GPP) at various spatiotemporal scales. However, this relationship varies with the changing environmental conditions, requiring further investigation and interpretation at various scales. In this study, we investigated (i) the temperature sensitivities of the SIF/GPP ratio, (ii) their correlation in 33 evergreen needleleaf forest sites using TROPOMI SIF and flux tower datasets from 2018 to 2021, and (iii) the temperature responses of the ratio of quantum yield of fluorescence to quantum efficiency of photosystem II (ΦF/ΦPSII) using leaf measurements from 3 datasets. At the canopy scale, SIF effectively tracked the GPP during the year, and the SIF/GPP ratio was relatively stable from 8 to 18 °C, while it increased at cold (28°C). Furthermore, the SIF–GPP correlation was strongest at moderate temperatures (~25 °C). At the leaf scale, ΦF/ΦPSII also increased at low temperatures, which demonstrated the direct impact of temperature on the energy partitioning in the light reaction. This study indicated the need to consider the changing temperature and energy partitioning during the light reaction when using fluorescence to track photosynthesis, especially under extreme environments.

Solar-induced chlorophyll fluorescence (SIF) provides a promising approach to monitoring plant photosynthesis. To this end, numerous retrieval algorithms have emerged and been developed to estimate ground, airborne, and satellite SIF; however, the accuracy of SIF retrieval methods in the red band is still somewhat limited. One potential obstacle that hinders the accuracy of retrieval of red SIF is the difficulty of modeling the true shape of the reflectance in the O2-B band. To overcome this issue, herein, an improved spectral-fitting method (SFM) using principal component analysis (PCA) data-driven reflectance reconstruction method, SFM-PCA, is proposed based on a novel SIF-free reflectance dataset to improve the accuracy of SIF retrieval in the O2-B band. In this work, the SFM-PCA method was validated using a field leaf dataset, canopy simulations, and tower-based canopy measurements. Compared to the true red SIF values at either the leaf or canopy levels, the SFM-PCA method was found to perform better than previous methods, with$R^{2}$values of 0.97 and 0.999 for leaf measurements and canopy simulations, respectively, and corresponding normalized root-mean-square error (NRMSE) values of 6.98% and 2.335%. For the tower-based measurements, the red SIF retrieved using the SFM-PCA method was also more consistent with the O2-A SIF. This indicates that it is feasible to make use of principal components (PCs) derived from SIF-free reflectance measurements to accurately model the true shape of the reflectance spectrum in the O2-B band and to improve ground-based SIF retrieval in the red band. This also has the potential to be applied to the satellite-based measurements.

Quantifying and monitoring land surface evapotranspiration (ET) is an essential task for understanding the earth’s water, energy, and carbon cycles. ET, specifically plant transpiration ($T$), is closely linked to the photosynthesis, which is coupled through stomatal function. However, the mechanistic links between sun-induced chlorophyll fluorescence (SIF) information indicating canopy photosynthetic activity and$T$are complex and difficult to derive empirically. An empirical SIF-$T$relationship at ecosystem scale was developed and coupled to the two-source energy balance model (TSEB-SIF) to estimate the ET and its components,$T$and soil evaporation,$E$. By comparing model predictions with observations from an irrigated cropland site located in a semiarid region, the TSEB-SIF model shows a slightly better performance to the TSEB model in estimating ET, especially under water deficit conditions. Moreover, the TSEB-SIF model more reliably partitioned the$T$from ET, while the TSEB model tended to overestimate the contribution of$T$to ET.

The collaborative use of Landsat and Sentinel-2 could substantially improve the temporal observation frequency at the medium spatial resolution, which was very important for the studies demanding dense temporal observations. The purpose of this study was to develop the global cross-sensor transformation functions for the well-established Landsat-8 and Sentinel-2 top of atmosphere (TOA) and surface reflectance (SR) products integrated within Google Earth Engine (GEE). Comparison results indicated the significant radiometric differences between Landsat-8 and Sentinel-2, resulting in band-wise root mean square error (RMSE) ranging from 0.0091 to 0.0357 and 0.0168 to 0.0348 for TOA and SR, respectively, and the linear relationships were developed accordingly. Furthermore, via using 12 validation sites across the globe, this study confirmed that the proposed correction models could substantially IMPROVE the time-series agreement between Landsat-8 and Sentinel-2, resulting in a 0.63%–27.83% and 7.16%–21.36% reduction in RMSE for TOA and SR, respectively. The findings of this study were highly useful for the collaborative utilization of Landsat-8 and Sentinel-2 data within GEE for the applications requiring high temporal observation frequency at medium spatial resolution.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) has been widely investigated with satellites/sensors covering the spectral range of SIF emission with fine spectral resolutions. The potential precision of SIF retrievals is limited by instruments’ spectral specifications. Here, the influence of spectral characteristics (spectral resolution (SR), signal-to-noise ratio (SNR), and spectral coverage) on data-driven SIF retrieval was assessed using simulations, and the SIF retrieval capability of the obsolete, in-orbit, and planned satellites was evaluated from the spectral perspective. As a result, the 757–759nm and 735–758nm fitting windows were found to be optimal for far-red SIF retrieval on satellites with fine (-2sr-1nm-1, followed by satellites with moderate SRs (0.1–0.5 nm) such as CO2M, TROPOMI and GOME-2 (RMSE*-2sr-1nm-1). The high SNR of SCIAMACHY did not improve the far-red SIF retrieval greatly (RMSE* = 0.47 mW m-2sr-1nm-1) but obtained a low RMSE* at the red band (0.43 mW m-2sr-1nm-1). The highest red SIF retrieval potential was found on TROPOMI, with an RMSE* of 0.41 mW m-2sr-1nm-1. TEMPO and FLEX gave poor performance (RMSE* > 0.9 mW m-2sr-1nm-1) with their current spectral specifications. Improvement of the spectral characteristics is still needed to obtain precise SIF retrievals.

