[Investigation of the hyperspectral image characteristics of wheat leaves under different stress].

The diagnosis of growing status and vigor of crops under various stresses is an important step in precision agriculture. Hyperspectral imaging technology has the advantage of providing both spectral and spatial information simultaneously, and has become a research hot spot. In the present study, auto-development of the pushbroom imaging spectrometer (PIS) was utilized to collect hyperspectral images of wheat leaves which suffer from shortage of nutrient, pest and disease stress. The hyperspectral cube was processed by the method of pixel average step by step to highlight the spectral characteristics, which facilitate the analysis based on the differences of leaves reflectance. The results showed that the hyperspectra of leaves from different layers can display nutrient differences, and recognize intuitively different stress extent by imaging figures. With the 2 nanometer spectral resolution and millimeter level spatial resolution of PIS, the number of disease spot can be qualitatively calculated when crop is infected with diseases, and, the area of plant disease could also be quantitatively analyzed; when crop suffered from pest and insect, the spectral information of leaves with single aphid and aphids can be detected by PIS, which provides a new means to quantitatively detect the aphid destroying of wheat leaf. The present study demonstrated that hyperspecral imaging has a great potential in quantitative and qualitative analysis of crop growth.

[Monitoring of winter wheat stripe rust based on the spectral knowledge base for TM images].

In most cases, the reversion model for monitoring the severity degree of stripe rust based on the hyperspectral information can not be directly applied by the satellite images with relatively broad bandwidth, while the airborne hyperspectral images can not be applied for large-scale monitoring either, due to the scale limitation of its data and high cost. For resolving this dilemma, we developed a monitoring method based on PHI images, which relies on the construction of spectral knowledge base of winter wheat stripe rust. Three PHI images corresponding to the winter wheat experimental field that included different severity degree of stripe rust were used as a medium to establish the spectral knowledge base of relationships between disease index (DI) and the simulated reflectance of TM bands by using the empirical reversion model of DI(%) and the relative spectral response (RSR) function of TM-5 sensor. Based on this, we can monitor and identify the winter wheat stripe rust by matching the spectral information of an untested pixel to the spectral knowledge base via Mahalanobis distance or spectral angle mapping (SAM). The precision of monitoring was validated by simulated TM pixels, while the effectiveness of identification was tested by pixels from TM images. The results showed that the method can provide high precision for monitoring and reasonable accuracy for identification in some certain growth stages of winter wheat. Based on the simulated TM pixels, the model performed best in the pustulation period, yielded a coefficient of determination R2 = 0.93, while the precision of estimates dropped in the milk stage, and performed worst in the jointing stage, which is basically inappropriate for monitoring. Moreover, by using the pixels from TM images, the infected pixels could be identified accurately in pustulation and milk stages, while failed to be identified in jointing stage. For matching algorithms, the Mahalanobis distance method produced a slightly better result than SAM method.

[The analysis of consistency between HJ-1B and Landsat 5 TM for retrieving LST based on the single-channel algorithm].

To ascertain whether the thermal infrared image of HJ-1B which has the similar sensor parameter and setting to Landsat 5 TM6 image is applicable for retrieving the land surface temperature (LST), a comparison of retrieved LST between two types of sensors was conducted. Two scenes of thermal infrared images that came from different sensors were acquired in 5th, Apr 2009, which covered the same region in Beijing. To retrieve LST, a generalized single-channel algorithm developed by Jiménez-Muñoz and Sobrino was applied. The LST of study area for both images was thus generated. Based on the LST mapping results and corresponding statistics, an apparent trend could be observed which indicated the consistency in both LST value and its spatial distribution. Consequently, the performance of HJ-IB IRS serving as the data source for LST retrieval was assessed and illustrated in this study. Besides, a high temporal resolution as well as wide swath of the HJ-IRS data suggested its potential in application.

[Monitoring stripe rust of winter wheat using PHI based on sensitive bands].

Forty six points representing different severity degree of stripe rust were established in winter wheat field. The canopy reflectance was collected by an ASD hand-held spectrometer at each point. Meanwhile, the diseases index was investigated. These data were used for the following analysis. Firstly, the relationships between diseases index and reflectance of bands in the range of 300-1500 nm were analyzed. The sensitive bands were selected for stripe rust detecting. Secondly, considering the character of PHI image, red bands (620-718 nm) and near infrared bands (770-805 nm) were assigned as the best bands. Finally, the mean reflectance of red bands (620-718 nm) and near infrared bands (770-805 nm) was calculated respectively to construct the reverse model with the observed diseases indexes: DI = 19.241 R1 - 2.20667 R2 + 12.2744. With this model, the severity degree of stripe rust of winter wheat was monitored successfully in PHI image.