EFAST Method for Global Sensitivity Analysisof Remote Sensing Model’s Parameters

Sensitivity analysis for parameters of remote sensing physical models is a prerequisite for inversion.The EFAST(Extended Fourier Amplitude Sensitivity Test)as a global sensitivity analysis method,can analyze not only a single parameter’s sensitivity but also the coupling effects among parameters.It is usually applied to analyse parameters’ sensitivity of the high-dimensional nonlinear models.In this paper,the SAIL model is taken as an example,the EFAST method and the field measured data of winter wheat in Shunyi district in 2001 were applied to analyze the model parameters’ sensitivity throughout the growing season and in different growth stages respectively.The results are compared with those of the USM (Uncertainty and Sensitivity Matrix) method.The results show that either the EFAST or the USM method for parameters’ sensitivity analysis of the SAIL model is feasible;but the EFAST method,which takes into account of the coupling effects among all the parameters and the analysis result is global,compared to the USM method,is more objective and comprehensive.     

Analysis of deformation over permafrost regions of Qinghai-Tibet plateau based on permanent scatterers

The surface displacement by seasonally freezing bulge and thawing subsidence are the main hazards for engineering construction in permafrost regions, especially for the Qinghai-Tibet railway. One of the main problems is how to monitor the frozen ground's displacement in the process of construction and protection of the Qinghai-Tibetan railway. The technology of Permanent Scatterers (PS) has been successfully used to detect the long-term subsidence at urban areas. For detecting the subsidence of the frozen earth on Qinghai-Tibet Plateau, this paper extended the capability of the technology of PS to investigate deformation phenomena in vegetated area. The paper analyses an interferometric phase model, and presents improved PS algorithms for separating different components in interferometric phase. The proposed technique is implemented using ENVISAT ASAR images to detect the deformation over permafrost region of Qinghai-Tibet Plateau. The results are in concordance with results provided by a traditional ground levelling, which encourages future development using the Permanent Scatterers method to analyse deformation of the frozen earth on Qinghai-Tibet Plateau.     

A method for retrieving soil moisture in Tibet region by utilizing microwave index from TRMM/TMI data

According to simulation analysis of the advanced integral equation model (AIEM), there is a good linear relationship between emissivity and soil moisture under conditions of given roughness. The normalized difference of emissivities at 19.35 GHz and 10.65 GHz with vertical polarization can partly eliminate the influence of roughness and the squared correlation coefficient is about 0.985. This paper uses the normalized brightness temperature for retrieving soil moisture in Tibet from TRMM/TMI data. This method avoids parametrizing the land surface temperature which is a key parameter for the computation of emissivity. We make some sensitivity analysis for the atmosphere which is the main influence factor for our method. The analysis results indicate that our method is very good for clear days but is not very good when there is rainfall. We evaluate our algorithm by using the ground truth data obtained from GAME‐Tibet and the retrieval error of soil moisture is about 0.04m³ m^?3 relative to experimental data. The analysis indicates that the relationship obtained from the theoretical model should be revised through the local ground measurement data because the method is still influenced by roughness and vegetation. After making a regression revision, the retrieval error of soil moisture is under 0.02m³ m^?3. Finally, we retrieve the soil moisture in Tibet from TRMM/TMI data, and the distribution trend of retrieval results is consistent with the real world.     

Evaluation of soil nitrogen emissions from riparian zones coupling simple process-oriented models with remote sensing data

