[A inversion model for remote sensing of leaf water content based on the leaf optical property].

Leaf water content is a fundamental physiological characteristic parameter of crops, and plays an important role in the study of the ecological environment. The aim of the work reported in this paper was to focus upon the retrieval of leaf water content from leaf-scale reflectance spectra by developing a physical inversion model based on the radiative transfer theory and wavelet analysis techniques. A continuous wavelet transform was performed on each of leaf component specific absorption coefficients to pick wavelet coefficients that were identified as highly sensitive to leaf water content and insensitive to other components. In the present study, for identifying the most appropriate wavelet, the six frequently used wavelet functions available within MATLAB were tested. Two biorl. 5 wavelet coefficients observed at the scale of 200 nm are provided with good performance, their wave-length positions are located at 1 405 and 1 488 nm, respectively. Two factors (α and Δ) of the predictive theoretical models based on the biorl. 5 wavelet coefficients of the leaf-scale reflectance spectra were determined by leaf structure parameter N. We built a database composed of thousands of simulated leaf reflectance spectra with the PROSPECT model. The entire dataset was split into two parts, with 60% the calibration subset assigned to calibrating two factors (α and Δ) of the predictive theoretical model. The remaining 40% the validation subset combined with the LOPEX93 experimental dataset used for validating the models. The results showed that the accuracy of the models compare to the statistical regression models derived from the traditional vegetation indices has improved with the highest predictive coefficient of determination (R2) of 0. 987, and the model becomes more robust. This study presented that wavelet analysis has the potential to capture much more of the information contained with reflectance spectra than previous analytical approaches which have tended to focus on using a small number of optimal wavebands while discarding the majority of the spectrum.

[Characteristics of terrestrial ecosystem primary productivity in East Asia based on remote sensing and process-based model].

Based on the bi-linearly interpolated meteorological reanalysis data from National Centers for Environmental Prediction, USA and by using the leaf area index data derived from the GIMMS NDVI to run the process-based Boreal Ecosystems Productivity Simulator (BEPS) model, this paper simulated and analyzed the spatiotemporal characteristics of the terrestrial ecosystem gross primary productivity (GPP) and net primary productivity (NPP) in East Asia in 2000-2005. Before regional simulating and calculating, the observation GPP data of different terrestrial ecosystem in 15 experimental stations of AsiaFlux network and the inventory measurements of NPP at 1300 sampling sites were applied to validate the BEPS GPP and NPP. The results showed that BEPS could well simulate the changes in GPP and NPP of different terrestrial ecosystems, with the R2 ranging from 0.86 to 0.99 and the root mean square error (RMSE) from 0.2 to 1.2 g C x m(-2) x d(-1). The simulated values by BEPS could explain 78% of the changes in annual NPP, and the RMSE was 118 g C x m(-2) x a(-1). In 2000-2005, the averaged total GPP and total NPP of the terrestrial ecosystems in East Asia were 21.7 and 10.5 Pg C x a(-1), respectively, and the GPP and NPP exhibited similar spatial and temporal variation patterns. During the six years, the total NPP of the terrestrial ecosystems varied from 10.2 to 10.7 Pg C x a(-1), with a coefficient of variation being 2. 2%. High NPP (above 1000 g C x m(-2) x a(-1)) occurred in the southeast island countries, while low NPP (below 30 g C x m(-2) x a(-1)) occurred in the desert area of Northwest China. The spatial patterns of NPP were mainly attributed to the differences in the climatic variables across East Asia. The NPP per capita also varied greatly among different countries, which was the highest (70217 kg C x a(-1)) in Mongolia, far higher than that (1921 kg C x a(-1)) in China, and the lowest (757 kg C x a(-1)) in India.

[Spatial and temporal variability of soil C/N ratio in Songnen Plain maize belt].

The C/N ratio of soils is a sensitive indicator of soil quality, and an indicator for assessing carbon and nitrogen nutrition balance of soils. Its variation is significant in reflecting the carbon and nitrogen cycling of soils. Based on field investigation, sample collection and analysis, and application of geostatistics and GIS technology, spatial and temporal variation of C/N ratio was analyzed and studied from 1980 to 2005 in Songnen Plain maize belt. The results indicated that the mean value of C/N ratio is 10.56 and 12.30 in 1980 and 2005, respectively. Spatial correlation distance of soil C/N ratio in two periods is 196.3 km and 51.1 km, showing a decreasing trend, which indicated that farming management factors were enhancing. In the past 25 years, 84.88% of soil C/N ratio was on rise with the highest value in the west of the study area, but parts of Dehui County and Jiutai County decreased. As for different land use types, soil C/N ratios in the upland, paddy land, forest and woodland and grassland showed upward trends, with the highest increase from 10.03 +/- 1.12 in 1980 to 12.61 +/- 0.87 in 2005 in grassland and higher in upland and paddy land than the national average. The increasing soil C/N ratio illustrated that soil carbon increased faster than nitrogen. To maintain the steady growth of soil C/N ratio, it is suggested that the return of carbon be paid more attention when the input of nitrogen, such as incorporating crop residues into soil and inputting more organic fertilizers into soils for future farming practices.

[Simulation of cropland soil moisture based on an ensemble Kalman filter].

By using an ensemble Kalman filter (EnKF) to assimilate the observed soil moisture data, the modified boreal ecosystem productivity simulator (BEPS) model was adopted to simulate the dynamics of soil moisture in winter wheat root zones at Xuzhou Agro-meteorological Station, Jiangsu Province of China during the growth seasons in 2000-2004. After the assimilation of observed data, the determination coefficient, root mean square error, and average absolute error of simulated soil moisture were in the ranges of 0.626-0.943, 0.018-0.042, and 0.021-0.041, respectively, with the simulation precision improved significantly, as compared with that before assimilation, indicating the applicability of data assimilation in improving the simulation of soil moisture. The experimental results at single point showed that the errors in the forcing data and observations and the frequency and soil depth of the assimilation of observed data all had obvious effects on the simulated soil moisture.

[Forest canopy leaf area index in Maoershan Mountain: ground measurement and remote sensing retrieval].

Leaf area index (LAI) is one of the most important structural parameters of terrestrial ecosystem, while the remote sensing retrieval and the ground optical instrument measurement and based on canopy gap model are the effective approaches to rapidly obtain LAI. However, these two approaches can only acquire effective LAI (LAI(e)), due to the clumping of vegetation canopy. Taking the experimental forest farm of Northeast Forestry University at Maoershan Mountain in Heilongjiang Province of Northeast China as study site, this paper measured the forest canopy LAI(e) by LAI2000, and estimated the LAI by the combination of TRAC (tracing radiation and architecture of canopies) measurement of foliage clumping index. A LAI remote sensing retrieval model was constructed through the analysis of the relationships between different vegetation indices calculated from Landsat5-TM and measured LAI(e). The results showed that at the study site, the LAI of broad leaved forests was close to the LAI(e), but the LAI of needle leaved forests was 27% larger than the LAI(e). Reduced simple ratio index (RSR) had the highest relationship with measured LAI(e) (R2 = 0.763, n = 23), which could be used as the best predictor of LAI. The LAI at study site increased rapidly with increasing elevation when the elevation was below 400 m, but had a slow increase when the elevation was from 400 m to 750 m. When the elevation was above 750 m, the LAI decreased. There was a significant correlation between the forest canopy LAI and aboveground biomass.