Forwarding is the keyway for information propagation in social networks that affected by complex factors. Due to the fact that the forwarding mechanisms are essentially unclear, this paper focuses on the formation and evolution of the external as well as internal driving mechanisms and develops a Gaussian dynamic topic model for forwarding prediction by incorporating all the information of nodes and edges. First, based on the diversity of communities each user located in, latent Dirichlet allocation (LDA) traditional text modeling method is applied to user following relationships modeling that leads to the initial formation of communities, and user interacting relationships modeling that leads to the evolution of communities. Taking the advantage of LDA topic model in dealing with the problem of polysemy and synonym, we can mine user latent distribution over communities and analyze the external community driving effects. Second, considering the differences in individual habits, time factor is introduced and the dynamic topic model is proposed to model user behavioral attributes. Meanwhile, in regard to continuous user attributes modeling, the parameter of topic-word distribution in the topic model is replaced by multivariate Gaussian distributions, which shown to be effective at capturing the regularities of individual behavioral habits and analyzing the internal individual driving effects. Finally, combining with external and internal factors, a probabilistic graph model is used to modeling forwarding behavior. We propose methods based on Gibbs sampling and an expectation–maximization algorithm to estimate model parameters and fit our model to predict user forwarding actions. Experimental results indicate that the model can not only detect the latent communities but also can improve the performance of forwarding prediction effectively.

Land cover change mapping using remote sensing data, especially medium-resolution imagery, has often been constrained by lack of high-quality training and validation data, especially for historical satellite images. In this study, we presented a new automatic classification approach for Landsat imagery based on time-series image spectral database (TSID). Firstly, a new atmospheric correction procedure was performed on Landat images to retrieve the surface reflectance. Secondly, the TSID with a spatial resolution of 1.5°×1.5° was built using the MODIS reflectance product for each land cover type in the GlobCover 2009 mapping. Thirdly, the spectral signature in the TSID was employed to build a generalized classifier, and was tested on a time series of five Landsat TM images of the Tibetan Plateau. The overall accuracy achieved was between 88.35% and 94.25%, which is comparable to the results obtained using traditional scene-by-scene supervised classification. Finally, the new TSID classification approach was tested on 510 Landsat OLI images for the whole China, the preliminary result showed its promising potential for global land cover mapping with fine resolution, such as 30m.

Solar-induced chlorophyll fluorescence (SIF) is related to photosynthesis and can serve as a remote sensing proxy for estimating photosynthetic energy conversion and carbon uptake. In this paper, three key factors affecting the relationship between SIF and gross primary production (GPP) were investigated using both models and observations on winter wheat (C3 crop) and maize (C4 crop). Firstly, the bidirectional SIF emission was investigated by multi-angular spectral measurement, which was found to be similar to that of the canopy reflectance in the solar principal plane. Secondly, the wavelength dependent predictive power of SIF to estimate GPP was assessed using diurnal observations on winter wheat. As indicated by the preliminary studies, the far-red SIF may be more reliable for remote sensing of GPP than the red SIF due to the heterogeneous and diverse vegetation growth status at regional or global scale. Finally, the potential of far-red SIF to track the diurnal and seasonal variations in GPP for C3 and C4 crops was investigated, and the result show that the GPP SIF relationship is dependent on the type of photosynthesis.

Numerous observations and modeling results have shown that there is noticeable directional variation in the solar-induced chlorophyll fluorescence (SIF), and this has not been well investigated. In this paper, 16 multiangular spectral observations were carried out on winter wheat to assess the bidirectional SIF emission. First, the bidirectional SIF emission was retrieved from the spectral measurements made by a high-performance QE Pro spectrometer and an automatic multiangle observation system using the 3FLD algorithm. The bidirectional shape of the SIF emission was found to be similar to that of the canopy reflectance in the solar principal plane, with a mean correlation coefficient of 0.94 and 0.97 at the O2-B and O2-A bands, respectively. The modified Rahman-Pinty-Verstraete (MRPV) model, a semiempirical bidirectional reflectance distribution function (BRDF) model, was then employed to describe the bidirectional variation in the SIF and reflectance with a mean root-mean-square-error value of 0.036 and 0.041 mW m-2sr-1nm-1for the SIF at the O2-B and O2-A bands, respectively. Finally, both the bidirectional reflectance and SIF were BRDF corrected to nadir using the MRPV model. Most of the directional variation was successfully corrected by this method-the mean correction ratios were 87% and 81% for the reflectance at the O2-B and O2-A bands and 84% and 72% for the SIF at the O2-B and O2-A bands, respectively. Therefore, the SIF emission cannot be regarded as isotropic, and the high similarity between the bidirectional SIF and reflectance, together with the BRDF correction results, indicates that the bidirectional SIF emission can be adjusted using either the BRDF reflectance models or prior knowledge.

