Wang Jindi

Inversion of foliar biochemical parameters at various physiological stages and grain quality indicators of winter wheat with canopy reflectance

Canopy reflectance data selected at key growth stages of winter wheat were analysed. Regression equations between foliar total nitrogen content and other foliar biochemical concentrations, dried biomass indices, and grain quality indicators were established. The results showed that there were robust correlations between foliar total nitrogen content and other foliar biochemical concentrations, dried biomass indices, and grain quality indicators. The predictions of soluble sugar content, foliar water content, stem water content, foliar starch content, foliar dried weight, plant dried weight, and Leaf Area Index (LAI), etc. by foliar total nitrogen content were successful and feasible. The predictions of grain protein and dry gluten content by foliar total nitrogen content at anthesis were surprisingly good. The wavelength bands related to foliar total nitrogen content selected by regression equation were located at 1000–1140 nm and 1200–1300 nm.

Study on NDVI-T s space by combining LAI and evapotranspiration

This paper focuses on interpreting the different spatial relationships between NDVI and Ts, a triangular or a trapezoid, and on analyzing transformation conditions, the physical and ecological meanings of the vegetation index-surface temperature space as well. Further, we use the Temperature-Vegetation Dryness Index (TVDI) to explain the existent meaning of a triangular space after NDVI reaches its saturated state by employing the relationships between NDVI, LAI and evapotranspiration. The specific relations between NDVI and Ts are useful for describing, validating and updating land surface models.

High spatial-and temporal-resolution NDVI produced by the assimilation of MODIS and HJ-1 data

The normalized difference vegetation index (NDVI) is widely used in global environmental and climatic change research. However, the 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) is too coarse to quantify changes in heterogeneous landscapes. On the other hand, the 30 m charge-coupled device (CCD) sensor on the Chinese environment satellite (HJ-1) is severely affected by weather, which limits its use in studying the biophysical processes evolving rapidly during the growing season. In cloudy areas, the problem is compounded; only a few images can be obtained for the whole year. It is therefore impossible to obtain the high temporal spatial resolution NDVI required in some applications. To solve this problem, the continuous correction (CC) data assimilation method was proposed to produce high temporal spatial resolution NDVI by combining the advantages of the MODIS temporal information and the CCD spatial information. The MODIS 16 day compositing/8 day windows Nadir BRDF-Adjusted Reflectance and the CCD reflectance were used to predict 8 day/30 m NDVI for the Heihe River basin, China, in 2009. Comparison of predicted data with field data showed that the two were in good agreement. The method demonstrated feasibility, and the NDVI produced provided better vegetation information. The performance of CC depended on the acquisition time and amount of the CCD images.

The definition of effective emissivity of land surface at the scale of remote sensing pixels

Remote sensing (RS) of land surface temperature (LST) is a very challenging problem at the present development stage of RS science. Tremendous efforts have been devoted to atmosphere correction and temperature emissivity separation (TES) of new LST product algorithms. However, the mechanism of directionality of thermal emission from land surface remains unknown, and even worse, there are confusions on the definition of the effective emissivity of land surface at the scale of RS pixels. The mechanism of directionality of thermal emission for isothermal pixels and non-isothermal pixels is different. For non-isothermal pixels (case in most canopy/soil structures), the directionality of their thermal emission is determined by both bidirectional reflectance distribution function (BRDF) derived emissivity and distribution patterns of temperature differences. A new definition is suggested to take into account material mixture, multiple scattering, and temperature variation within thermal infrared (TIR) RS pixels.

Application of ensemble kalman filter to geophysical parameters retrieval in remote sensing: A case study of kernel-driven BRDF model inversion

The use of a priori knowledge in remote sensing inversion has great implications for ensuring the stability of inversion process and reducing uncertainties in retrieved results, especially under the condition of insufficient observations. Common optimization algorithms have difficulties in providing posterior distribution and thus cannot directly acquire uncertainties in inversion results, which is of no benefit to remote sensing application. In this article, ensemble Kalman filter (EnKF) has been introduced to retrieve surface geophysical parameters from remote sensing observations, which has the capability of not merely obtaining inversion results but also giving its posterior distribution. To show the advantage of EnKF, it is compared to standard MODIS AMBRALS algorithm and highly efficient global optimization method SCE-UA. The inversion abilities of kernel-driven BRDF models with different kernel combinations at several main cover types are emphatically discussed when observations are deficient and a priori knowledge is introduced into inversion.

Principle and application of three-band gradient difference vegetation index

Vegetation index is a simple, effective and experiential measurement of terrestrial vegetation activity, and plays a very important role in qualitative and quantitative remote sensing. Aiming at shortages of current vegetation indices, and starting from the analysis of vegetation spectral characteristics, we put forward a new vegetation index, the three-band gradient difference vegetation index (TGDVI), and established algorithms to inverse crown cover fraction and leaf area index (LAI) from it. Theoretical analysis and model simulation show that TGDVI has high saturation point and the ability to remove the influence of background to some degree, and the explicit functional relation with crown cover fraction and LAI can be established. Moreover, study shows that TGDVI also has the ability to partly remove the influence of thin cloud. Experiment in the Shunyi District, Beijing, China shows that reasonable result can be reached using the vegetation index to retrieve LAI. We also theoretically analyzed the reason why the normalized difference vegetation index (NDVI) owns the low saturation point, and show that it is determined by the definition of NDVI and the characteristic of vegetation spectra, and is unavoidable to some degree. Meanwhile, through model simulation, we also indicate that the relationship between simple ratio vegetation index (SR) and LAI closes to a piecewise linear one instead of a linear one, which is mainly caused by the influence of background and different change rates of reflectance in red and infrared bands with LAI increasing.

