IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

The purpose of this study is to evaluate the capability of surface radar backscatter models to estimate soil moisture over agricultural fields from fully polarimetric RADARSAT-2 C-band synthetic aperture radar (SAR) responses. For validation purposes, ground measurements over 44 sampling sites in eastern Ontario, Canada were carried out in the spring of 2008 simultaneously with satellite data acquisitions. Soil moisture retrieval was accomplished using two semi-empirical scattering models (Dubois and Oh) and the SAR image backscatter. Discrepancies between measured radar backscatter coefficients and those predicted by the models were previously reported, requiring correction factors to reduce biases associated with these semi-empirical approaches. Soil moisture was estimated by explicitly solving the two backscatter equations of the Dubois model, and using a look-up table (LUT) approach applied to the Oh model. Results showed that the Oh model in a cross-polarization (HH-HV) and Dubois in a co-polarization (HH-VV) inversion scheme provide the best estimates. These model configurations were implemented to produce multi-date soil moisture maps for the eastern Ontario site. To expand the range of validity of these soil moisture estimates, the maps produced by the Dubois and Oh models were uniquely combined. These estimates of absolute soil moisture were then used to derive spatial patterns of near-surface moisture content using the Getis statistic. The Getis statistic maps provide meaningful spatial information, demonstrating the potential of combining the Getis statistic and RADARSAT-2 data in predicting soil moisture conditions.

Observations over the last three decades show that desertification poses a serious threat to the livelihood and productivity of inhabitants of the Horqin Sandy Land region of China. We evaluated the dynamics and trends of changes of land cover in the Horqin Sandy Land by using Landsat archive images from 1975, 1987, 1999, and 2007. We applied two supervised classification methods, the self-organizing map neural network method and the subspace method. Our analyses revealed significant changes to land cover over the period 1975-2007. The area of cropland doubled over the last three decades. This expansion was accompanied by large increases in water consumption and considerable loss of areas of grassland and woodland. Many lakes and rivers shrank rapidly or disappeared in this region between 1975 and 2007. The sandy area expanded rapidly from 1975 to 1987 but gradually slowed thereafter.

We present the analyses of UARS MLS ozone data obtained by the Belgian Assimilation System for Chemical ObsErvations (BASCOE). This system, based on the 4D-var method, is dedicated to the assimilation of stratospheric chemistry observations. It uses a 3-D Chemical Transport Model (3D-CTM) including 57 chemical species with explicit calculation of stratospheric chemistry. The CTM is driven by ECMWF ERA-40 analyses of winds and temperature, with a horizontal grid of 3.75 in latitude by 5 in longitude, and with 37 pressure levels from the surface to 0.1 hPa. BASCOE has assimilated UARS MLS observations acquired during the period 1992-1997. We discuss how BASCOE is able to reproduce MLS data, and we evaluate the BASCOE analyses with respect to independent observations from UARS HALOE, ozonesondes, and ground-based lidars. An excellent agreement is found with independent observations (bias usually less than 10%), except in the lowermost stratosphere and in the Antarctic ozone hole. The performances of BASCOE ozone analyses are also compared to those of two other long-term ozone reanalyses; namely, ERA-40 and ERA-Interim, both from ECMWF. Finally, sensitivity test based on BASCOE free model runs suggest that ozone analyses during the ozone hole period would be greatly improved by driving BASCOE with the dynamical fields of the new ECMWF reanalyses ERA-Interim. This work is part of the Stratospheric Ozone Profile Record service raised by the GMES Service Element PROMOTE.

Fire, a natural disaster, has significant effects on ecosystems and plays a major role in deforestation, and it is a major source of trace gases, aerosols and carbon fluxes. Remote sensing is a valuable data source to investigate different phases of fire management. The Moderate Resolution Imaging Spectroradiometer (MODIS) has been designed to include specific characteristics for fire detection. It provides global coverage every 1 to 2 days. MODIS for forest fire monitoring has high detection accuracy, high radiometric resolution, moderate spatial resolution modes, and a high saturation level. Fires occur repeatedly in Iranian forests during the summer time. According to the Food and Agriculture Organization (FAO) reports, 0.06% of Iran's forests burn every year. Fire season in the northern part of Iran is from May until the end of October. The results show that 86.21% of the fires detected by MODIS from 2001 to 2008 occurred in cropland, grass land and plain regions. Most of these fires occurred in the eastern regions of the Mazandaran Sea. A correlation of 0.76 exists between the fire frequency and the rainfall. Areas with precipitation lower than 1000 mm experienced 86.01% of the fires. Most of the fires occurred at elevations below 500 m from mean sea level (MSL). The fire frequency has a correlation of 0.58 with the average monthly Normalized Difference Vegetation Index (NDVI) values. Temporal analysis from 2001 to 2008 shows that most of the fires occurred in June.

A number of energy balance models of variable complexity that use remotely sensed boundary conditions for producing spatially distributed maps of surface fluxes have been proposed. Validation typically involves comparing model output to flux tower observations at a handful of sites, and hence there is no way of evaluating the reliability of model output for the remaining pixels comprising a scene. To assess the uncertainty in flux estimation over a remote sensing scene requires one to conduct pixel-by-pixel comparisons of the output. The objective of this paper is to assess whether the simplifications made in a simple model lead to erroneous predictions or deviations from a more complex model and under which circumstances these deviations most likely occur. Two models, the S-SEBI and TSEB algorithms, which have potential for operationally monitoring ET with satellite data are described and a spatial inter-comparison is made. Comparisons of the spatially distributed flux maps from the two models are made using remotely sensed imagery collected over an agricultural test site in Northern Germany. With respect to model output for radiative and conductive fluxes no major differences are noted. Results for turbulent flux exchange demonstrate that under relatively dry conditions and over tall crops model output differs significantly. The overall conclusion is that under unstressed conditions and over homogeneous landcover a simple index model is adequate for determining the spatially distributed energy budget.