Methods based on Fraunhofer line discrimination (FLD) for solar-induced chlorophyll fluorescence retrieval require making assumptions or estimations about true reflectance, which is a large source of error, particularly at the O2-B band. This letter presents an alternative solution, which is named the pFLD method, based on principal components analysis to reconstruct the reflectance spectra. The principal components were generated with simulated reflectance spectra covering the most common conditions of vegetation. The pFLD method has been tested with both simulated and field experiment data sets. Compared with the widely used three-band FLD and improved FLD methods, pFLD showed better performance at the O2-B band, particularly when the spectral resolution (SR) or the signal-to-noise ratio was relatively low. The pFLD method can be also successfully applied to field measurements with credible accuracy even at low SR.

The transpiration from shelterbelts and the evapotranspiration (ET) from cropland (maize and vegetables) and orchards (apple) in an irrigated area in the middle reaches of the Heihe River, China, were estimated using a modified Penman-Monteith (P-M) formula and airborne remote sensing data. The results were compared to the shelter transpiration results obtained from measurements of sup flow in tree trunks made with thermal dissipation probes and the latent heat fluxes observed by the eddy covariance technique at flux towers in croplands. The modified P-M formula was found to be an effective means to estimate not only the cropland and orchard ET but also the shelter transpiration. The seasonal variation of shelterbelt transpiration was smaller than those of cropland and orchard ET. Estimates of ET made using the P-M formula along with the remote sensing data showed that 9.9%, 3.1%, and 87.0% of the total ET were allotted to shelterbelts, apple orchards, and cropland, respectively.

This study presents a comparison of FPAR derived from GIMMS NDVI and MODIS product, as the preparation research for generating the global FPAR from 1981 to present. Eurasia was selected as the study area. First, we estimated FPAR in 2006 by the NDVI-based approach, and then we compared the spatial and seasonal patterns of estimating FPAR with the MODIS standard product. The results showed that: the GIMMS NDVI SR FPAR show the consistent dynamics spatial and seasonal dynamics with MODIS FPAR, and two datasets capture the seasonal variation well. MODIS FPAR values are larger than GIMMS NDVI SR FPAR, especially for the forest vegetation. The differences among of two datasets can also be partly attributed to the biome specific MODIS algorithm and NDVI-based approach in this study.

It is not completely clear that the spatial change of the green house gases such as CO2concentration, CH4concentration and their changes with the land ecological variation because there are not enough observational data available. A preliminary result investigating the spatial distributions variation of CO2and CH4concentrations in China using observed data by The Greenhouse Gases Observing SATellite (GOSAT), were demonstrated. The 1-km grid landuse data and the CO2dry air mixing ratios (XCO2) and CH4dry air mixing ratios (XCH4) inversed from GOSAT FTS/SWIR data were used. The results showed that the total average of XCO2 is 371ppmv, XCH4 is 1.72ppmv based on statistics of the entire GOSAT observing points from April 2009 to March 2010 in China. XCO2 over the urban and built-up areas and farmlands showed larger concentrations than that over the forest and grasslands.

This study presents a novel passive-detection method to discriminate between the C3 and C4 plant functional types using the solar-induced ChlF. The solar-induced ChlF radiation was extracted from airborne hyperspectral data acquired by the OMIS II instrument. Our results showed that the fluorescence signal of C4 species was about 2.2 times greater than that of C3 species at the same NDVI level. The spectral separation rate of the solar-induced ChlF was 1.31, but the maximum value of the spectral reflectance and NDVI was only 0.63 at the 750 nm band. A simple decision tree was built to discriminate between the C3 and C4 species based on the difference between their ChlF. An accuracy assessment indicated that the C3 and C4 species had been well classified, with an overall classification accuracy of 92% and a kappa coefficient of 0.84. Although it is quite challenging to classify the C3 and C4 species based on the reflectance signal, our results present a novel method for successfully discriminating between the C3 and C4 species.

