The European Space Agency (ESA) launched the SMOS (Soil Moisture Ocean Salinity) mission in 2009; currently, multiple global soil moisture (SM) products are based on the measurements of its L-band (1.4 GHz) radiometer. We compared four SMOS products with each other: Level 2, Level 3, IC (INRA-CESBIO), and Near Real Time products. The comparisons focused on core validation sites (CVS), whose spatial representativeness errors allow the estimation of the SM product performance for bias-insensitive metrics (unbiased root mean square error (ubRMSE) and correlation (R), and anomaly R) with negligible uncertainty and for bias-sensitive metrics (mean difference (MD) and root mean square difference or RMSD) with acceptable uncertainty. When the products were compared with CVS independently, the results showed that the ubRMSE, R, and anomaly R of the IC product were better than those of the other products, while the MD was larger. However, the differences between the performances were smaller when the products were assessed using only the data points when each product had a valid retrieval. This indicates that the algorithms have similar performance and that data screening and quality flagging of the retrievals markedly affects the performance. The NASA Soil Moisture Active Passive (SMAP) mission produces a similar SM product as SMOS using an L-band radiometer. The closeness of the ubRMSE, R, and anomaly R performance of the IC product and the SMAP product (0.039 m3/m3vs. 0.041 m3/m3, 0.80 vs. 0.81, and 0.75 vs. 0.75) demonstrate that the SMOS and SMAP radiometers can achieve similar SM sensitivity.

Thermal infrared (TIR) remote sensing has been widely adopted for monitoring land surface temperature (LST). However, its application has been limited to cloud-free conditions, resulting in a need for LST retrieval methods that combine microwave (MW) and TIR channels. This is especially crucial in areas frequently covered by clouds. One limitation of the current LST retrieval methods is the absence of considering the penetration effect (PE) of MW, which leads to great uncertainty in barren and sparsely vegetated areas. To address this issue, this study proposes a new perspective that considers the PE to merge the LST retrieved from MW and TIR channels. The soil temperature integral equation is simplified based on the soil temperature and water content profiles. Consequently, a PE-based model is developed to convert the effective soil temperature into LST and merge the LST estimated from passive MW observations with those from MODIS LST products. The model considering PE performs better than the method that does not consider PE, as demonstrated by higherRand lower RMSE values. The PE-based model is then applied to AMSR-E data, and the estimated LST is found to fit well with the MODIS LST product (R = 0.91). Using this model, an all-sky LST is retrieved by merging passive microwave observations and MODIS LST products. Validation of the model at eight ground-based stations over the Tibetan Plateau demonstrates its reasonable accuracy in both clear-sky and cloudy conditions.

This study concerns the effects of considering vertically heterogeneous canopy structure when modeling microwave scattering from grassland with the Tor Vergata (TVG) model, which uses the matrix doubling method (MDM). The TVG model was extended with the M-volume approach to accommodate height-dependent variation in structure for every scatterer type. Used approach was to reproduce 1 – 10 GHz backscatter for all linear polarization combinations from an alpine meadow measured by a ground-based scatterometer with both the default (1-volume) and the M-volume approach with 3 volumes. Measured in-situ vegetation parameters were used to constrain the model. We found that both models were able to reproduce the angle-dependent backscattering for C- and X-band, measured on two afternoons, and the 31-day average measured radar return power for L-, S-, C-, and X-band within, or close to, the measurement uncertainty. Our analysis proved inconclusive on whether the 1- or 3-volume approach worked better for the considered grassland, but did show that the 3-volume approach allows for more flexibility in reproducing the actual angle-dependent backscattering for multiple frequencies, a flexibility that may prove necessary when more scattering angles are considered. Furthermore, predictions of the bistatic scattering coefficient at higher frequencies (C- and X-band) were significantly different between both models. For X-band with hh polarization differences up to 3 dB were found for the specular direction. We conclude that considering vertical heterogeneity of vegetation canopy structure with MDM leads to significantly different results than with the vertically homogeneous canopy approximations typically used.

Active and passive microwave characteristics of diurnal soil freeze-thaw transitions and their relationships are crucial for developing retrieval algorithms of the soil liquid water content ($\theta _{\mathrm {liq}}$) and freeze/thaw state, which, however, have been less explored. This study investigates these microwave characteristics and relationships via analysis of ground-based measurements of brightness temperature ($T_{B}$) and backscattering coefficients ($\sigma ^{0}$) in combination with simulations performed with the Tor Vergata discrete radiative transfer model. Both an L-band (1.4 GHz) radiometer ELBARA-III and a wide-band (1–10 GHz) scatterometer are installed in a seasonally frozen Tibetan meadow ecosystem to measure diurnal variations of$T_{B}$and copolarized$\sigma ^{0}$at both hh ($\sigma _{\mathrm {hh}}^{0}$) and vv ($\sigma _{\mathrm {vv}}^{0}$) polarizations. Analysis of measurements collected between December 2017 and March 2018 shows that 1) diurnal cycles are observed in both$T_{B}$and$\sigma ^{0}$due to the change in surface$\theta _{\mathrm {liq}}$caused by diurnal soil freeze-thaw transitions; 2) a negatively linear relationship is found between$e$and$\sigma ^{0}$regardless of frequency, polarization combinations, and observation angles; 3) slopes ($\beta$) of linearly fit equations between$e^{H}$and$\sigma _{\mathrm {hh}}^{0}$decrease with increasing observation angles of ELBARA-III, while the ones between$e^{V}$and$\sigma _{\mathrm {vv}}^{\mathrm {0 {}}}$increase with increasing observation angles; and 4) correlations between$e$and$\sigma ^{0}$increase with decreasing microwave frequency of$\sigma ^{0}$measurements and ELBARA-III observation angles, and magnitudes of diurnal$\sigma ^{0}$cycles also increase with decreasing microwave frequency. Moreover, the calibrated Tor Vergata model shows capability to reproduce both diurnal$e$and$\sigma ^{\mathrm {0 {}}}$variations as well as to quantify their relationships at different frequencies and observation angles.