In most hydrologic modeling studies, the hypothesis is made that an improvement in the modeled soil moisture leads to an improvement in the modeled surface energy balance. The objective of this paper is to assess whether this hypothesis is true. The study was performed over the winter wheat fields in the AgriSAR 2006 domain. Remotely sensed soil moisture values and latent heat fluxes were used, in combination with in situ observations. First, the land cover and saturated subsurface flow parameters were estimated using the in situ observations. A spatially distributed model simulation was then performed, for which the Brooks-Corey parameters were derived from a soil texture map, and of which the results were validated using the remote sensing data. The remotely sensed soil moisture values were then used to optimize the Brooks-Corey parameters. As expected, a better performance with respect to the soil moisture estimation was obtained. However, this did not improve the latent heat flux estimates. This can be explained by the consumption of water from the deeper soil layers by the vegetation. The overall conclusion is that, under conditions where evapotranspiration is limited by energy and not by the soil moisture content, surface soil moisture values alone are not sufficient for the optimization of hydrologic model results. More data sets are needed for this purpose.

During April and May 2007, several hundred fires burned uncontrollably in Georgia and Florida. The smoke from these fire events were visible throughout the Southeastern United States and had a major impact on particulate matter (PM) air quality near the surface. In this study, we show the strength of polar orbiting and geostationary satellite data in capturing the spatial distribution and diurnal variability of columnar smoke aerosol optical depth from these fires. We quantitatively evaluate PM air quality from satellites and ground-based monitors, near and far away (> 300 km) from fire source regions. We also show the changes in organic carbon concentrations (a tracer for smoke aerosols) before, during and after these fire events. Finally, we use fire locations and emissions retrieved and estimated from satellite observations as input to a regional mesoscale transport model to forecast the spatial distribution of aerosols and their impact on PM air quality. During the fire events, near the source regions, total column 550 nm aerosol optical thickness (AOT) exceeded 1.0 on several days and ground-based PM2.5 mass (particles less than 2.5 mum in aerodynamic diameter) reached unhealthy levels ( > 65.5 mug m^-3). Since the aerosols were reasonably well mixed in the first 1-2 km (as estimated from meteorology), the column AOT values derived from both geostationary and polar orbiting satellites and the surface PM2.5 were well correlated (linear correlation coefficient, r > 0.7). Several hundred miles away from the fire sources, in Birmingham, AL, the impact of the fires were also seen through the high AOT's and PM2.5 values. Correspondingly, PM2.5 mass due to organic carbon obtained from ground-based monitors showed a three fold increase during fire events when compared to background values. Satellite data were especially useful in capturing PM2.5 air quality in areas where there were no ground-based monitors. Although the mesoscale transport model- captured the timing and location of aerosols, when compared to observations, the simulated mass concentrations are underestimated by nearly 70% due to various reasons including uncertainties in fire emission estimates, lack of chemistry in the model, and assumptions on vertical distribution of aerosols. Satellite products such as AOT, fire locations, and emissions from space-borne sensors are becoming a vital tool for assessing extreme events such as fires, smoke, and particulate matter air quality.

Destructive earthquakes challenge Earth Observation (EO) systems to demonstrate their usefulness in supporting intervention and relief actions. The use of EO data in a disaster context has been widely investigated from a theoretical point of view, but only recently the developed methods seem to have reached near to the operational use. In this paper a case study on the April 6th, 2009 earthquake ( M _w = 6.3) event, which stroke L'Aquila, Italy, is presented and commented. Although damage to the city was not extremely extensive, the case is interesting because it was handled by the authors in a real-time, emergency context. A new data fusion approach, between SAR and optical data, has been proposed. It shows that optical data are more suitable to distinguish between damage and non-damage classes, while SAR textures features allow to better distinguishing different classes of damages at block scale such as low and heavy damage.

In this paper, we develop a novel Graphics Processing Unit (GPU)-based high-performance Radiative Transfer Model (RTM) for the Infrared Atmospheric Sounding Interferometer (IASI) launched in 2006 onboard the first European meteorological polar-orbiting satellites, METOP-A. The proposed GPU RTM processes more than one profile at a time in order to gain a significant speedup compared to the case of processing just one profile at a time. The radiative transfer model performance in operational numerical weather prediction systems nowadays still limits the number of channels they can use in hyperspectral sounders to only a few hundreds. To take the full advantage of such high resolution infrared observations, a computationally efficient radiative transfer model is needed. Our GPU-based IASI radiative transfer model is developed to run on a low-cost personal supercomputer with 4 NVIDIA Tesla C1060 GPUs with total 960 cores, delivering near 4 TFlops theoretical peak performance. The model exhibited linear scaling with the number of graphics processing units. Computing 10 IASI radiance spectra simultaneously on a GPU, we reached 763x speedup for 1 GPU and 3024x speedup for all 4 GPUs, both with respect to the original single-threaded Fortran CPU code. The significant 3024x speedup means that the proposed GPU-based high-performance forward model is able to compute one day's amount of 1,296,000 IASI spectra within 6 minutes, whereas the original CPU-based version will impractically take more than 10 days. The GPU-based high-performance IASI radiative transfer model is suitable for the assimilation of the IASI radiance observations into the operational numerical weather forecast model.

The discrete wavelet transform (DWT)-based Set Partitioning in Hierarchical Trees (SPIHT) algorithm is widely used in many image compression systems. The time-consuming computation of the 9/7 discrete wavelet decomposition is usually the bottleneck of these systems. In order to perform real-time Reed-Solomon channel decoding and SPIHT+DWT source decoding on a massive bit stream of compressed images continuously down-linked from the satellite, we propose a novel graphic processing unit (GPU)-accelerated decoding system. In this system the GPU is used to compute the time-consuming inverse DWT, while multiple CPU threads are run in parallel for the remaining part of the system. Both CPU and GPU parts were carefully designed to have approximately the same processing speed to obtain the maximum throughput via a novel pipeline structure for processing continuous satellite images. As part of the SPIHT decoding system, the GPU-based inverse DWT is about 158 times faster than its CPU counterpart. Through the pipelined CPU and GPU heterogeneous computing, the entire decoding system approaches a speedup of 83x as compared to its single-threaded CPU counterpart. The proposed channel and source decoding system is able to decompress 1024x1024 satellite images at a speed of 90 frames per second.

Modern active and passive satellite and airborne sensors with higher temporal, spectral and spatial resolutions for Earth remote sensing result in a significant increase in data volume. This poses a challenge for data transmission over error-prone wireless links to a ground receiving station. Low-density parity-check (LDPC) codes have been adopted in modern communication systems for robust error correction. Demands for LDPC decoders at a ground receiving station for efficient and flexible data communication links have inspired the usage of a cost-effective high-performance computing device. In this paper we propose a graphic-processing-unit (GPU)-based regular LDPC decoders with the log sum-product iterative decoding algorithm (log-SPA). The GPU code was written to run NVIDIA GPUs using the compute unified device architecture (CUDA) language with a novel implementation of asynchronous data transfer for LDPC decoding. Experimental results showed that the proposed GPU-based high-throughput regular LDPC decoder achieved a significant 271x speedup compared to its CPU-based single-threaded counterpart written in the C language.