As a key parameter representing the outgoing solar flux fractions reflected by earth surface, the albedo of land surface has been strongly altered by the change of land covers, especially the increase of the impervious surface caused by the urbanization. This paper discussed and demonstrated the albedo changed with the impervious increases around Beijing-Tianjin urban region, where land cover type has been strongly changed since the 1980s, especially a large scale of buildings and roads increased in Beijing 2008 Olympic Games. We extracted the variation information of albedo from MODIS data as well as the impervious changes by using two scenes Landsat Thematic Mapper images. The relationship between the change pattern of albedo and the impervious surface was discussed and especially the regions within the five rim of Beijing urban, and the surrounding areas along two Jing-Jin highways were paid more attention to. It was found that the variation of the near infrared albedo albedo in 2008 shows an obvious change with the increase of impervious surface, while the change is not apparent in the visible band.

This paper focuses on the methodology of estimating biophysical and biochemical parameters and yield of winter wheat based on Landsat TM images. In order to develop the method of retrieving wheat parameters from remote sensing data, five Landsat TM images were acquired respectively at erecting stage, jointing stage, heading stage, early grain-filling stage and grain-filling stage of winter wheat. Experiment sites' wheat biophysical and biochemical parameters such as biomass, LAI, chlorophyll, nitrogen content and yield were measured. Based on the TM images, vegetation indices such as NDVI, OSAVI, MSAVI EVI, SIPI and NDWI were calculated. Then the correlation coefficients between wheat parameters and spectral indices of the experiment sites were computed. According to the correlation coefficients, the optimal spectral indices for estimating wheat parameters were determined. The best-fitting method was employed to build the relationship models between wheat parameters and the optimal spectral indices. Finally, the models were used to estimate wheat biophysical and biochemical parameters and yield based on Landsat TM data. Research results show wheat parameter estimation models possessed more R2values than 0.615.

The effect of crop leaf angle on the 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 paper, experiments on winter wheat (Triticum aestivumL.) were conducted to identify the crop leaf angle distribution (LAD) by two-temporal (erecting and elongation stages) and bidirectionalin situreflected spectrum and the Airborne Multiangle Thermal Infrared (TIR) Visible Near-Infrared (VNIR) Imaging System (AMTIS) images. The distribution characters of the leaf angle for different LAD varieties were expressed using the beta-distribution function and the SAILTH radiative transfer models. The proportion of the leaf angle in 5deg angle classes (from 5deg to 90deg) for erectophile, planophile, and horizontal varieties was dominated by 75deg, 55deg, and 35deg. The different LAD varieties had a similar canopy reflectance in 680 nm (red) and 800 nm (near-infrared band) at the erecting stage, while they had significant differences at the elongation stage. The ratio of the canopy reflectance of 800 nm at the erecting stage [R800(B)] to the canopy reflectance of 800 nm at the elongation stage [R800(A)] was used to identify the different LAD varieties through the selected two-temporal canopy reflectance. A method based on the semiempirical model of the bidirectional reflectance distribution function (BRDF) was also introduced in this paper. The structural parameter-sensitive index (SPEI) was used in this paper for crop LAD identification. SPEI is proved to be more sensitive to identify erectophile, planophile, and horizontal LAD varieties than the structural scattering index and the normalized difference f-index. We found that it is feasible to identify horizontal, planophile, and erectophile LAD varieties of wheat by studying two-temporal and bidirectional canopy-reflected spectrum

A novel instrument to measure photochemical reflectance index (PRI) and nitrogen reflectance index (NRI) is presented. It can measure the incident and reflected radiance of vegetation at 531 nm, 570 nm and 670 nm bands by the 6 specially designed interference filters, then PRI and NRI is derived. The PRI index can be used for the diagnosis of crop stress, such as waterlogging and drought. The NRI can be used for the diagnosis of crop nitrogen nutrition deficiency. The instruments can diagnose the plant growth status by the acquired spectral response. This optical instrument includes photoelectric detector module, signal process and A/D convert module, the data storing and transmission module and human-machine interface module. The detector is the core of the instrument which measures the spectrums at special bands. The microprocessor calculates the PRI and NRI value based on the A/D value. And the value can be displayed on the instrument's LCD, stored in the flash memory of instrument and can also be uploaded to PC through the PC's RS232 serial interface. The prototype was tested in the crop field at different view directions. The PRI and NRI data acquired by the novel instrument are compared and calibrated by an ASD FieldSpec spectrometer, which shows that this instrument is successfully developed and reliable. The winter wheat's leaf area index, water content, chlorophyll density and nitrogen content are successfully detected by the PRI and NRI instrument, this novel instrument is portable and could be fixed on the agricultural machine traveling in the field, which demonstrates the promising application of the novel portable instrument.

The delineation of management zones is an economical and effective measure for the variable-rate application in precision agriculture. The methods of empirical and unsupervised classification have been used by many researchers in the delineation of management zones, but these methods are only built upon the information of attributes in every spatial cell, and the spatial relationships and their spatial interaction between cells are not considered. AS a result, there are many isolated cells or patches in the zoned map, this is not advantageous for the operation of the variable-rate application. Based on the traditional k-means cluster (K-M) and the spatial autocorrelation, a new method, spatial contiguous k-means clustering algorithm (SC-KM), was developed in this study. According to the spatial variability of wheat growth under within-field level extracted from OMIS image of the key growth stage, management zones were delineated by using K-M and SC-KM methods. Two evaluation indices were employed to evaluate the zoned results of the above mentioned two methods .The results showed that the sum of the weighted variance of the corresponding within-zones based on the two methods appeared no significant difference, and that the SC-KM method could remove lots of isolated cells or patches and improved the continuity of the corresponding management zone map, compared with the K-M method. The zoned result based on the SC-KM method can be used as the variable management unit for precision agriculture and can be used to advise the sampling of subsequent soil or crop.

