Huang Wenjiang

Evaluation of variable-rate nitrogen recommendation of winter wheat based on SPAD chlorophyll meter measurement

Abstract To develop a time‐Specific and time‐critical spatial variable rate nitrogen application (VRN) method and to overcome the limitations of traditional field sampling methods, this study focused on the relationship between SPAD chlorophyll meter readings and nitrogen content in leaves in order to determine the amount of nitrogen fertilisation required for agricultural objectives. Field experiments were conducted in three wheat growth duration stages from 2003 to 2006. Grain yields and soil NO3‐N contents were measured in all plots. Our results indicated that VRN technology reduced wheat yield spatial variability. The benefits of VRN included low soil residual NO3‐N content and NO3‐N leaching potential, suggesting that VRN technology based on SPAD readings can potentially reduce groundwater pollution and therefore protect our limited environmental resources.

Using hyperspectral indices to estimate foliar chlorophyll a concentrations of winter wheat under yellow rust stress

Abstract The canopy hyperspectral reflectance of winter wheat infected with yellow rust at different levels of severity were measured by an ASD FieldSpec Pro FR™ spectrometer in the field and the concentrations of chlorophyll a (Chl a) in the leaves corresponding to the spectra were determined by biochemical methods in the laboratory. Correlation analyses were made between Chl a concentrations and canopy hyperspectral data of diseased wheat. Results show that foliar Chl a concentrations are strongly correlated with canopy spectrum in the visible region and the first‐order derivative spectrum in blue edge, green edge, and red edge. Linear and nonlinear models for estimating Chl a concentrations of the diseased wheat were built based on several spectral indices. Results indicate that SDr/SDg, in which SDr and SDg are the sums of the first derivative within red and green edges, outperformed the other indices in predicting Chl a concentrations. The relative estimation errors for Chl a for 12 unseen samples are 17.5%. It is concluded that derivative spectra in red edge and green edge have strong prediction power for foliar Chl a concentrations of diseased winter wheat. Using hyperspectral remote sensing data to monitor crop disease and nutrition status is very promising.

Continuous Wavelet Analysis Based Spectral Feature Selection For Winter Wheat Yellow Rust Detection

Remote-sensing technologies can provide quick responses for determining the presence of yellow rust disease. However, most studies selected spectral indicators solely based on the statistical relevance to disease severity. Few of them investigated the underlying mechanism including the variation of biochemical properties due to the presence of disease. This study aims at identifying some mechanism based spectral features through continuous wavelet (CWT) analysis. An inoculation of yellow rust fungal was conducted in the experimental field. The hyperspectral data and biochemical properties including the content of water and pigment were measured for both infected and non-infected winter wheat plants. A two-tailed paired student t-test was used to identify the biochemical properties which have significant variation when the plants were infected. For those sensitive biochemical properties, a CWT transformation was then processed with the spectral data. The sensitive spectral features were selected through a correlation scalogram. It was found that the content of chlorophyll decreased significantly in yellow rust infected plants. Four spectral features were identified which could well reflect the chlorophyll content. The predicted model of chlorophyll content was thus established based on the partial least squares (PLS) regression. In addition, the linear discrimination analysis (LDA) was adopted in classifying the infected and non-infected plants. The classification accuracy reached 74.8% which indicated the selected spectral features have great potential in detecting the winter wheat yellow rust infection.Abstract This study aims at identifying some mechanisms based on spectral features through continuous wavelet (CWT) analysis, and examining their estimating and discriminating power. In 2003, an inoculation of yellow rust fungal was conducted. Field measurements of canopy reflectance and biochemical properties were made with 5–7 day intervals during key growing period. Through atwo-tailed paired student t-test, it was found that the variation of chlorophyll content is closely associated with the yellow rust infection. Based on this relationship, four spectral features were thus identified by CWT analysis. According to the results of stepwise linear regression and partial least squares (PLS) regression, the estimating power of those spectral features was not satisfied. However, the discriminating power of those features was revealed by linear discrimination analysis (LDA) and quadratic discriminate analysis (QDA), which yielded a success rate of 76.4%. Therefore, the CWT analysis and discriminate analysis ...