Riparian ecosystems have critical impacts on controlling the non-point source pollution and maintaining the health of aquatic ecosystems. In this study, a process oriented soil denitrification model was extended with algorithms from a simple nitrogen (N) cycle model and coupled to land surface remote sensing data to enhance its performance in spatial and temporal prediction of gaseous N emissions from soils in the riparian buffer zone surrounding the Guanting reservoir (China). The N emission model is based on chemical and physical relationships that govern the heat budget, soil moisture variations and nitrogen movement in soils. Besides soil water and heat processes, it includes nitrification, denitrification and ammonia (NH₃) volatilization. SPOT-5 and Landsat-5 TM satellite data were used to derive spatial land surface information and the temporal variation in land cover parameters was also used to drive the model. A laboratory-scale anaerobic incubation experiment was used to estimate the soil denitrification model parameters for the different soil types. An in situ field-scale experiment was conducted to calibrate and validate the soil temperature, moisture and nitrogen sub-models. An indirect method was used to verify simulated N emissions, resulting in a coefficient of determination of R² = 0.83 between simulated and observed values. Then the model was applied to the whole riparian buffer zone catchment, using the spatial resolution (10 m) of the SPOT-5 image. Model sensitivity analysis showed that soil moisture was the most sensitive parameter for gaseous N emissions and soil denitrification was the main process affecting N losses to the atmosphere in the riparian area. From the aspect of land use management around the Guanting reservoir, the spatial structure and distribution of land cover and land use types in the riparian area should be adapted, to enhance faster ecological restoration of the wetland ecological system surrounding this strategically important water resource.     

基于极化似然比的极化SAR影像变化检测

由于数据获取困难等问题,目前SAR影像变化检测方法多基于幅度,而较少引入极化信息.针对此方面的不足,以极化SAR数据为研究对象,在分析多极化SAR影像极化特征及其分布模型的基础上,构建极化似然比检验模型,以此进行不同时相的多极化SAR数据地表地物变化程度分析,通过设定恒虚警率确定变化区域,最后考虑地物空间信息剔出斑点噪声引起的孤立检测结果.利用多极化SAR数据进行算法的验证,并与图像比值法进行比较,实验表明:基于极化似然比方法可以有效区分地物的变化情况,且变化检测精度要优于图像比值法.     

Automatic detection of forest fire disturbance based on dynamic modelling from MODIS time-series observations

Forest disturbances provide an important reference and a basis for studying the carbon cycle, biodiversity, and eco-social development. Remote sensing is a promising data source for monitoring forest ecosystem dynamics and detecting disturbance areas. This research used a seasonal trend method to model Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series from 2007 to 2011 recursively with a fixed-size temporal sliding window and a step length of 1 (i.e. 16 days). Model parameter variations were monitored to detect changes in the structure of the time-series data. Significant changes in the time-series structure were captured as disturbance signals. The method was applied to the 2009 Minto Flats fire in Alaska, USA, and the forest-disturbance detection results obtained using the proposed method essentially agreed with the Monitoring Trends of Burned Severity data set. This result indicates that the proposed method can reliably reveal the occurrence of forest fire disturbances. Moreover, because the model parameter variations reflect the disturbance signal, and the modelling and detection process requires only MODIS NDVI time-series data without any other ancillary ground information, the disturbance area can be detected effectively and automatically.     

Evaluating the generalization ability of convolutional neural networks for built-up area extraction in different cities of China

The difficulty of build-up area extraction is due to complexity of remote sensing data in terms of heterogeneous appearance with large intra-class variations and lower inter-class variations. In order to extract the built-up area from Landsat 8-OLI images provided by Google earth engine(GEE), we propose a convolutional neural networks(CNN) utilizing spatial and spectral information synchronously, which is built in Google drive using Colaboratory-Keras. To train a CNN model with good generalization ability, we choose Beijing, Lanzhou, Chongqing, Suzhou and Guangzhou of China as the training sites, which are very different in term of natural environments. The Arc GIS-Model Builder is employed to automatically select 99 332 samples from the 38-m global built-up production of the European Space Agency(ESA) in 2014. The validate accuracy of the five experimental sites is higher than 90%. We compare the results with other existing building data products. The classification results of CNN can be very good for the details of the built-up areas, and greatly reduce the classification error and leakage error. We applied the well-trained CNN model to extract built-up areas of Chengdu, Xi’an, Zhengzhou, Harbin, Hefei, Wuhan, Kunming and Fuzhou, for the sake of evaluating the generalization ability of the CNN. The fine classification results of the eight sites indicate that the generalization ability of the well-trained CNN is pretty good. However, the extraction results of Xi’an, Zhengzhou and Hefei are poor. As for the training data, only Lanzhou is located in the northwest region, so the trained CNN has poor image classification ability in the northwest region of China.     