An unexplored aspect of L-band microwave emission is the impact of soil moisture and soil temperature (SMST) profile dynamics on diurnal brightness temperature ( TB) signatures of frozen soil. This study investigates this effect by comparing the TBsimulations of layered ( TB,l) and uniform ( TB,u) soils using a newly developed integrated land emission model. The multilayer Wilheit model and the single-layer Fresnel model are adopted to compute the smooth soil reflectivity for the layered and uniform soils, respectively. A four-phase dielectric mixing model is used to calculate the soil permittivity ( εs). A data set of concurrent ELBARA-III TBand SMST profile measurements performed in a seasonally frozen Tibetan meadow ecosystem is used for the analysis. The simulated TB,lconsidering SMST profile information captures well the ELBARA-III measurements with low biases (≤6 K) and high correlations ( R2≥ 0.88). TB,uproduced based on the Fresnel model using the soil moisture of 2.5 cm is more consistent with the TB,l. The sensitivity test of averaging SMST profile below 2.5 cm leads to maximum differences of 2 K in TB,lsimulations, indicating that the TBvariations are primary dominated by the SMST dynamics at the surface layer. A sensitivity test of the Wilheit model to different εsparameterizations shows that the dielectric model of Zhang et al. is comparable to the four-phase dielectric model in simulating TB,l, while the Mironov et al. 's model demonstrates larger biases for frozen soil with, on average, 2.2% clay content, 49.7% sand content, and a bulk density of 1 g·cm-3.

The soil moisture active passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSD of not more than 0.04 m3/m3volumetric accuracy in the top 5 cm for vegetation with a water content of less than 5 kg/m2. Single-channel algorithm (SCA) and dual-channel algorithm (DCA) are implemented for the processing of SMAP radiometer data. The SCA using the vertically polarized brightness temperature (SCA-V) has been providing satisfactory SM retrievals. However, the DCA using prelaunch design and algorithm parameters for vertical and horizontal polarization data has a marginal performance. In this article, we show that with the updates of the roughness parameter h and the polarization mixing parameters Q, a modified DCA (MDCA) can achieve improved accuracy over DCA; it also allows for the retrieval of vegetation optical depth (VOD or τ). The retrieval performance of MDCA is assessed and compared with SCA-V and DCA using four years (April 1, 2015 to March 31, 2019) of in situ data from core validation sites (CVSs) and sparse networks. The assessment shows that SCA-V still outperforms all the implemented algorithms.

The NASA SMAP (Soil Moisture Active Passive) mission provides a global coverage of soil moisture measurements based on its L-band microwave radiometer every 2-3 days at about 40 km resolution. The soil moisture retrieval algorithms model the brightness temperature as a function of soil moisture, surface conditions and vegetation. External data sources inform the algorithms about the surface conditions and vegetation, which enable the retrieval of soil moisture. The inversion process contains uncertainties related to radiometer measurements, forward model assumptions and ancillary data sources. This study focuses on the uncertainties that depend on the seasonal evolution of the surface conditions and vegetation. The study compares the SMAP and core validation site (CVS) soil moisture values over a period of four years to extract the evolution of performance metrics over time. The analysis showed that most CVS that include managed agriculture exhibit significant time-dependent seasonal bias. This bias was linked to seasonal temperature cycle, which is a proxy to several features that can cause seasonally dependent errors in the SMAP product.

This letter presents an experiment to explore the nonisothermal effects on temperature and emissivity separation (TES). The innovation of this experiment lies in its design, which highlights the contrast between isothermal and nonisothermal conditions in emissivity measurements. We artificially created a sharply contrasting nonisothermal soil surface using liquid nitrogen cooling and solar heating. The iterative spectrally smooth TES (ISSTES) algorithm was used to process the experimental data. The analyzed results of the experimental data show that the nonisothermal conditions have a significant effect on TES. The bias of retrieved emissivity increases with the component temperature difference as well as with wavelength. The bias around the split window band can reach up to 1% when the difference of the component temperature is 40K. Considering that 1% error in emissivity can cause approximately 1K error of retrieved land surface temperature (LST), the nonisothermal effects on emissivity cannot be ignored. We hope that this experiment will arouse attention of the nonisothermal effects on TES and call for more efforts to be devoted to this issue in the future.