Remote sensing is utilized across a wide array of disciplines, including resource management, disaster relief planning, environmental assessment, and climate change impact analysis. The data volume and processing requirements associated with remote sensing are rapidly expanding as a result of the increasing number of satellite and airborne sensors, greater data accessibility, and expanded utilization of data intensive technologies such as imaging spectroscopy. However, due to the limited ability of current computing systems to gracefully scale with application requirements, particularly in the desktop level market, large amounts of data are currently underutilized or never explored. Computing limitations thus constrain our ability to efficiently and accurately address key science questions using remote sensing. The current evolution in general purpose computing on Graphics Processing Units (GPUs), an emerging technology that is redefining the field of high performance computing, facilitates significantly improved computing capabilities for current and future image analysis needs. We demonstrate the advantages of this technology by accelerating an imaging spectroscopy algorithm for submerged marine habitats using GPU computing. Results indicate that considerable improvement in performance can be achieved using a single GPU on a standard desktop computer. This technology has enormous potential for continued growth exploiting high performance computing, and provides the foundation for significantly enhanced remote sensing capabilities.

For the large-volume ultraspectral sounder data, compression is desirable to save storage space and transmission time. To retrieve the geophysical paramters without losing precision the ultraspectral sounder data compression has to be lossless. Recently there is a boom on the use of graphic processor units (GPU) for speedup of scientific computations. By identifying the time dominant portions of the code that can be executed in parallel, significant speedup can be achieved by using GPU. Predictive partitioned vector quantization (PPVQ) has been proven to be an effective lossless compression scheme for ultraspectral sounder data. It consists of linear prediction, bit depth partitioning, vector quantization, and entropy coding. Two most time consuming stages of linear prediction and vector quantization are chosen for GPU-based implementation. By exploiting the data parallel characteristics of these two stages, a spatial division design shows a speedup of 72x in our four-GPU-based implementation of the PPVQ compression scheme.

Keyhole Markup Language (KML), the de facto standard for representing, visualizing and transmitting geospatial data on Virtual Globes, lately approved by the Open Geospatial Consortium (OGC), Inc., has been widely used by the Earth Science communities. Most of the popular virtual globe systems, such as Google Earth and Microsoft Virtual Earth support KML format. This new online approach is changing the way in which scientists and the general public interact with three-dimensional geospatial data in a virtual environment. The so-called A-Train, a series of seven U.S. and international Sun-synchronous satellites, flying in tight formation just seconds to minutes apart, across the local afternoon equator, has been producing abundant measurements of vertical profiles of atmospheric parameters. This paper first discusses the key technical points for access to and visualization of three-dimensional Earth science data by using KML and Virtual Globes. Then, the Virtual Globes are taken as a virtual three-dimensional platform to synergize horizontal data and vertical profiles along the A-Train tracks to explore the scientific relationships among multiple physical phenomena. Two kinds of scientific scenarios are investigated: a) The relationships among cloud, aerosol and atmospheric temperature, and b) the relationships among cloud, wind and precipitation. The seamless visualization and synergy of multiple versatile datasets facilitate scientists to easily explore and find critical relationships between some phenomena that would not be easily found otherwise.

This paper describes the air quality data products and services available through Giovanni, a web based tool for access, visualization, and analysis of satellite remote sensing products, and also model output and surface observations relevant to global air quality. Available datasets include total column aerosol measurements from numerous satellite instruments, column NO₂ and SO₂, vertical aerosol products from CALIPSO, surface PM_2.5 concentrations over the continental U.S, and speciated model Aerosol Optical Depth. Giovanni was designed to make satellite and ground-based data easier to use; it does not require separate access to or downloading of data sets, making the visualizations and analysis services accessible to both the novice and the experienced user. Giovanni air quality data products are provided on a common grid and can also be obtained in KMZ format for Google Earth visualization. This feature allows collocation of datasets to aid in analysis of pollution events and to facilitate satellite/monitor comparisons and aerosol intercomparison studies in a fraction of the time compared to traditional methods. Giovanni also supports multiple interoperability protocols which permit data sharing with other online tools, in order to enhance access to the datasets for improved air quality decision making. The Giovanni team is currently actively involved in several data networking initiatives with service oriented tools at other institutions such as DataFed.

The four articles in this special section are devoted to the deployment of analytical data models for geospatial user communities.

The international research community involved in the GMES, INSPIRE, and GEOSS initiatives is actively pursuing the specification of information and service oriented solutions for geospatial data interoperability. A prominent interoperability issue pertains to discovery services. From an information technology point of view, the challenge is to implement interoperable discovery services for data and processing resources that are collected and managed using multidisciplinary standards and tools. We have designed and experimented a new, improved model and technology for the discovery of geospatial resources: an advanced catalog service featuring additional functionalities like mediation and asynchronous distribution. Besides, the described solution addresses another well-recognized issue: the integration of discovery and access services for complex resources-such as EO datasets.

