基于SAIL模型的HJ-1卫星LAI反演算法研究

基于辐射传输模型和人工神经网络算法,研究出适用于环境一号卫星CCD相机数据的叶面积指数反演算法针对环境一号卫星CCD相机的波段特征,设计出一种新的植被指数HJVI,有效的避免了数据饱和现象,提高了算法的精度。     

Modeling MODIS LAI time series using three statistical methods

Leaf Area Index (LAI) is one of the most important variables characterizing land surface vegetation and dynamics. Many satellite data, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), have been used to generate LAI products. It is important to characterize their spatial and temporal variations by developing mathematical models from these products. In this study, we aim to model MODIS LAI time series and further predict its future values by decomposing the LAI time series of each pixel into several components: trend, intra-annual variations, seasonal cycle, and stochastic stationary or irregular parts. Three such models that can characterize the non-stationary time series data and predict the future values are explored, including Dynamic Harmonics Regression (DHR), STL (Seasonal-Trend Decomposition Procedure based on Loess), and Seasonal ARIMA (AutoRegressive Intergrated Moving Average) (SARIMA). The preliminary results using six years (2001-2006) of the MODIS LAI product indicate that all these methods are effective to model LAI time series and predict 2007 LAI values reasonably well. The SARIMA model gives the best prediction, DHR produces the smoothest curve, and STL is more sensitive to noise in the data. These methods work best for land cover types with pronounced seasonal variations.     

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.     

Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling

Land surface and climate modelling requires continuous and consistent Leaf Area Index (LAI). High spatiotemporal resolution and long-time record data are more in demand nowadays and will continue to be in the future. MODIS LAI products meet these requirements to some degree. However, due to the presence of cloud and seasonal snow cover, the instrument problems and the uncertainties of retrieval algorithm, the current MODIS LAI products are spatially and temporally discontinuous and inconsistent, which limits their application in land surface and climate modelling. To improve the MODIS LAI products on a global scale, we considered the characteristics of the MODIS LAI data and made the best use of quality control (QC) information, and developed an integrated two-step method to derive the improved MODIS LAI products effectively and efficiently on a global scale. First, we used the modified temporal spatial filter (mTSF) method taking advantage of background values and QC information at each pixel to do a simple data assimilation for relatively low quality data. Then we applied the post processing-TIMESAT (A software package to analyze time-series of satellite sensor data) Savitzky-Golay (SG) filter to get the final result. We implemented the method to 10 years of the MODIS Collection 5 LAI data. In comparison with the LAI reference maps and the MODIS LAI data, our results showed that the improved MODIS LAI data are closer to the LAI reference maps in magnitude and also more continuous and consistent in both time-series and spatial domains. In addition, simple statistics were used to evaluate the differences between the MODIS LAI and the improved MODIS LAI.     

Evaluation of the JRC-TIP 0.01° products over a mid-latitude deciduous forest site

The Joint Research Centre Two-stream Inversion Package (JRC-TIP) makes use of white sky albedo products—derived from MODIS and MISR observations in the visible and near-infrared domain—to deliver consistent sets of information about the terrestrial environments that gave rise to these data. The baseline version of the JRC-TIP operates at a spatial resolution of 0.01° and yields estimates of the Probability Distribution Functions (PDFs) of the effective canopy Leaf Area Index (LAI), the canopy background albedo, the vegetation scattering properties, as well as, the absorbed, reflected and transmitted fluxes of the vegetation canopy. In this contribution the evaluation efforts of the JRC-TIP products are extended to the deciduous forest site of Hainich (Germany) where multiannual datasets of in-situ estimates of canopy transmission—derived from LAI-2000 observations—are available. As a Fluxnet site, Hainich offers access to camera acquisitions from fixed locations in and above the canopy that are being used in phenological studies. These images qualitatively confirm the seasonal patterns of the effective LAI, canopy transmission and canopy absorption products (in the visible range of the solar spectrum) derived with the JRC-TIP. Making use of the LAI-2000 observations it is found that 3/4 of the JRC-TIP products lie within a ± 0.15 interval around the in-situ estimates of canopy transmission and absorption. The largest discrepancies occur at the end of the senescence phase when the scattering properties of the vegetation (evidenced by the pictures) and the images qualitatively confirm the seasonal patterns of the effective LAI, canopy transmission and canopy absorption products (in the visible range of the solar spectrum) derived with the JRC-TIP. Making use of the LAI-2000 observations it is found that 3/4 of the JRC-TIP products lie within a ± 0.15 interval around the in-situ estimates of canopy transmission and absorption. The largest discrepancies occur at the end of the senescence phase when the scattering properties of the vegetation (evidenced by the pictures) and the effective LAI (also derived from LAI-2000 measurements) are experiencing large simultaneous changes. It was also found that the seasonal pattern of vegetation scattering properties derived from MISR observations in the near-infrared varies together with the Excess Green index computed from the various channels of the camera data acquired at the top of the canopy.     

Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest

Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas. In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine (Pinus ponderosa) dominated ecosystem in the Black Hills of South Dakota. Linear spectral mixture analyses of IKONOS and ETM+ data were used to isolate spectral endmembers (bare soil, understory grass, and tree/shade) and calculate their subpixel fractional coverages. We then compared these endmember cover estimates to similar cover estimates derived from lidar data and field measures. The IKONOS-derived tree/shade fraction was significantly correlated with the field-measured canopy effective leaf area index (LAI_e) (r²=0.55, p<0.001) and with the lidar-derived estimate of tree occurrence (r²=0.79, p<0.001). The enhanced vegetation index (EVI) calculated from IKONOS imagery showed a negative correlation with the field measured tree canopy effective LAI and lidar tree cover response (r²=0.30, r=−0.55 and r²=0.41, r=−0.64, respectively; p<0.001) and further analyses indicate a strong linear relationship between EVI and the IKONOS-derived grass fraction (r²=0.99, p<0.001). We also found that using EVI resulted in better agreement with the subpixel vegetation fractions in this ecosystem than using normalized difference of vegetation index (NDVI). Coarsening the IKONOS data to 30 m resolution imagery revealed a stronger relationship with lidar tree measures (r²=0.77, p<0.001) than at 4 m resolution (r²=0.58, p<0.001). Unmixed tree/shade fractions derived from 30 m resolution ETM+ imagery also showed a significant correlation with the lidar data (r²=0.66, p<0.001). These results demonstrate the power of using high resolution lidar data to validate spectral unmixing results of satellite imagery, and indicate that IKONOS data and Landsat 7 ETM+ data both can serve to make the important distinction between tree/shade coverage and exposed understory grass coverage during peak summertime greenness in a ponderosa pine forest ecosystem.     

LAI based trees selection for mid latitude urban developments: A microclimatic study in Cairo, Egypt

To study the leaf area index, LAI, based thermal performance in distinguishing trees for Cairo's urban developments, ENVI-met plants database was used as platform for a foliage modeling parameter, the leaf area density, LAD. Two Egyptian trees; Ficus elastica, and Peltophorum pterocarpum were simulated in 2 urban sites with one having no trees, whilst the second is having Ficus nitida trees. Trees LAD values were calculated using flat leaves' trees LAI definition to produce maximum ground solid shadow at peak time. An empirical value of 1 for LAI is applied to numerically introduce LAD values for ENVI-met.Basically, different meteorological records showed improvements for pedestrian comfort and ambient microclimate of the building using F. elastica. About 40–50% interception of direct radiation, reductions in surfaces' fluxes around trees and in radiant temperature T_mrt in comparison to base cases gave preferability to F. elastica. The lack of soil water prevented evapotranspiration to take place effectively and the reduced wind speeds concluded negligible air temperature differences from both base cases except slightly appeared with the F. elastica. Results show that a flat leaves tree if does not validate LAI of 1, the ground shading would not fulfill about 50% direct radiation interception and this value can be used as a reference for urban trees selection.Further simulations were held to investigate LAI value of maximum direct radiation interception.Performing additional simulations, F. elastica of LAI of 3 intercepted almost 84% of direct radiation and revealed implications about urban trees in practice and its actual LAI.     

MSC池计费方案的研究

简要论述了MSC Pool组网方式下计费的相关问题,提出了2个计费方案,对2个方案的优劣进行了比较分析,并提出了应用建议。     

Simple parameterizations of the radiation budget of uniform broadleaved and coniferous canopies

Simulations of the different components of the spectral radiation budget of structurally simple leaf and shoot canopies with varying canopy leaf area index (LAI) were performed. The aims were (1) to test a proposed parameterization of the budget using two spectrally invariant canopy structural parameters (p and p_t) governing canopy absorption and transmittance, respectively, and (2) to incorporate the effect of within-shoot scattering in the parameterization for shoot canopies. Results showed that canopy spectral absorption and scattering were well described by a single parameter, the canopy p value or ‘recollision probability’, which was closely related to LAI. The relationship between p and LAI was however different in leaf and shoot canopy: e.g., at the same LAI the recollision probability was larger in the shoot canopy. It was shown that the p value of the shoot canopy could be decomposed into the p value of an individual shoot (p_sh) and the p value of the leaf canopy with the same effective LAI (LAI_e). The canopy p value allows calculation of canopy absorption and scattering at any given wavelength from the leaf (or needle) scattering coefficient at the same wavelength. To calculate canopy reflectance, separation of the downward and upward scattered parts is needed in addition. The proposed parameter p_t worked rather well in the leaf canopy at moderate values of LAI, but not in the coniferous shoot canopy nor at high values of LAI. However, the simulated fraction of upward scattered radiation increased in a straightforward manner with LAI, and was not particularly sensitive to the leaf (or needle) scattering coefficient. Judged by this ‘smooth’ behavior, the existence of another kind of simple parameterization for this separation remains an interesting possibility.     

基于TM影像的冬小麦苗期长势与植株氮素遥感监测研究

叶面积指数和叶片氮素含量是决定小麦群体长势的重要生理指标,也是制定栽培管理措施的必要依据。利用遥感监测小麦返青后的叶面积指数和叶片氮素含量,便于及时采取施肥、灌溉、中耕等调控措施,达到优质、高产稳产、高效的目的。本文使用TM影像数据与实地GPS定位相结合的方法,研究了冬小麦返青后叶面积指数及植株氮素含量的变化态势。结果表明:(1)TM影像的NDVI的地域性差异较大,且随纬度呈现极明显的线型负相关变化态势;(2)将用NDVI反演的LAI与实测的LAI进行比较,二者较为一致,其均方差根(RMSE)为0.111;(3)利用NDVI监测的小麦植株氮素含量与实地观测的植株氮素含量较为相近,二者的RMSE为0.085。总之,利用TM影像的NDVI可以快速、精确地监测返青期小麦的LAI和植株氮素营养状况。同时,本研究结果也可为冬小麦返青期的苗情诊断和管理决策提供及时、准确的信息支持。