It is difficult to quantify the amount of chlorophyll fluorescence emitted by a leaf or canopy under natural sunlight because the reflected light obscures the fluorescence signal. In this study, two diurnal experiments were conducted on winter wheat (Triticum aestivum L.) and Japan Creeper (Parthenocissus tricuspidata) to detect the solar-induced chlorophyll fluorescence from field radiance spectra. In the separation of the fluorescence emissive signal from canopy radiance spectrum based on Fraunhofer lines, two Fraunhofer lines of the terrestrial oxygen absorption at 688 and 760 nm were observed in the radiance spectra by an Analytical Spectral Devices FieldSpec Pro NIR spectrometer, which largely overlaps the chlorophyll fluorescence emission spectrum of leaves. Therefore, Fraunhofer lines at 688 and 760 nm were selected to detect the emissive fluorescence. The diurnal changes of chlorophyll fluorescence in the two experiments were primarily affected by the diurnal changes of photosynthetically available radiation (PAR). The correlation coefficients (R/sup 2/) were greater than 0.9 for all the relationships between PAR and the solar-induced fluorescence of winter wheat and Japan Creeper at 688 and 760 nm based on Fraunhofer line-depth (FLD), suggesting that the solar-induced fluorescence could closely track the changes of PAR and chlorophyll fluorescence. The relative solar-induced fluorescence based on FLD was negatively related to Fv/Fm measured by an OS1-FL modulated chlorophyll fluorometer. The correlation coefficients (R/sup 2/) were 0.97 at 688 nm and 0.99 at 760 nm for winter wheat, and 0.79 at 688 nm and 0.78 at 760 nm for Japan Creeper. These results demonstrate that the solar-induced fluorescence from plant canopies can be detected from field radiance spectra based on the Fraunhofer line principle.

In the first part of this article, traditional methods for deriving city vegetation information through remotely sensed images, the study of the area's characteristics and the research flow are introduced. In this article, two main issues are studied, one is city shadow correction, the other is the classifying method of city vegetation through remote sensing images. The biggest problem of high-resolution images is the shadow of high buildings. City shadows decrease the classification accuracy, so a shadow adjustment method must be studied. In this article, we analyze the radiation correction model, and acquire the shadow adjustment model. The model's parameters can be calculated through the image pixel values. The result of shadow correction shows that the model can correct for city shadow. After shadow correction, the vegetation's pixel value in the shadow is similar to the pixel value of vegetation in other areas. Shadow correction increases classification accuracy. In the second part of this article, three methods are studied to derive city vegetation information, including the vegetation index method, a back propagation neural network method, and a texture method. Finally, the three methods' classification accuracies are calculated and appraised. A conclusion is drawn, which is that the texture classification method is a good classification method. The accuracy of texture classification method can reach 91.53%.

Recently, there were many strong sandstorms occurred in North China, which became a severely economical, social and environmental problem, especially for Beijing City. The bare soil is an important source of sandstorm, and the croplands naked in winter in 55 counties around Beijing City became the main source of the sandstorm. It is very important to monitor the seasonal bare soil areas using remote sensing data. The multitemporal TM images of Beijing area in May 16, 1997, April 7, 2003, May 25, 2003, October 24, 2003 and January 28, 2004 were selected. Firstly, the smoothing filter-based intensity modulation (SFIM) completed the image fusion. Secondly, the multistage classification approach was adopted. The multisource data, such as DEM, NDVI, bare soil index (BI), shadow index (SI), were integrated and applied in classification, and the seasonal bare soil areas, such as wasteland, cropland, were successfully extracted. Thirdly, the seasonal and inter-annual dynamic changes of the bare soil areas were also analyzed.

This work focused on developing the appropriate spectral index to monitor yellow rust disease, which is relatively insensitive to species, canopy structures (such as leaf area index and leaf distribution angles), foliar inner structures, and soil condition variations. Firstly, canopy spectra and canopy chlorophyll concentration were acquired during growth duration, and the relationship between SPAD value and measured chlorophyll concentration was established. It indicated that the logarithmic relationship exists between TCARI/OSAVI and SPAD value, with a coefficient of determination R/sup 2/ = 0.7795 (n = 320). Therefore, the combined index (CCII) can be used to minimize the effects of LAI and no photosynthetic materials on the retrieval of chlorophyll concentration at canopy level. Secondly, The photochemical reflectance index (PRI) was chosen for disease index inversion by minimizing internal leaf structure effect, has and the linear negative relationship exists between the normalized photochemical reflectance index (NPRI) and disease index (DI), with a coefficient of determination R/sup 2/ = 0.8477 (n = 63), so the normalized photochemical reflectance index (NPRI) can be used to monitor the disease index of yellow rust.