In the near future, data from two microwave remote sensors at L-band will enable estimation of near-surface soil moisture. The European Space Agency's Soil Moisture and Salinity Mission (SMOS) launched in November 2009, and NASA is developing a new L-band soil moisture mission named Soil Moisture Active/Passive (SMAP). Soil moisture retrieval theory is well-established, but many details of its application, including the effects of spatial scale, are still being studied. To support these two L-band missions, studies are needed to improve our understanding of the various error sources associated with retrieval of soil moisture from satellite sensors. The purpose of this study is to quantify the magnitude of the scaling error created by the existence of sub-footprint scale variability in soil and vegetation properties, which have nonlinear relationships with emitted microwave energy. The scaling error is related to different functional relationships between surface microwave emissivity and soil moisture that exist for different soils and land cover types within a satellite footprint. We address this problem using single-frequency, single-polarization passive L-band microwave simulations for an Upper Midwest agricultural region in the United States. Making several simplifying assumptions, the analysis performed here helps provide guidance and define limits for future mission requirements by indicating hydrological and landscape conditions under which large errors are expected, and other conditions that are more conducive to accurate soil moisture estimates. Errors associated with spatial aggregation of highly variable land surface characteristics within 40 km satellite ?footprints? were found to be larger than the baseline mission requirements of 0.04-0.06 Volumetric Soil Moisture (VSM) over much of the study area. Soil moisture estimation errors were especially large and positive over portions of the domain characterized by mixtures of forests, wetlands, and open wate- r or mixtures of forest and pasture. However, by eliminating from the analysis areas with high vegetation water content or substantial surface water fractions, conditions that have well-documented adverse effects on soil moisture retrieval, we obtained errors that are in line with these mission requirements. We developed a parameterization for effective optical depth (?_eff) based on the standard deviation of optical depth (?_?) within a footprint in order to improve soil moisture retrieval in the presence of highly variable vegetation density. Use of the resulting parameterized optical depth in retrievals eliminated almost all of the soil moisture biases in our simulated setting. Operationally, the empirical relationship between ?_eff and ?_? would need to be determined a priori based on intensive measurements from ground-based instrumentation networks or via tuning of the algorithm. Due to this issue and other confounding factors, results are not expected to be as good as in the simulated cases presented here. However, the relationship found in this study is likely to be consistent across landscapes, so any correction following this functional form would very likely lead to large improvements over retrievals based simply on weighted mean properties.

This study evaluates the sensitivity of a multiscale ensemble assimilation system to different configurations of satellite soil moisture observations, namely the retrieval accuracy, spatial availability, and revisit time. We perform horizontally coupled assimilation experiments where pixels are updated not only by observations at the same location but also all in the study domain. Carrying out sensitivity studies within a multiscale assimilation system is a significant advancement over previous studies that used a 1-D assimilation framework where all horizontal grids are uncoupled. Twin experiments are performed with synthetic soil moisture retrievals. The hydrologic modeling system is forced with satellite estimated rainfall, and the assimilation performance is evaluated against model simulations using in-situ measured rainfall. The study shows that the assimilation performance is most sensitive to the spatial availability of soil moisture observations, then to revisit time and least sensitive to retrieval accuracy. The horizontally coupled assimilation system performs reasonably well even with large observation errors, and it is less sensitive to retrieval accuracy than the uncoupled system, as reported by previous studies. This suggests that more information may be extracted from satellite soil moisture observations using multiscale assimilation systems resulting in a potentially higher value of such satellite products.

In many areas of Earth science, including climate change research and operational oceanography, there is a need for near real-time integration of data from heterogeneous and spatially distributed sensors, in particular in situ and space-based sensors. The data integration, as provided by a smart sensor web, enables numerous improvements, namely, (1) adaptive sampling for more efficient use of expensive space-based and in situ sensing assets, (2) higher fidelity information gathering from data sources through integration of complementary data sets, and (3) improved sensor calibration. Our ocean-observing smart sensor web presented herein is composed of both mobile and fixed underwater in situ ocean sensing assets and Earth Observing System satellite sensors providing larger-scale sensing. An acoustic communications network forms a critical link in the web, facilitating adaptive sampling and calibration. We report on the development of various elements of this smart sensor web, including (a) a cable-connected mooring system with a profiler under real-time control with inductive battery charging; (b) a glider with integrated acoustic communications and broadband receiving capability; (c) an integrated acoustic navigation and communication network; (d) satellite sensor elements; and (e) a predictive model via the Regional Ocean Modeling System interacting with satellite sensor control.

A variety of sensors have been developed and deployed to monitor the Earth, ranging from in situ seismographic networks to hyperspectral imaging instruments carried onboard NASA satellites. Despite an impressive collection of sensing assets, there is still much untapped potential, as evidenced by the limited number of studies that successfully employ high-resolution data from multiple instruments. Sensor webs offer the potential to go beyond simple data fusion by dynamically combining sensing assets into coordinated, multi-instrument observers of specific geophysical objects, phenomena, and processes. In this paper, we describe Adaptive Sky, an algorithm package for sensor webs developed through funding from the NASA Earth Science Technology Office under the Advanced Information Systems Technology program. Fundamentally, Adaptive Sky aims to relate the observations from one sensor at time t to the observations from another sensor at time t ', providing a “gestalt,” or unified, perspective that is more than the sum of its parts. A scenario involving the eruption of Bezymianny Volcano on the remote Kamchatka Peninsula on 14 October 2007 demonstrates conceptually how Adaptive Sky can be leveraged to create unprecedented spatio-temporal and phenomenological coverage of a complex geophysical event of interest, despite limitations inherent in the individual sensors.

Monitoring aerosols over wide areas is important for the assessment of the population's exposure to health relevant particulate matter (PM). Satellite observations of aerosol optical depth (AOD) can contribute to the improvement of highly needed analyzed and forecasted distributions of PM when combined with a model and ground-based observations. In this paper, we evaluate the contribution of column AOD observations from a future imager on a geostationary satellite by performing an Observing System Simulation Experiment (OSSE). In the OSSE simulated imager, AOD observations and ground-based PM observations are assimilated in the chemistry transport model LOTOS-EUROS to assess the added value of the satellite observations relative to the value of ground-based observations. Results show that in highly polluted situations, the imager AOD observations improve analyzed and forecasted PM2.5 concentrations even in the vicinity of simultaneously incorporated ground-based PM observations. The added value of the proposed imager is small when considering monthly averaged PM distributions. This is attributed to relatively large errors in the imager AODs in case of background aerosol loads coupled to the fact that the imager AODs are column values and an indirect estimate of PM. In the future, model improvements and optimization of the assimilation system should be achieved for better handling of situations with aerosol plumes above the boundary layer and satellite observations containing aerosol profile information. With the suggested improvements, the developed OSSE will form a powerful tool for determining the added value of future missions and defining requirements for planned satellite observations.

This study focuses on the way urban dynamic processes challenge existing monitoring approaches and how urban structure types (UST) support an effective urban management. Due to their microstructure and the instability of shape, the differentiation of settlement structures is substantially difficult. Hence, highly sophisticated data such as color infrared (CIR) orthophotos as well as methods of image analysis, e.g., segmentation of objects will be employed. It will be shown how an object-oriented analysis strategy with CIR aerial photographs can be used to detect and discriminate different urban structure types by describing typical characteristics of color, texture, shape, and context. The urban structure classification is characterized by identifying different types of buildings (different types of housing, industrial buildings, infrastructure) and open spaces (woodland, community gardens, parks), their structural composition in terms of the amount, connectivity, and distribution of impervious surfaces, green spaces, and other open spaces on an aggregated neighborhood scale. This investigation contributes to a transferable methodology to monitor the urban dynamics and structure on a local level.

