[Characteristics of water and heat fluxes and its footprint climatology on farmland in low hilly region of red soil]. / 低丘红壤区农田水热通量变化特征及气候学足迹.

The analysis of the characteristics and footprint climatology of farmland water and heat fluxes has great significance to strengthen regional climate resource management and improve the hydrothermal resource utilization in the region of red soil. Based on quality controlled data from large aperture scintillometer and automatic meteorological station in hilly region of red soil, this paper analyzed in detail the characteristics of farmland water and heat fluxes at different temporal scales and the corresponding source area distribution of flux measurement in the non-rainy season and crop growth period in hilly region of red soil. The results showed that the diurnal variation of water and heat fluxes showed a unimodal trend, but compared with the sunny day, the diurnal variation curves fluctuated more complicatedly on cloudy day. In the whole, either ten-day periods or month scale, the water and heat fluxes were greater in August than in September, while the net radiation flux was more distributed to latent heat exchange. The proportion of net radiation to latent heat flux decreased in September compared to August, but the sensible heat flux was vice versa. With combined effects of weather conditions (particularly wind), stability, and surface condition, the source areas of flux measurement at different temporal scales showed different distribution characteristics. Combined with the underlying surface crops, the source areas at different temporal scales also had different contribution sources.

[Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat].

In order to improve the accuracy of wheat yellow rust disease severity using remote sensing and to find the optimum inversion model of wheat diseases, the canopy reflectance and disease index (DI) of winter wheat under different severity stripe rust were acquired. The three models of PLS (Partial Least Square), BP neural network using seven hyperspectral vegetation indices which have significant relationship with the occurrence of disease and vegetation index (PRI) were adopted to build a feasible regression model for detecting the disease severity. The results showed that PLS performed much better. The inversion accuracy of PLS method is best than of the VI (PRI, Photochemical Reflectance Index) and BP neural network models. The coefficients of determination (R2) of three methods to estimate disease severity between predicted and measured values are 0.936, 0.918 and 0.767 respectively. Evaluation was made between the estimated DI and the measured DI, indicating that the model based on PLS is suitable for monitoring wheat disease. In addition, to explore the different contributions of diverse types of vegetation index to the models, the paper attempts to use NDVI, GNDVI and MSR which on behalf of vegetation greenness and NDWI and MSI that represents the moisture content to be input variables of PLS model. The results showed that, for the wheat yellow rust disease, changes in chlorophyll content is more sensitive to the disease severity than the changes in water content of the canopy. However, the accuracy of the two models are both lower than predicted when participating in all seven vegetation indices, namely using several species of vegetation indices tends to be more accurate than that using single category. It indicated that it has great potential for evaluating wheat disease severity by using hyper-spectral remote sensing.

[Inversion of LAI by considering the hotspot effect for different geometrical wheat].

Aimed to deal with the limitation of canopy geometry to crop LAI inversion accuracy a new LAI inversion method for different geometrical winter wheat was proposed based on hotspot indices with field-measured experimental data. The present paper analyzed bidirectional reflectance characteristics of erective and loose varieties at red (680 nm) and NIR wavelengths (800 nm and 860 nm) and developed modified normalized difference between hotspot and dark-spot (MNDHD) and hotspot and dark-spot ratio index (HDRI) using hotspot and dark-spot index (HDS) and normalized difference between hotspot and dark-spot (NDHD) for reference. Combined indices were proposed in the form of the product between HDS, NDHD, MNDHD, HDRI and three ordinary vegetation indices NDVI, SR and EVI to inverse LAI for erective and loose wheat. The analysis results showed that LAI inversion accuracy of erective wheat Jing411 were 0.9431 and 0.9092 retrieved from the combined indices between NDVI and MNDHD and HDRI at 860 nm which were better than that of HDS and NDHD, the LAI inversion accuracy of loose wheat Zhongyou9507 were 0.9648 and 0.8956 retrieved from the combined indices between SR and HDRI and MNDHD at 800 nm which were also higher than that of HDS and NDHD. It was finally concluded that the combined indices between hotspot-signature indices and ordinary vegetation indices were feasible enough to inverse LAI for different crop geometrical wheat and multiangle remote sensing data was much more advantageous than perpendicular observation data to extract crop structural parameters.

[Recovery growth of Microcystis aeruginosa after the sub-high temperature stress].

Recovery growth of Microcystis aeruginosa after sub-high temperature stress was investiga- ted in this paper. The treated groups under 35 °C were cultured for 3, 6, and 12 days before being transferred to normal conditions, and the algae under 25 °C all the time was set as the control. Cell density, chlorophyll a, carotenoid, malondialdehyde and antioxidant enzymes activities were measured. The results showed that the growth of M. aeruginosa was inhibited significantly under the sub-high temperature stress. The cell density and chlorophyll a content were 14.5% and 22.3% lower than the control respectively on the 12th day, but carotenoid synthesis was not inhibited obviously. The longer the stress was, the higher the malondialdehyde content and SOD, CAT activities became. After the relief of stress, algal cells got recovered with the decreasing malondialdehyde content and antioxidase activities. The 3-, 6- and 12-day stress treatments at 35 °C showed under-compensation, over-compensation and exact-compensation, respectively, indicating that the com- pensatory degree was decided by the time under stress. As the recovery time proceeded, the differ- ence between treated groups and the control reduced gradually. The growth parameters tended to be stable. Regression equations of cell density and chlorophyll a changing with the stress time and recovery time were revealed. The compensation effect of M. aeruginosa was similar to the process of algal bloom. According to this endogenous biological characteristic, this study provided a theoretical support for prediction system of algal bloom.

[Inversion of leaf area index during different growth stages in winter wheat].

Being orientated to the low prescion of crop leaf area index (LAI) inversion using the same spectral vegetation index during different crop growth stages, the present paper analyzed the precision of LAI inversion by employing NDVI(normalized difference vegetation index). Ten vegetation indices were chosen including six broad-band vegetation indices and four narrow-band vegetation indices responding to vegetation cover to inverse LAI in different growth stages. Several conclusions were drawn according to the analysis. The determinant coefficient (R2) and root mean square error (RMSE) between LAI inversion value and true value were 0.5585 and 0.3209 respectively during the whole growth duraton. The mSR (modified simple ratio index) index was appropriate to inverse of LAI during earlier growth stages (before jointing stage) in winter wheat. The R2 and RMSE between LAI inversion value and true value were 0.7287 and 0.2971 respectively. The SR (simple ratio index) index was suitable enough to inverse of LAI during medium growth stages (from joingting stagess to heading stages). The R2 and RMSE between LAI inversion value and true value were 0.6546 and 0.3061 respectively. The NDVI (normalized difference vegetation index) index was proven to be fine to inverse LAI during later growth stages(from heading stage to ripening stage). The R2 and RMSE between LAI inversion value and true value were 0.6794 and 0.3164 respectively. Therefore it was indicated that the results of LAI inversion was much better inverse of winter wheat LAI choosing different vegetation indices during differen growth stages for winter wheat according to the change of vegetation cover and canopy reflectance than merely with NDVI to inverse LAI in the whole growth stages. It was concluded that the precision of LAI inversion was significantly improved with segmented models based on different vegetation indices.