The objective in this study was to develop proper vegetation indices for prediction of soil irrigation demanding under vegetation covering conditions. The traditional method for the winter wheat yellow rust field survey is time consuming. It was discussed of the selection method of characteristic spectral bands and the establishing of inversion model to monitor winter wheat yellow rust using hyperspectral data in this study. The correlation coefficients between selected vegetation index and disease incidence (DI) at infected stages. Inversion models between DI and vegetation index such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), transformed vegetation index (TVI) were used to monitor yellow rust. The multi-temporal hyperspectral airborne images were acquired from winter booting stage to milking stage, and the yellow rust disease of winter wheat was analyzed using hyperspectral images. Compared with healthy wheat, spectral reflectance of disease wheat was higher in 560-670 nm bands but lower in near infrared bands and the absorption depth of chlorophyll in red band and reflectance peak in green band are relatively reduced. A novel spectral index for yellow rust indices was presented, and the degree and area of yellow rust disease were successfully remotely sensed from the multi-temporal hyperspectral data based on spectral index.

Remote sensing offers the potential to determine rapidly the physiological condition of crop over large areas. Canopy reflectance data selected at key growth stages of winter wheat were analyzed. It indicated that winter wheat growth phonological changes can be evaluated by red edge variables (REV) characteristics. Regression equations between chlorophyll concentration, nitrogen concentration, soluble sugar were established, prediction of foliar soluble sugar and chlorophyll concentration by red edge position, foliar nitrogen concentration by the amplitude of red edge, starch concentration by the near infrared plateau, leaf area index by the area of red edge peak are successful and feasible. For this purpose red edge variables can play a vital role in providing time-specific and time-critical information for precision farming, due to their capabilities in measuring canopy spectrum variability.

NDVI is the most widely applied vegetation index, it can indicate the information of growth status, LAI, et al. However, NDVI is also affected by the atmosphere conditions, soil background, view conditions and so on. In this paper, we analyses the effects of chlorophyll, surface soil moisture, the ratio of sky light, view zenith and Sun zenith on NDVI under different LAI using the leaf radiative transfer model PROSPECT and canopy reflectance model SAIL, and find the sensitive factors and ranges.

In recent years, as the availability of remote sensing imageries of varying resolution has increased, merging images of differing spatial and spectral resolutions has become a significant operation in the field of remote sensing. With the development of quantitative remote sensing, not only improving spatial details but also preserving the spectral information of multispectral bands were required. The principle, algorithm and methods of two fusion algorithms, SFIM (Smoothing Filter-based Intensity Modulation) and Gram-Schmidt (Gram-Schmidt transform), were described, also advantage and disadvantage of them were discussed. In a case of Ikonos image in city, visual judgment, quantitative statistical parameters and graph method were used to qualitatively and quantitatively assess these two algorithms, also compared to the traditional fusion methods of IHS transform and PC (principal component) transform. The results showed that there was no distinct difference in spatial details improved. However in terms of spectral information fidelity, both IHS and PC method were the worst, Gram-Schmidt method was better, while SFIM method was the best.

The ground cover of vegetation indicates light interceptor of plant, and plant productivity. The red and near-infrared vegetation indices have been employed to predict the ground coverage of crop, however these vegetation indices were much affected by uncertain factors such as leaf color, crop cultivar and others. This work focused on the feasibility of predicting ground cover by near infrared and mid-infrared spectral indices, which were little sensitive to cultivar, fertilization, irrigation and leaf color. The digital photographs of wheat canopy were taken vertically 1.5 m aboveground and then spectra were measured by an ASD Fieldspec FR2500 spectrometer. The ground cover was automatically extracted by a novel image processing procedure. 8 diagnostic bands in infrared and short-infrared region were selected to calculate 56 ratio indices and 28 normalized difference indices. Also the popular 7 red and near-infrared vegetation indices were calculated. The general linear model (GLM) was applied to assess the relationship between ground coverage and spectral indices, and to evaluate whether they were sensitive to cultivar, fertilization, irrigation and leaf color. Results showed that red and near infrared vegetation indices to predict ground coverage were affected by at least one of the four factors mentioned above, however it is promising that some infrared indices, such as R1690/R1450, R1450/R1690, R1450:R1690, predict ground coverage well and not sensitive to cultivar, fertilization, irrigation and leaf color.

The objective in this study was to develop the time-specific and time-critical method to overcome the limitations of traditional field sampling methods for variable rate fertilization. Farmers, agricultural managers and grain processing enterprises and interested in measuring and assessing soil and crop status in order to apply adequate fertilizers quantities to crop growth. This work focused on studying the relationship between SPAD readings and crop chlorophyll concentration and/or nitrogen content to determine the amount of nitrogen fertilizer recommended for variable rate management in precision agriculture. The recommended rate of conventional uniform rate fertilizer management was higher than that of variable management. The grain yield, ear numbers, 1000-grain weight and grain protein content were measured between CK and variable rate fertilizer treatments. It indicated that variable rate fertilization reduced the variability of wheat yield, ear numbers and 1000-grain weight, but it didn't increased crop yield and grain protein content significantly, compared to traditional nitrogen application. The nitrogen fertilizer use efficiency was improved, for this purpose, the variable rate technology (VRT) based on SPAD readings could be used to prevent the water pollution and environmental deterioration.