Research in the area of 3-D city modeling from remote sensed data greatly developed in recent years with an emphasis on systems dealing with the detection and representation of man-made objects, such as buildings and streets. While these systems produce accurate representations of urban environments, they ignore information about the vegetation component of a city. This paper presents a complete image analysis system which, from high-resolution color infrared (CIR) digital images, and a Digital Surface Model (DSM), extracts, segments, and classifies vegetation in high density urban areas, with very high reliability. The process starts with the extraction of all vegetation areas using a supervised classification system based on a Support Vector Machines (SVM) classifier. The result of this first step is further on used to separate trees from lawns using texture criteria computed on the DSM. Tree crown borders are identified through a robust region growing algorithm based on tree-shape criteria. A SVM classifier gives the species class for each tree-region previously identified. This classification is used to enhance the appearance of 3-D city models by a realistic representation of vegetation according to the vegetation land use, shape and tree species.

Understanding the spatial distribution of fine particle sulfate (SO₄ ^2-) concentrations is important for optimizing emission control strategies and assessing the population health impact due to exposure to SO₄ ^2-. Aerosol remote sensors aboard polar orbit satellites can help expand the sparse ground monitoring networks into regions currently not covered. We developed a generalized additive model (GAM) using MISR fractional aerosol optical depths (AODs) scaled by GEOS-Chem aerosol profiles to predict ground-level SO₄ ^2- concentrations. This advanced spatial statistical model was compared with alternative models to evaluate the effectiveness of including simulated aerosol vertical profiles and adopting an advanced statistical model structure in terms of improving the AOD- SO₄ ^2- association. The GAM is able to explain 70% of the variability in SO₄ ^2- concentrations measured at the surface, and the predicted spatial surface of annual average SO₄ ^2- concentrations are consistent with interpolated contours from ground measurements. Comparisons with alternative models demonstrate significant advantages of using model-scaled lower-air fractional AODs instead of their corresponding column values. The nonlinear association between SO₄ ^2- concentrations and fractional AODs makes the GAM a more suitable model structure than conventional linear regressions.

A system for processing Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite-based 0.532 and 1.064 elastic and polarization lidar datasets for global aerosol transport model assimilation is described. A method for constructing one-degree along-track and cloud-free signal composite averages, consistent with Navy Aerosol Analysis and Prediction System (NAAPS) model gridding, using CALIOP Level 1B attenuated backscatter and Level 2 cloud boundary-height products is outlined. Optimal vertical resolutions and relative signal uncertainties for the composite signal averages are described for both day and nighttime measurement scenarios. Depolarization profiles are described for the 0.532 channel as well as attenuated color ratio profiles using 0.532 and 1.064 attenuated backscatter measurements. Constrained by NAAPS model aerosol optical depths, processed attenuated backscatter profiles are inverted to solve for extinction and backscatter coefficients, their ratio, and extinction coefficient profiles which serve as the basis for data assimilation.

Maps describing the eco-hydrology of inland wetland systems in Africa are needed to identify and implement appropriate adaptive management plans related to land use and land cover. Many African countries lack regional baseline information on the temporal extent, distribution and characteristics of wetlands. This information is provided here in the form of maps which characterize two wetland sites of international importance in Malawi and Mozambique. Multi-temporal L-band Synthetic Aperture Radar (SAR) datasets are combined with Landsat Thematic Mapper and ASTER images, digital elevation models, and vegetation species data to provide information on wetland ecology and hydrology. These data were used as input to a hybrid, Decision Tree classifier and a Principal Components Analysis classification approach to produce maps depicting the spatial distribution of vegetation species and characterizing the wetland dynamics. The maps exhibit classification accuracies of 89% and 84% for the two sites respectively. The L-band SAR datasets have proved to be an essential information source in the production of these maps due to (i) frequent cloud cover/smoke which reduces the temporal coverage of optical data, and (ii) a systematic observation strategy and frequent image acquisition which enables characterization of the flood dynamics at a high temporal resolution.

This paper investigates the potential of multi-temporal C- and L-band SAR data, acquired within a short revisiting time (1-2 weeks), to map temporal changes of surface soil moisture content (m_v) underneath agricultural crops. The analysed data consist of a new ground and SAR data set acquired on a weekly basis from late April to early August 2006 over the DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) agricultural site (Northern Germany) during the European Space Agency 2006 AgriSAR campaign. The paper firstly investigates the main scattering mechanisms characterizing the interaction between the SAR signal and crops, such as winter wheat and rape. Then, the relationship between backscatter and soil moisture content temporal changes as a function of different SAR bands and polarizations is studied. Observations indicate that rationing of the multi-temporal radar backscatter can be a simple and effective way to decouple the effect of vegetation and surface roughness from the effect of soil moisture changes, when volume scattering is not dominant. The study also assesses to which extent changes in the incidence angle between subsequent radar acquisitions may affect the radar sensitivity to soil moisture content. Finally, an algorithm based on the change detection technique retrieving superficial soil moisture content is proposed and assessed both on simulated and experimental data. Results indicate that for crops relatively insensitive to volume scattering in the vegetation canopy (as for instance winter wheat at C-band or winter rape and winter wheat at L-band), m_v can be retrieved during the whole growing season, with accuracies ranging between 5% and 6% [m³/m³]. We also show that low incidence angles (e.g., 20-35 ) and HH polarization are generally better suited to m_v retrieval than VV polarization and higher incidence angles.

Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here, we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23° N-50° N and 128° W-65° W. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

Rice agriculture is an important crop that influences land-atmosphere interactions and requires substantial resources for flood management. Multitemporal acquisition strategies provide an opportunity to improve rice mapping and monitoring of hydroperiod. The objectives of this study were to 1) delineate rice paddies with Phased Array L-band Synthetic Aperture Radar (PALSAR) fine-beam single/dual (FBS/D) mode measurements and 2) integrate multitemporal, ScanSAR Wide-Beam 1 (WB1)- and Moderate Resolution Imaging Spectroradiometer (MODIS)- observations for flood frequency mapping. Multitemporal and multiscale PALSAR and MODIS imagery were collected over the study region in the Sacramento Valley, California, USA. A decision-tree approach utilized multitemporal FBS (HH polarization) data to classify rice fields and WB1 measurements to assess paddy flood status. High temporal frequency MODIS products further characterized hydroperiod for each individual rice paddy using a relationship between the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI). Validation found the PALSAR-derived rice paddy extent maps and hydroperiod products to possess very high overall accuracies (95% overall accuracy). Agreement between MODIS and PALSAR flood products was strong with agreement between 85-94% at four comparison dates. By using complementing products and the strengths of each instrument, image acquisition strategies and monitoring protocol can be enhanced. The results highlight how the integration of multitemporal PALSAR and MODIS can be used to generate valuable agro-ecological information products in an operational context.