基于图像融合的反照率产品降尺度方法研究

提出了一种基于物理意义图像融合的反照率降尺度算法,用以增强现有全球陆表特征参量(GLASS)反照率产品的空间分辨率,快速生成高分辨率反照率。首先对GLASS反照率产品和HJ反射率数据进行预处理,包括图像重投影、图像裁剪、雪像元和无效值像元掩膜;然后基于朗伯假设,将HJ反射率数据通过窄波段到宽波段的转换得到初级的高分辨率反照率;在此基础上将低分辨率的GLASS反照率产品与高分辨率的HJ数据进行融合得到高分辨率的反照率产品,其中考虑了GLASS产品的空间响应;最后分析了融合得到的高分辨率反照率影像,并使用黑河中游地面实测数据对融合结果进行了验证。结果显示:利用基于物理意义图像融合的反照率降尺度算法得到的高分辨率反照率产品精度较高(RMSE为0.02左右),并且该方法快速高效,具有推广和发展成为业务化算法的潜力。     

图层约束下的点状自然灾害风险地图综合

点状自然灾害现象如地震、滑坡等,由于其特殊性,灾害风险与其周边的地理环境有着复杂的联系和相互作用,孕灾环境对灾情具有放大或缩小的效应,在制图综合过程中,不能只考虑单个灾害点个体,而因将与之相关的各因素综合考虑,从而判定其风险范围。基于此,在地理学与地图学的基础上,从灾害系统的角度考虑,探讨基于图层约束(LC)和模糊推理系统(FIS)相结合的点状现象自动综合的适用性问题,重点阐述基于图层约束理论和FIS算法相结合的滑坡灾害自动综合技术,并以滑坡灾害为例,构建了基于滑坡灾害程度区划、地形坡度、地貌区划、地震长期烈度区划、年暴雨日数、年均降水量等为约束图层的自动综合应用;通过多尺度综合分析结果表明,中国存在三大重点滑坡区:即青藏高原东部斜坡带、黄土高原滑坡区和太行山东麓、巫山、武陵山脉一线滑坡带。本研究为多尺度、多图层约束下的自然灾害风险地图自动综合提供了一种有效途径,同时对不同区域尺度下的灾害风险管理提供了更高效、更准确的决策支持和技术支撑。     

A multi-scale wavelet-based temperature and emissivity separation algorithm for hyperspectral thermal infrared data

ABSTRACT

Land surface temperature and emissivity are essential variables in numerous environmental studies. This article proposes a multi-scale wavelet-based temperature and emissivity separation (MSWTES) algorithm. MSWTES is based on the fact that the high frequencies of ground-leaving radiance and derived emissivity spectra using inaccurate temperature are both closely correlated with the atmospheric downward radiance spectrum. First, surface emissivity can be decomposed by multi-scale wavelet into an optimal level that can be derived from correlation between reconstructed high frequency of ground-leaving radiance and atmospheric downward radiance. Then the ratio of high-frequency energy to low-frequency energy of surface emissivity spectrum is used to measure the degree of atmospheric downward radiance residue in the calculated emissivity spectrum as well as the disparity between the initial surface temperature and the true value. Finally, we can derive the optimal estimate of surface temperature and calculate the surface emissivity spectrum accordingly with this criterion. The MSWTES is first tested by simulation data. When a noise-equivalent spectral error of 2.5 × 10^–9 W cm^?2 sr^?1 cm is considered, the average temperature bias is 0.027 K and the root mean square error (RMSE) of emissivity is less than 0.003, except at the low and high ends of the 750–1250 cm^?1 spectral region. Then, the MSWTES is applied to field measurements. As a whole, the MSWTES achieves an RMSE of 0.01 for emissivity retrieval under most conditions, but its accuracy degrades when sample emissivity is extremely low. Meanwhile, the MSWTES is compared to the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm. The performance of the MSWTES is better than that of the ISSTES, which demonstrates the good performance of the MSWTES.