Spectral reflectance characteristics of different snow and snow-covered land surface objects and mixed spectrum fitting.

The field spectroradiometer was used to measure spectra of different snow and snow-covered land surface objects in Beijing area. The result showed that for a pure snow spectrum, the snow reflectance peaks appeared from visible to 800 nm band locations; there was an obvious absorption valley of snow spectrum near 1 030 nm wavelength. Compared with fresh snow, the reflection peaks of the old snow and melting snow showed different degrees of decline in the ranges of 300-1 300, 1 700-1 800 and 2 200-2 300 nm, the lowest was from the compacted snow and frozen ice. For the vegetation and snow mixed spectral characteristics, it was indicated that the spectral reflectance increased for the snow-covered land types (including pine leaf with snow and pine leaf on snow background), due to the influence of snow background in the range of 350-1 300 nm. However, the spectrum reflectance of mixed pixel remained a vegetation spectral characteristic. In the end, based on the spectrum analysis of snow, vegetation, and mixed snow/vegetation pixels, the mixed spectral fitting equations were established, and the results showed that there was good correlation between spectral curves by simulation fitting and observed ones (correlation coefficient R2 = 0.950 9).

[Progress in retrieving vegetation water content under different vegetation coverage condition based on remote sensing spectral information].

The present paper reviews the progress in the methods of retrieving vegetation water content using remote sensing spectral information, including vegetation spectral reflectance information (VIR, SWIR, and NIR) to directly extract vegetation water content and establish vegetation water indices (WI), i. e. NDWI = (R860 - R1 240)/(R860 + R1 240) and PWI = R970/R900; and using radiation transfer (RT) model such as PROSPAIL to detect plant water content information. The authors analyze the method of retrieving vegetation water content under low crop coverage condition. The plant water can be estimated by using canopy physiological parameters firstly, and using vegetation indices and radiation transfer model secondly, which can eliminate soil background effect. The estimated agricultural drought and vegetation water content by using multi-angle polarized reflectance and bi-directional reflectance (BRDF) was discussed in this paper. In the end, the possible development trend of retrieval methods for plant water information under plant low coverage conditions was discussed.

[The progress in retrieving land surface temperature based on thermal infrared and microwave remote sensing technologies].

Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

[Simulation of rice yield response to elevated carbon dioxide and temperature for different cultivars in the cold region of China.] / 寒地不同熟性品种水稻产量对CO2浓度和温度升高响应的模拟.

This paper was aimed to assess the potential impacts of rising temperature and CO2 concentration on the production of different rice cultivars in the cold region of China, Heilongjiang Province. Total three representative rice varieties with different maturity types were selected to conduct the simulation experiments according to the required accumulated temperature. Daily weather data and open top chamber (OTC) test yield data for year 2013 were used to initialize CERES-Rice model parameters. CERES-Rice model was executed to simulate the influence of climate change on early-mature, mid-mature and late-mature rice production under fixed weather scenarios, which consisted of three CO2 concentrations (i.e. 390, 450 and 550 µmol·mol-1) and four temperature rise levels (i.e. 1, 2, 3 and 4 ℃). Results showed that with the increase in concentration of CO2, the rice yield would increase. With the rise in temperature, early-mature rice yield would decline significantly. However, mid-mature and late-mature rice yield would increase at first and then gra-dually decline. Without considering the effect of CO2 fertilization, except that the medium and late varieties under 1 ℃ warming would slightly increase the yield by 3.1% and 0.27% respectively, yield under the other treatments would reduce. The most serious reduction occurred to early-mature rice, which decreased up to 57.7% when the temperature increased by 4 ℃, while mid-mature rice and late-mature rice yields decreased about 10%. Considering the effect of CO2 fertilization, mid-mature and late-mature rice yields would even increase by 0.75% and 3.2% at 450 µmol·mol-1 CO2 under 2 ℃ warming, respectively. Mid-mature rice yield would still increase 4.5% under 3 ℃ warming and late-mature rice yield would also increase 0.39% under 4 ℃ warming at 550 µmol·mol-1 CO2. However, it was identified that early-mature rice yield would always sharply decrease with temperature increasing regardless of the effect of CO2 fertilization. Similarly, CO2 fertilization effects could improve rice yield to certain extent with temperature increasing. However, the diffe-rence among the varieties in response to CO2 fertilization effect was not significant. The contribution rate of CO2 fertilization effect on rice yield was less than 10%.

Detection, emission estimation and risk prediction of forest fires in China using satellite sensors and simulation models in the past three decades--an overview.

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.