In this paper, different methods for the evaluation of building detection algorithms are compared. Whereas pixel-based evaluation gives estimates of the area that is correctly classified, the results are distorted by errors at the building outlines. These distortions are potentially in an order of 30%. Object-based evaluation techniques are less affected by such errors. However, the performance metrics thus delivered are sometimes considered to be less objective, because the definition of a ldquocorrect detectionrdquo is not unique. Based on a critical review of existing performance metrics, selected methods for the evaluation of building detection results are presented. These methods are used to evaluate the results of two different building detection algorithms in two test sites. A comparison of the evaluation techniques shows that they highlight different properties of the building detection results. As a consequence, a comprehensive evaluation strategy involving quality metrics derived by different methods is proposed.

We applied the Numerical Maxwell Model of three-dimensional simulations (NMM3D) in the Dense Media Radiative Theory (DMRT) to calculate backscattering coefficients. The particles' positions are computer-generated and the subsequent Foldy-Lax equations solved numerically. The phase matrix in NMM3D has significant cross-polarization, particularly when the particles are densely packed. The NMM3D model is combined with DMRT in calculating the microwave scattering by dry snow. The NMM3D/DMRT equations are solved by an iterative solution up to the second order in the case of small to moderate optical thickness. The numerical results of NMM3D/DMRT are illustrated and compared with QCA/DMRT. The QCA/DMRT and NMM3D/DMRT results are also applied to compare with data from two specific datasets from the second Cold Land Processes Experiment (CLPX II) in Alaska and Colorado. The data are obtained at the Ku-band (13.95 GHz) observations using airborne imaging polarimetric scatterometer (POLSCAT). It is shown that the model predictions agree with the field measurements for both co-polarization and cross-polarization. For the Alaska region, the average snow depth and snow density are used as the inputs for DMRT. The grain size, selected from within the range of the ground measurements, is used as a best-fit parameter within the range. For the Colorado region, we use the Variable Infiltration Capacity Model (VIC) to obtain the input snow profiles for NMM3D/DMRT.

Recent technological advances in the performance of small micro-lasers and multi-channel multi-event photo-detectors have enabled the development of experimental airborne lidar (light detection and ranging) systems based on a low-SNR (LSNR) paradigm. Due to dense point spacing (tens of points per square meter) and sub-decimeter range resolution, LSNR lidar can likely enable detection of meter-scale targets that would go unnoticed by traditional lidar technology. Small vehicle obstructions and other similar targets in the beach and littoral zones are of particular interest, because of LSNR lidar's applicability to the near-shore environment and the general desire to improve detection of antivehicle and antipersonnel obstacles in the coastal zone. A target detection procedure is presented that exploits the detailed information available from LSNR lidar data while diminishing the effect of spurious noise events. Consideration is given to detection in both topographic and bathymetric scenarios. Data sets for target detection analysis are supplied by a numerical sensor simulator developed at the University of Florida. Target detection performance is evaluated as a function of environmental characteristics, such as water clarity and depth, and system parameters, specifically transmitted pulse energy and laser pulse repetition frequency. Analysis of results with regards to consideration for future system design is discussed.

Land surface shortwave albedo plays a central role in global and regional climate modeling. In this study, we analyzed the land surface shortwave broadband albedo from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2008. The statistical results are obtained using MODIS Collection 5 land surface albedo (MCD43C3), land cover (MOD12C1) datasets, and Global Energy and Water-cycle Experiment (GEWEX) surface radiation data. The results include all nine-year shortwave Black-Sky albedo (BSA) and White-Sky albedo (WSA) variability for global, Northern Hemisphere (NH), Southern Hemisphere (SH), and 15 International Geosphere-Biosphere Program (IGBP) ecosystem surface types; each has a discernible signature. We also compared spatial and temporal variations of MODIS albedos with other datasets: International Satellite Cloud Climatology Project (ISCCP), 21 Global Circulation Models (GCMs)-which were used in the fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC-AR4)-and GEWEX albedos. The comparison results show that most GCM-simulated albedos are lower than the remotely sensed MODIS data. The MODIS-based global average land surface albedo is 0.24, and has its peak value in the winter and lowest in summer. Comparison of global albedo anomalies from MODIS shows a small decrease of ~ 0.01 during these years in the Northern Hemisphere (NH), and increases of ~ 0.01 in the Southern Hemisphere (SH). Moreover, the map of the nine-year global MODIS albedo, and normalized difference vegetation index (NDVI) variation trends, are correlated in this paper. We also summarize global and zonal albedos for different IGBP land surface classes, and present the global and zonal albedos under both snow-covered and snow-free conditions.

The objective of this paper is to present a series of improvements on the Joint Research Centre Two-stream Inversion Package (JRC-TIP) that enhance its effectiveness to generate reliable surface products and associated uncertainties from surface albedo values. Lookup tables (LUTs) are built in the observation space from the JRC-TIP and are used to store solutions obtained from off-line dedicated procedures on selected sets of prior conditions. This new approach drastically limits the occurrence of questionable solutions, revealed by outliers in the retrievals, often associated with local instead of global minima and ensures that the retrieved values are insensitive to small variations in the input albedo values. This TIP table-based approach also reduces considerably the computing time requirement, which is a definite asset in the systematic application of the TIP against large data sets of surface albedo products.