Methods and application of remote sensing to forecast wheat grain quality

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

Integration of Ground Observations and Crop Simulation Model for Crop Leaf Area Index Estimation

Crop leaf area index (LAI) is a major indicator for crop growth monitoring and yield estimation. Actual observed data provide statistical properties of crop LAI, while crop simulation model provides physical properties of LAI within the whole crop growth period. Then, in this work, a data assimilation scheme integrating observations and crop simulation model based on Ensemble kalman filtering (EnKF) algorithm was proposed for LAI estimation. Calibrating input parameters and initial conditions of crop simulation model based on historic observed data, and then assimilating observations into crop simulation model for LAI estimation base on EnKF algorithm. This algorithm can integrate practical observed information into physical model to construct estimations obeying crop growth rules and actual growth situations. Winter wheat in Beijing was selected as experimental object. The numerical results showed enhanced estimation accuracy of the proposed algorithm. Theoretical analysis and practical experiments fully confirmed the feasibility and effectiveness of this algorithm in LAI estimation.

Simulation system development of infrared remote sensing images: HJ-1B case

Satellite image simulation is one of the key methods to check the expected performance of the satellites before they launched or when satellites can not provide images in other time. In order to provide a useful tool to analyze whether the payload of HJ-1B (a small satellite of the environment-monitoring constellation) is enough, we develop a simulation system for the infrared cameras, which consists of four bands including NIR band (0.75–1.10μm), SWIR (1.55–1.75μm), MIR (3.50–3.90μm) and TIR (10.5–12.5μm). The spatial resolution of NIR and SWIR band is 150 meter, while 300 meter for the MIR and TIR band. The sensor is an optical-mechanics multi-scanning system with maximum scanning degree of 29 degree.

Development of Linux based USB device driver for portable spectrometer

USB device interface on the PXA270 processor is designed on demand of data transmission for portable spectrometer. Analyzing gadget system architecture in Linux system, USB device driver is realized, and the function of mass storage provided by gadget is used for spectrometer to compliment its function of USB storage device, so that spectral data file can be transferred easily from portable spectrometer to personal computer. By test, this USB device interface works correctly.

Study on winter wheat yield estimation model based on NDVI and seedtime

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

Hyperspectral image for discriminating aphid and aphid damage region of winter wheat leaf

Wheat aphid, Sitobion avenae F. is the most destructive insect infesting winter wheat and appears almost annually in northwest China. Past studies have demonstrated the potential of remote sensing for detecting diseases and insects damage. In the study, hyperspectral imaging in the visible and near-infrared (500-900nm) region was tried to determinate aphid of wheat leaf and detect damage region of winter leaf caused by aphid. The principal component analysis (PCA) and spectral indices which used to monitor some stresses were applied to extract aphid information. The result showed that the classification result was better based on the second principal component (PC2) image and the third principal component (PC3) image by principal component (PC) transformation than spectral indices. Then, the mean reflectance of pixels with aphid and pixels without aphid was obtained, respectively, and the most sensitive reflectance regions to aphid were selected in visible and near-infrared by comparing the reflectance difference of two classes. Further, Leaf aphid damage index (LADI) was established according to two the sensitive reflectance region, and the leaf region with aphid, the infested leaf region and healthy leaf region were classified by LADI value of image. The result showed that the aphid damage area ratio of each wheat leaf estimated by pixels number of three classes was consistent with the survey the damage area ratio. So LADI had potential for detecting the leaf damage region caused by aphid.

Discriminating wheat aphid damage level using spectral correlation simulating analysis

Wheat aphid, Sitobion avenae F. is main aphid species infesting winter wheat in the filling stage in Northwest China, and it has severe impact on both wheat yield and quality. The study acquired hyperspectral data by ASD FieldSpec Pro spectrometer at the canopy level and aphid damage levels of samples in the filling stage of winter wheat. The spectral characteristics of wheat uninfected by aphid and healthy wheat were analyzed, then the correlation simulating analysis model (CSAM) which was established by a 2-dimensional coordinate system with average spectral of healthy wheat samples called also base spectrum as abscissa axis and the spectral of other samples as vertical axis respectively is developed and tried to monitor the aphid damage levels. It is concluded that the fitting curves obtained by the reflectance of samples relative to healthy wheat samples are near to straight line in the range from 400nm to 1000nm (R2>0.99), and the slopes of fitting lines decrease as aphid damage levels become serious. Moreover, the most sensitive band regions were selected out. The result shows that the correlation between the slopes of fitting line and aphid damage levels is the highest in the range from 400 nm to 810 nm (R2=0.89). Therefore, the CSAM can be sued to discriminate the aphid damage levels in the filling stage of winter wheat.