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| 1 | A new semi-empirical model for soil moisture content retrieval by ASAR and TM data in vegetation-covered areas显示文摘Active microwave and passive optical remote sensing data have demonstrated their respective advantages in inversion of surface soil moisture content. A new semi-empirical model is presented for soil moisture content retrieval in vegetation-covered areas, using ENVISAT-ASAR and LANDSAT-TM data collaboratively. Derivation of the algorithm is based on simplification of the Michigan Microwave Canopy Scattering Model (MIMICS). In the model, the ground surface is divided into a canopy layer and a soil layer, and empirical relationships simulated among vegetation water mass Wc, the backscatter coefficient σpq1, the bidirectional scattering coefficient σpq2 and the extinction coefficient τp . The key input parameters of the semi-empirical model are reduced to only the leaf area index (LAI), which can be easily inverted by the optical model PROSAIL, allowing coupling of the microwave and optical models to be achieved. Also, vegetation RMS height (Sveg) is introduced to correct for the radar-shadow effect caused by over-laying vegetation. Analysis of the parameter sensitivity of the semi-empirical model showed that when the regional Leaf Area Index is small (LAI≤3), the model is more applicable. Soil moisture distribution in the study area was mapped using the semi-empirical model and field ground measurements used for model validation. This showed that, after correction of the radar-shadow effect, the average relative error (Er) between ground-measured and semi-empirical model-derived estimates of soil moisture decreased from 17.6% to 10.4%, while the RMS reduced from 0.055 to 0.031 g cm-3. The accuracy of soil moisture estimates from the semi-empirical model is much better than for the MIMICS model (Er = 22.7%, RMS = 0.068 g cm-3), showing that the semi-empirical model is efficient at obtaining regional surface soil moisture contents when LAI is small. | YU Fan ZHAO YingShi | 2011 | Science China Earth Sciences2011，54，12： | 8 |
| 2 | Multi-scale MSDT inversion based on LAI spatial knowledge显示文摘Quantitative remote sensing inversion is ill-posed.The Moderate Resolution Imaging Spectroradiometer at 250 m resolution(MODIS_250m) contains two bands.To deal with this ill-posed inversion of MODIS_250m data,we propose a framework,the Multi-scale,Multi-stage,Sample-direction Dependent,Target-decisions(Multi-scale MSDT) inversion method,based on spa-tial knowledge.First,MODIS images(1 km,500 m,250 m) are used to extract multi-scale spatial knowledge.The inversion accuracy of MODIS_1km data is improved by reducing the impact of spatial heterogeneity.Then,coarse-scale inversion is taken as prior knowledge for the fine scale,again by inversion.The prior knowledge is updated after each inversion step.At each scale,MODIS_1km to MODIS_250m,the inversion is directed by the Uncertainty and Sensitivity Matrix(USM),and the most uncertain parameters are inversed by the most sensitive data.All remote sensing data are involved in the inversion,during which multi-scale spatial knowledge is introduced,to reduce the impact of spatial heterogeneity.The USM analysis is used to implement a reasonable allocation of limited remote sensing data in the model space.In the entire multi-scale inversion process,field data,spatial knowledge and multi-scale remote sensing data are all involved.As the multi-scale,multi-stage inversion is gradually refined,initial expectations of parameters become more reasonable and their uncertainty range is effectively reduced,so that the inversion becomes increasingly targeted.Finally,the method is tested by retrieving the Leaf Area Index(LAI) of the crop canopy in the Heihe River Basin.The results show that the proposed method is reliable. | ZHU XiaoHua FENG XiaoMing ZHAO YingShi | 2012 | Science China Earth Sciences2012，55，8： | 5 |
| 3 | Assimilation of ASAR Data with a Hydrologic and Semi-empirical Backscattering Coupled Model to Estimate Soil Moisture显示文摘The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly. | LIU Qian WANG Mingyu ZHAO Yingshi | 2010 | Chinese Geographical Science2010，20，3： | 3 |
| 4 | Estimating leaf area index by inversion of reflectance model for semiarid natural grasslands显示文摘The study developed an integrated reflectance model combining radiative transfer and geometric optical properties in order to inverse leaf area index(LAI) of semiarid natural grasslands.In order to better link remote sensing information with land plants,and facilitate regional and global climate change studies,the model introduced a simple but important geometrical similarity parameter related to plant crown shapes.The model revealed the influences of different plant crown shapes(such as spherical,cylindrical/cuboidal and conic crowns) on leaf/branch angle distribution frequencies,shadow ground coverage,shadowed or sunlit background fractions,canopy reflectance,and scene reflectance.The modeled reflectance data agreed with the measured ones in the three Leymus chinensis steppes with different degradation degrees,which validated the reflectance model.The lower the degradation degree was,the better the modeled data agreed with the measured data.After this reflectance model was coupled with the optimization inversion method,LAI over the entire study region was estimated once every eight days using the eight-day products of surface reflectance obtained by multi-spectral Moderate-Resolution Imaging Spectroradiometer(MODIS) during the growing seasons in 2002.The temporal and spatial patterns of inversed LAI for the steppes with different cover degrees,swamps,flood plains,and croplands agreed with the general laws and measurements very well.But for unused land cover types(sands,saline,and barren lands) and forestlands,totally accounting for about 10% of the study region,the reasonable LAI values were not derived by inversing,requiring further revising of the model or the development of a new model for them. | ZHANG Na ZHAO YingShi | 2009 | Science China Earth Sciences2009，52，1： | 3 |
| 5 | A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge显示文摘In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI. | ZHU Xiaohua ZHAO Yingshi FENG Xiaoming | 2013 | Chinese Geographical Science2013，23，5： | 1 |
| 6 | Cloud detection and analysis of MODIS image显示文摘 | Xiaoning Song Zhenhua Liu Yingshi Zhao | 2004 | IEEE Geoscience and Remote Sensing Symposium2004，，4： | 1 |
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