Albedo may be derived from clear-sky remote-sensing images through inversion of a bidirectional reflectance distribution function (BRDF) model and angular integration. This paper proposes a new multi-angular and multi-spectral BRDF model (ASK Model) based on the kernel-driven conception and gives an effective algorithm for broadband albedo retrieval. By adding component spectra into kernels as prior known driven variables, the new model expresses BRDF as a linear combination of wavelength-independent kernel coefficients and kernels expressed as functions of both observation geometry and wavelength. In this way, the new model brings advantages in two aspects. On the one hand, for model inversion, the new angular and spectral kernels allow combination of observations not only at different viewing and illumination angles, but also at different wavebands, which give more reliable inversion results especially when the angular data are limited. On the other hand, different from traditional narrowband-to-broadband conversion, which gives empirical weights at several available bands, the new algorithm derives broadband albedo as a weighted linear combination of kernel integrations both in angular and wavelength domains. As model validation, ground-based measurements in Heihe Field Campaign have been chosen. Results show that the new model can accurately rebuild BRDF and derive broadband albedo. Furthermore, the new model and algorithm are demonstrated using CHRIS and EOS-MODIS data. The retrieved broadband albedos have been compared with MODIS BRDF/albedo product and the in situ measurements. Results show that the presented algorithm can be employed to retrieve broadband albedo from multisource satellite observations.

Harmful algal blooms (HABs) pose an enormous threat to the U.S. marine habitation and economy in the coastal waters. Federal and state coastal administrators have been devising a state-of-the-art monitoring and forecasting system for these HAB events. The efficacy of a monitoring and forecasting system relies on the performance of HAB detection. We propose a machine learning based spatio-temporal data mining approach for the detection of HAB events in the region of the Gulf of Mexico. In this study, a spatio-temporal cubical neighborhood around the training sample is introduced to retrieve relevant spectral information of both HAB and non-HAB classes. The feature relevance is studied through mutual information criterion to understand the important features in classifying HABs from non-HABs. Kernel based support vector machine is used as a classifier in the detection of HABs. This approach gives a significant performance improvement by reducing the false alarm rate. Further, with the achieved classification accuracy, the seasonal variations and sequential occurrence of algal blooms are predicted from spatio-temporal datasets. New variability visualization is introduced to illustrate the dynamic behavior of HABs across space and time.

In this paper, we explore the relationship between land use practices and landslides triggered by rainfall in eastern Taiwan. Before-and-after satellite images, combined with an artificial neural network method, enable the classification of land use and landslide zones. Genetic algorithms are used to evaluate the land use factors causing landslides. Using the geographic information system ArcGIS to support spatial reasoning, predictive maps are produced. The results suggest that the proposed method and procedures can be an effective tool for landslide monitoring and would be easily transferred to other similar applications.

The N-finder algorithm (N-FINDR) suffers from several issues in its practical implementation. One is its search region which is usually the entire data space. Another related issue is its excessive computation. A third issue is its use of random initial conditions which causes inconsistency in final results that can not be reproducible if a search for endmembers is not exhaustive. This paper resolves the first two issues by developing two approaches to speed-up of the N-FINDR computation while implementing a recently developed random pixel purity index (RPPI) to alleviate the third issue. First of all, it narrows down the search region for the N-FINDR to a feasible range, called region of interest (ROI), where two ways are proposed, data sphering/thresholding and RPPI, to be used as a pre-processing to find a desired ROI. Second, three methods are developed to reduce computing load of simplex volume computation by simplifying matrix determinant. Third, to further reduce computational complexity three sequential N-FINDR algorithms are implemented by finding one endmember after another in sequence instead of finding all endmembers together at once. The conducted experiments demonstrate that while the proposed fast algorithms can greatly reduce computational complexity, their performance remains as good as the N-FINDR is and is not compromised by reduction of the search region to an ROI.

The Brazilian Pantanal is a large continuous tropical wetland with large biodiversity and many threatened habitats. The interplay between the distribution of vegetation, the hydrology, the climate and the geomorphology nourishes and sustains the large diversity of flora and fauna in this wetland, but it is poorly understood at the scale of the entire Pantanal. This study uses multi-temporal L-band ALOS/PALSAR and C-band RADARSAT-2 data to map ecosystems and create spatial-temporal maps of flood dynamics in the Brazilian Pantanal. First, an understanding of the backscattering characteristics of floodable and non-floodable habitats was developed. Second, a Level 1 object-based image analysis (OBIA) classification defining Forest, Savanna, Grasslands/Agriculture, Aquatic Vegetation and Open Water cover types was achieved with accuracy results of 81%. A Level 2 classification separating Flooded from Non-Flooded regions for five temporal periods over one year was also accomplished, showing the interannual variability among sub-regions in the Pantanal. Cross-sensor, multi-temporal SAR data was found to be useful in mapping both land cover and flood patterns in wetland areas. The generated maps will be a valuable asset for defining habitats required to conserve the Pantanal biodiversity and to mitigate the impacts of human development in the region.

Information on the distribution of tropical forests is critical to decision-making on a host of globally significant issues ranging from climate stabilization and biodiversity conservation to poverty reduction and human health. The majority of tropical nations need high-resolution, satellite-based maps of their forests as the international community now works to craft an incentive-based mechanism to compensate tropical nations for maintaining their forests intact. The effectiveness of such a mechanism will depend in large part on the capacity of current and near-future Earth observation satellites to provide information that meets the requirements of international monitoring protocols now being discussed. Here we assess the ability of a state-of-the-art satellite radar sensor, the ALOS/PALSAR, to support large-area land cover classification as well as high-resolution baseline mapping of tropical forest cover. Through a comprehensive comparative analysis involving twenty separate PALSAR- and Landsat-based classifications, we confirm the potential of PALSAR as an accurate (>90%) source for spatially explicit estimates of forest cover based on data and analyses from a large and diverse region encompassing the Xingu River headwaters in southeastern Amazonia. Pair-wise spatial comparisons among maps derived from PALSAR, Landsat, and PRODES, the Brazilian Amazon deforestation monitoring program, revealed a high degree of spatial similarity. Given that a long-term data record consisting of current and future spaceborne radar sensors is now expected, our results point to the important role that spaceborne imaging radar can play in complementing optical remote sensing to enable the design of robust forest monitoring systems.

Within the framework of Kyoto & Carbon Initiative of the Japanese Space Agency (JAXA), we used JERS-1 and ALOS/PALSAR radar images to build regional and continental scale mosaics of Sahara. The unique capability of L-band SAR to map subsurface structures in arid areas revealed previously unknown geological features: craters, faults, paleo-rivers. The latter are of particular interest for water resource detection in arid regions.