Kernel -based vegetation index and its validation with different-scale BRDF data sets

Traditional vegetation indices are usually constructed by using red and near-infrared band reflectance data under single solar incidence-observation geometry. However, because of the anisotropy of the Earth surfaces reflectance, vegetation indices acquired from different solar-incidence observation geometries exhibit lots of variances. Meanwhile, most of those indices only utilize vegetations spectral information, and anisotropic reflectance of vegetation is considered as a disturbing factor rather than a source of vegetations structural information. In this paper, kernel-based vegetation index (KVI) is constructed based on the semi-empirical kernel-based BRDF model parameters. Validation results of ground measured bidirectional reflection data of different vegetation types show: kernel-based vegetation index has better linear relationship with corresponding vegetations leaf area index (LAI) than widely used normalized difference vegetation index does. The results of upscaling KVI from local scale to global large scale suggest the effect of scale needs to be considered when using it to different scale data sets. This study suggests that KVI provides a new method of better using multi-spectrum and multi-angle reflectance data, and has certain potential for multi-angular remote sensing applications

The 3D simulation database setup for the realistic scene of the typical crops

Computer simulation is one of the vegetation canopy reflectance modeling method, which can realistically simulate the radiation interaction process within vegetation canopies and their dependence on various canopy structural parameters. In this paper, we mainly use the computer simulation methods, combined computer graphics with radiosity equation to simulate the radiative transfer process of the scene in both visible and near-infrared regions. There are file components in our 3D model: computer graphics technique to generate 3D objects; radiosity equation to describe the radiation exchange among the 3D objects; graphics based methods to compute view factors; radiosity computation and 3D image display of the objects; lastly introduced our 3D simulation database built up to put all information in it, such as simulated vegetation canopys 3D structure files and the spectral data information of the vegetation and scene, in order to convenience users to query and retrieve correlating spectral information

A method for estimating chlorophyll content of wheat from reflectance spectra

Chlorophyll and carotenoid, relating to the physiological function of leaves, are two pigments which can absorb the light energy during the process of plant photosynthesis. Among the pigments, the chlorophyll plays an important role in the photosynthesis, and its content, as a predictor of the nutritional status of vegetation, is one of the main factors to evaluate the environment and growth conditions for the winter wheat. This paper, based on the reflectance spectra of wheat in Xiao Tangshan County, in China, took PLS regression as the quantitative inversion method to have established the hyperspectral inversion model between chlorophyll content and the reflectance spectra of wheat. Through analysis, it indicated that the chlorophyll content of wheat was highly relative to the reflectance of hyperspectral from 350 nm to 1060 nm. The correlation coefficient between the prediction value and the measured value is as high as 0.9, and the RMSEP is lower than 0.4. The research provided an effective method to estimate the chlorophyll content using the quantitative inversion technology of hyperspectral RS

The method on generating LAI production by fusing BJ-1 remote sensing data and modis LAI product

LAI is the more important parameter of vegetation canopy, so LAI inversion from remote sensing observations is the hot study field, especially for the high spatial and high temporal resolution remote sensing data. Beijing-1 microsatellite is an applied earth observing microsatellite of China, which can also give us the good data of short cycle time and wider coverage. So it is necessary to generate the quantitative product of BJ-1 remote sensing data. In this paper, the main object is to study on the method of the leaf area index inversion for producing BJ-1 LAI product. The neuronal network method is used to get the relationship between LAI and reflectance in green, red and NIR band. Based on the BJ-1 LAI inversion, the second object of this paper is to generate of high spatial and high temporal resolution LAI product. A method is proposed to get high spatial and temporal resolution LAI product by fusing the time-series MODIS LAI product(1 km, 8-day product)and BJ-1 LAI. Through this study, we can get the LAI products of BJ-1, which is with the high spatial resolution and high time resolution. This product will provide more information of vegetation for BJ-1 microsatellite data applications.LAI (Leaf Area Index) is the more important parameter of vegetation canopy, which can depict their growth course. So LAI inversion from remote sensing observations is the hot study field, especially for the high spatial and high temporal resolution remote sensing data. Beijing-1 microsatellite is an applied earth observing microsatellite of China,which can also give us the good data of short cycle time and wider coverage. So it is necessary to generate the quantitative product of BJ-1 remote sensing data. In this paper, the main object is to study on the method of the leaf area index inversion for producing BJ-1 LAI product. Because of no on-board calibration for BJ-1 multi-spectral images, we can’t get the reflectance data, but DN values. Therefore, we get the VI from BJ-1 multi-spectral data. And from analyzing some VIs, we take NDVI as the good index for the LAI estimation. In order to retrieve the leaf area index (LAI), the BRDF forward model is used to simulate the relationship between LAI and NDVI. Based on the BJ-1 LAI inversion, the second object of this paper is to generate of high spatial and high temporal resolution LAI product. A method is proposed to get high spatial and temporal resolution LAI product by fusing the time-series MODIS LAI product(1 km, 8-day product)and BJ-1 LAI.In this method,the BJ-1 classification image is used to register with MODIS data, then the percentage of classes of FPT classification in the MODIS pixel can be calculated, and the time-series LAI at every classes can be obtained through linear unmixing.At last,the BJ-1 LAI is used to adjust this curve of time-series LAI to estimate the LAI at high spatial and temporal resolution.Through this study, we can get the LAI products of BJ-1, which is with the high spatial resolution and high time resolution (32m, 4-day product). This product will provide more information of vegetation for BJ-1 microsatellite data applications.