The seedtime plays an important role on the growth, yield and quality of winter wheat. Firstly, the seedtime was successfully remotely sensed by NDVI derived from a LandSat TM image captured in elongation stage. Secondly, an optimized yield estimation model was designed based on NDVI and seedtime, and the optimized model was successfully tested by 3 LandSat TM images captured in heading, grain filling and milking stages.

A method for estimating grain quality of winter wheat using Landsat Thematic Mapper data and GIS data is presented, and it gave the development and application of the Geographical Information Application system for estimating wheat grain quality. The system, supported by COMGIS, JModel-Base Management System and other advanced Spatial Information Technologies, can deal with the fusion analysis of remotely sensed data, GIS data and other multisources data, flexible management of models and knowledge

PHI (pushbroom hyperspectral imager) is a hyperspectral imaging sensor which was developed by Chinese Academy of Science. From April to May of 2002, three airborne PHI images were obtained over a winter wheat field near Beijing. In this paper, wheat yield prediction models are established using PHI spectrum information and after-harvest grain yields data. Within-field yield variability was mapped for entire wheat field based on the models. A yield map was produced from the combine harvest yield data to evaluate those models. The analysis results indicates that (1) hyperspectral remote sensing data could be used to estimate yields in winter wheat yield, (2) new and more effective indicators are needed to capture more of the factor's that affect crop yield, and such parameters should be extracted from remote sensing data

There is considerable interest in assessing leaf area index (LAI) to evaluate crop growth and production. In this article, an operational approach was proposed to evaluating LAI of summer maize for different cultivars under different nitrogen treatments and developmental stages by selecting ten familiar remote sensing vegetation indices (VIs). The result indicated that VIs had the potential for faithfully estimating LAI, and the estimation power of VIs for assessing LAI was best from bell stage to silking stage, which primarily depended on the LAI dynamic variation during the process of the growth. When the estimation power of VIs was systematically verified with the other independent data set, the VIs could accurately evaluate LAI. The ratio spectral index of R/sub 810//R/sub 560/ was the best index to estimate LAI, which was wondrously sensitive to LAI dynamic variation almost without the influence of cultivars, growth stages and nitrogen treatments. The exponential regression model for LAI based on R/sub 810//R/sub 560/ was also established with a mean determination of coefficient (R/sup 2/) of 0.9573 (P value=0.01) and a mean root mean square error (RAISE) of 0.0365. Therefore, the spectral index of R/sub 810//R/sub 560/ could be considered as a sensitive indicator as LAI of summer maize.

The objective in this study was to develop proper vegetation indices for prediction of soil irrigation demanding under vegetation covering conditions. The four tested winter wheat varieties were under four different irrigation treatment levels at two different growth stages, one at booting stage and another at grain filling stage, the winter wheat canopy and bare soil reflected spectrum, foliar and soil moisture condition were tested in the field at an interval of 7 days from erecting stage to ripening stage. The combined spectral indices of photochemical reflectance index (PRI) and normalized difference water index (NDWI) were chosen in this study. Regression equations between the ration of NPRI to NDWI and soil water content (SWC) were found to be most appropriate for assessing soil water condition under vegetation covering, and relationship between the ration of PRI to NDWI and soil irrigation demanding conditions were also studied. It indicated that soil irrigation demanding condition under vegetation covering conditions could be evaluated by a pragmatic approach for canopy level estimation with proper combined vegetation index

With the urbanizing development of Beijing, the planting area and spatial distribution structure of Beijing suburb crops are changing constantly. Because the TM (Thematic Mapper) images of Beijing area during 2003 summer suffered from weather conditions, such as cloud or haze, it is difficult to obtain the satisfying classification with only one kind of remote sensing data. To get the first data of the exact area and spatial distribution of Beijing crops, a perfect classification strategy is designed in This work. The images of 2003's TM/ETM+ (Enhanced Thematic Mapper Plus) in comparative better weather conditions, MODIS (Moderate Resolution Imaging Spectroradiometer), NDVI (Normalized Difference Vegetation Index) standard products in 2003 and DEM (Digital Elevation Model) at 30 m spatial resolution of Beijing area are adopted. The classification system for main crops in Beijing is constructed which includes winter wheat, maize, soybean, clover, garden, orchard, etc. Based on the phenological features of the main crops, the different crops extracting plan is constructed respectively. The orchard and vegetable in greenhouse planting information are from the supervised classification with maximum likelihood methods. By means of the decision tree classification function of the ENVI (The Environment for Visualizing Images) software, the winter wheat and clover planting information are extracted by the logic operation algorithm among the 4 NDVI data from the TM/ETM+ images adopted in This work while the spring maize and soybean are extracted with the logic algorithm with MODIS NDVI. The results indicated that this classification strategy can not only improve the classification precision, but also decrease the post classification work.