An extensive dataset of images acquired by the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) is investigated for clear-cut detection in the county of Västerbotten, Sweden. Strong forest/non-forest contrast and temporal consistency were found for the Fine Beam Dual HV-polarized backscatter in summer/fall. In consequence of a clear-cut between image acquisitions, the HV-backscatter dropped in most cases between 2 and 3 dB. Thus, a simple thresholding algorithm that exploits the temporal consistency of time series of HV-backscatter measurements has been developed for clear-cut detection. The detection algorithm was applied at pixel level to ALOS PALSAR strip images with a pixel size of 50 m. The performance of the detection algorithm was tested with three different threshold values (2.0, 2.5 and 3.0 dB). The classification accuracy increased from 57.4% to 78.2% for decreasing value of the threshold. Conversely, the classification error increased from 3.0% to 9.7%. For about 90% of the clear-felled polygons used for accuracy assessment the proportion of pixels correctly detected as clear-felled was above 50% when using a threshold value of 2.0 dB. For the threshold values of 2.5 and 3.0 dB the corresponding figures were 80% and 65%, respectively. The total area classified as clear-felled during the time frame of the ALOS PALSAR data differed by 5% compared to an estimate of notified fellings for the same period of time when using a detection threshold of 2.5 dB. The performance of the simple detection algorithm is reasonable when aiming at detecting clear-cuts, whereas there are shortcomings in terms of delineation.

Over lake shores, altimetric waveforms are generally contaminated by lands, rough lake surfaces, and lag effects of the altimeter's automatic gain control. To improve altimeter ranging accuracy and in turn to get better surface height measurement, contaminated waveforms should be retracked against geophysical corrections. In this paper, an improved threshold retracker (ITR) is developed to retrack waveforms over lakes. ITR considers not only the physical characteristics of the reflecting surface, but also the stochastic feature of waveform, and two new retrackers, the N-Beta function model, and the N-5-Beta function model, are also put forward to develop the waveform retracking program of this study. TOPEX/POSEIDON waveforms over Hulun Lake in the North China are retracked to monitor the temporal lake level variations. A comparison with the in situ hydrological data indicates ITR is very efficient to monitor the lake level variations with the retracked altimetric data. The result of our study shows accurate seasonal level variations and the descending trend of Hulun Lake.

Near-real time ocean surface currents derived from satellite altimeter (JASON-1, GFO, ENVISAT) and scatterometer (QSCAT) data on 1deg times 1deg resolution for world oceans (60deg S to 60deg N) are available online as ldquoOcean Surface Current Analyses-Real Time (OSCAR).rdquo The probability distribution function (PDF) of the current speeds (omega), constructed from global OSCAR data from 1992 to 2008, satisfies the two-parameter Weibull distribution reasonably well, and such a PDF has little seasonal and interannual variations. Knowledge on PDF of omega will improve the ensemble horizontal flux calculation, which contributes to the climate studies.

In this study, we used same-day 30-m spatial resolution Landsat-7/ETM+ and Terra/ASTER data to study the short-term development of active fires in the Brazilian Amazon between the overpasses of the two satellites at approximately 10:00 and 10:30 local times, respectively. We analyzed the spatial progression of fire fronts and the temporal changes in the extent of burning at the scales of the pixel sizes of MODIS (1 km) and GOES Imager (4 km). The progression of fire fronts varied between individual fires, but in most cases remained within the scale of a few 30-m pixels, while the total extent of burning detected typically increased during the 30 min between the ETM+ and ASTER observations. This is in accordance with the typical mid-morning upslope part of the diurnal cycle of fire activity observed previously by coarse resolution sensors. We also assessed the potential changes in derived validation results of active fire products from medium and coarse resolution sensors. We derived fire detection probabilities from Terra/MODIS as a function of the total number of 30-m fire pixels from ASTER (representing simultaneous reference data and, hence, the ldquotruthrdquo) and separately from ETM+ (representing nonsimultaneous reference data). We found spuriously increased detection probabilities using ETM+ resulting from the observed increase of fire activity between the ETM+ and ASTER acquisitions. While this effect can potentially be corrected for by a statistical adjustment, the overall recommendation is that a temporally unbiased sample of nonsimultaneous reference data should be used for validation.

Accurate soil moisture information is required for studying the global water and energy cycles as well as the carbon cycle. The AMSR-E sensor onboard NASA's Aqua satellite offers a new means to accurately retrieve soil moisture information at a regional and global scale. However, the characterization of the factors such as precipitation, vegetation, cloud, ground roughness, and ice-snow packs is sensitive to the retrieval of the soil moisture content from the remotely sensed data. This paper examines the models that are used to generate soil moisture products from US National Snow and Ice Data Center (NSIDC), and to adapt the models to improve the accuracy of soil moisture retrieval in Xinjiang, northwest China. The ground truth data collected by the WET and WatchDog instruments in Xinjiang were used to derive the empirical parameters for the regressive model that are suited to the conditions in Xinjiang. To improve the accuracy of inversion, the impact of precipitation's lag-effect on the surface soil moisture has been addressed using the parameters monthly bases, daily variation and the lag-effect impact of precipitation in the improved model. The improved model is then used to retrieve the soil moisture information from the AMSR-E data. A comparative study between the result from the proposed model and the NSIDC products of May to September 2009 were performed with the AMSR-E data. Validation with ground truth and the comparison indicate that the improved model performs better and produces more accurate soil moisture maps than the NSIDC products in the study area.

Since the launch of the Scanning Multichannel Microwave Radiometer (SMMR) in 1978, several studies have demonstrated the capability of spaceborne passive microwave sensors for mapping global snow water equivalent (SWE). Currently, SWE values are estimated operationally from microwave brightness temperatures measured by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and distributed through the National Snow and Ice Data Center (NSIDC). In this study, we report results regarding the comparison between AMSR-E SWE and SWE/snow depth values distributed by the Snow Data Assimilation System (SNODAS) product of the NOAA's National Operational Hydrologic Remote Sensing Center and snow depth measured by automatic weather stations of the World Meteorological Organization. Generally, we found poor correlation between the AMSR-E and SNODAS SWE/snow depth values. The algorithm performance improves when considering WMO data, though the number of samples used for the analysis might play a role in this sense. We discuss algorithm-related sources of error and uncertainties, such as vegetation and grain size. Moreover, we report results aimed at evaluating whether replacing the linear approach with a nonlinear one and not using the brightness temperatures and ancillary data sets combined as in the current approach but taken separately as inputs to the algorithm might improve the performance of the algorithm.