In this study, the selection method of characteristic spectral band and the establishing of inversion model to monitor winter wheat stripe rust using hyperspectral data is discussed. The correlation coefficients between the DI (disease incidence) at different stages of infection and the initial canopy reflectance spectral and the derivative of the reflectance spectrum were compared, respectively. The results showed that the derivative of the reflectance spectra has reached higher significant level with the DI than the initial reflectance spectral data. The initial reflectance in the visible light 680 nm wavelength and the near infrared 976 nm, 1010 nm wavelength were selected to do regression with the DI of winter wheat stripe rust. And some inversion models between the DI and the hyperspectral data or its conversion patterns like NDVI (Normalized difference vegetation index), RVI (Ratio vegetation index), TVI (Transformed vegetation index) and its differential values of the canopy spectral reflectance data to monitor winter wheat stripe rust were established. Meanwhile, those correlation coefficients were compared respectively, of which we found the pattern of vegetation index has more efficient commonly than initial canopy spectral reflectance data by aggression analysis with the DI. The paper also suggested that the possibility of developing a special visible/near-infrared sensor for the detection of the DI of winter wheat stripe rust theoretically. Else, the SRSI (stripe rust stress index) mechanism model was presented for the first time in This work.

The wheat grain quality and its influence were introduced, and the mechanism and methods to forecast grain quality were studied. Firstly, the leaf nitrogen content at anthesis stage was proved to be an indicator of grain protein content, and the spectral indices significant correlated to leaf nitrogen content at anthesis stage were the potential indictors for grain protein content. The vegetation index. VIgreen, derived from the spectral reflectance at green and red bands, was significant correlated to the leaf nitrogen content at anthesis stage, and also high significant to the final grain protein content. Secondly, the environment stress, such as irrigation, fertilization, temperature, had important influence on grain quality. The irrigation stress can increase grain protein content. The leaf water content depends on irrigation levels, therefore, the spectral indices correlated to leaf water content were also potential indictors for grain quality. The spectral reflectance at SWIR band and other water indices at seeding filling stage were proved to be high significant correlate to grain protein. Finally, the predicting models of grain protein content were built based on the transfer principle of leaf nitrogen content and the effect of irrigation stress

This paper mainly proposed using multi-temporal spectral data with given weight to estimate yield using contribution ratios of different stages to improve yield estimation, then use stepwise regression to build models for yield estimation. The contribution ratio is calculated by principal component analysis respectively. Result show that this methodology reflects the crop growth status, physiological characters at different stages, and improves the accuracy obviously.

Remote sensing of vegetation liquid water has important application in agriculture and forestry. The spectral index or features of water absorption in NIR and SWIR have been found useful for the detection of plant water content (PWC). It is unfortunately that the foliar liquid water absorption is superposed by the atmospheric vapor absorption, and it is very difficult to distinguish the contribution of foliar liquid water and atmospheric vapor on the water absorption spectral features. In this study, the novel methods using red edge width, which located outside of water absorption bands, were proposed for deriving winter wheat PWC from field canopy spectra. The significant positive correlation coefficients were observed between PWC and spectral reflectances in 740-900nm region for all the 6 growth stages. And this mechanism also affects the spectral red edge in 680- 740nm region. The correlation coefficients between PWC and red edge width derived from inverted gaussian model is significant at 0.999 confident level, and we established the statistical models for PWC by red edge width in all the 6 growth stages.

At XiaoTangShan Precision Agriculture Experiment Base, suburban of Beijing city, we obtained the hyperspectral data of wheat canopy by field measurement and synchronal relative foliage water content (RFWC) at lab, according to the different growth stage of wheat, in the spring of 2002. In this paper, we extracted spectral feature parameters firstly. Then, the correlation analysis between the spectral features parameters and RFWC was made. two spectral feature parameters, which have good relativity to foliage water content, to make linear regression models between RFWC and spectral feature parameter according to the growth stages of wheat.

Spectral data are strongly related to canopy parameters, which are related to final yield at a critical stage of the crop growth, so it is possible to estimate wheat yield from spectral data. In this paper, firstly, the yield data were related to the field hyperspectral reflectance data in 8 different growth stages from regreening stage to milking stage. The statistical results show that yield is positive correlative with spectral reflectance in NIR bands and negative correlative in visible and SWIR bands. Secondly, the normalized difference spectral indices defined by [860 nm, 1200 nm], [890 nm, 980 nm], [820 nm, 1650 nm], [820 nm, 1650 nm] and [820 nm, 2200 nm] are designed and related to yields in all the 8 growth stages. Compared with NDVI, these spectral indices can predict yield earlier and more reliably. Finally, the remote sensing models in different growth stages for yield were built by [820 nm, 1650 nm] weak water absorption index.