This letter presents the results evaluating retrievals of liquid water content (θliq) performed with a zero-order radiative transfer (τ-ω) model under frozen and thawed soil conditions from Soil Moisture Active Passive (SMAP) and ELBARA-III brightness temperature (TBp) measurements collected over a Tibetan meadow ecosystem. A good agreement is found between time series of the SMAP and ELBARA-III measured TBpresulting in a Pearson product-moment coefficient (R) larger than 0.87. Differences noted between the two data sets can be associated with discrepancies in θliqmeasured in the specific footprints, whereby the SMAP measurements are best explained by the in situ θliq. Furthermore, the in situ θliqhas a better agreement with the horizontally polarized SMAP and ELBARA-III measurements (THB ) in the cold season, whereas the vertically polarized measurements (TVB ) are1111better correlated with θliqin the warm season. With the implementation of new vegetation and surface roughness parameterizations for the τ-ω model, the dynamics of in situ θliqis better reproduced by corresponding retrievals for both frozen and thawed soil conditions, leading to the reduction in the unbiased root-mean-square error (ubRMSE) by more than 31% in comparison with these retrievals using SMAP default parameterizations. Notably, the single-channel algorithm configured with the new parameterizations using SMAP TVB measured during the ascending overpass provides the best θliqretrievals with a ubRMSE of 0.035 m3·m-3that is well within the SMAP mission requirements.

Active and passive microwave signatures respond differently to the land surface and provide complementary information on the characteristics of the observed scenes. The objective of this paper is to explore the synergy of active radar and passive radiometer observations at the same spatial scale to constrain a discrete radiative transfer model, the Tor Vergata (TVG) model, to gain insights into the microwave scattering and emission mechanisms over grasslands. The TVG model can simultaneously simulate the backscattering coefficient and emissivity with a set of input parameters. To calibrate this model, in situ soil moisture and temperature data collected from the Maqu area in the northeastern region of the Tibetan Plateau, interpolated leaf area index (LA!) data from the Moderate Resolution Imaging Spectroradiometer LAI eight-day products, and concurrent and coincident Soil Moisture Active Passive (SMAP) radar and radiometer observations are used. Because this model needs numerous input parameters to be driven, the extended Fourier amplitude sensitivity test is first applied to conduct global sensitivity analysis (GSA) to select the sensitive and insensitive parameters. Only the most sensitive parameters are defined as free variables, to separately calibrate the activeonly model (TVG-A), the passive-only model (TVG-P), and the active and passive combined model (TVG-AP). The accuracy of the calibrated models is evaluated by comparing the SMAP observations and the model simulations. The results show that TVG-AP can well reproduce the backscattering coefficient and brightness temperature, with correlation coefficients of 0.87, 0.89, 0.78, and 0.43 and root-mean-square errors of 0.49 dB, 0.52 dB, 7.20 K, and 10.47 K for σHHo, σVVo, TBH, and TBV, respectively. In contrast, TVG-A and TVG-P can only accurately model the backscattering coefficient and brightness temperature, respectively. Without any modifications of the calibrated parameters, the error metrics computed from the validation data are slightly worse than those of the calibration data. These results demonstrate the feasibility of the synergistic use of SMAP active radar and passive radiometer observations under the unified framework of a physical model. In addition, the results demonstrate the necessity and effectiveness of applying GSA in model optimization. It is expected that these findings can contribute to the development of model-based soil moisture retrieval methods using active and passive microwave remote sensing data.

L-band passive microwave remote sensing is one of the most effective methods to map the global soil moisture distribution, yet, at which soil depth satellites are measuring is still inconclusive. Recently, with the Lv's multilayer soil effective temperature scheme, such depth information can be revealed in the framework of the zeroth-order incoherent model when soil temperature varies linearly with soil optical depth. In this paper, we examine the relationships between soil temperature microwave sensing depth, penetration depth, and soil effective temperature, considering the nonlinear case. The soil temperature sensing depth often also named penetration depth is redefined as the depth where soil temperature equals the soil effective temperature. A method is developed to estimate soil temperature sensing depth from one pair of soil temperature and moisture measurement at an arbitrary depth, the soil surface temperature, and the deep soil temperature which is assumed to be constant in time. The method can be used to estimate the soil effective temperature and soil temperature sensing depth.

Uncertainty of roughness parameters has effect on soil moisture retrievals with backscatter models from Synthetic Aperture Radar observations. The uncertainty of soil moisture retrievals is important information for the usability of these estimates. In this paper we introduce a methodology to estimate the uncertainty of effective roughness parameters in the Integral Equation Method surface backscatter model, using a Bayesian Markov Chain Monte Carlo approach. Using Sentinel-1 imagery we demonstrate the methodology for a selected field, showing the posterior uncertainty distributions of the roughness parameters, and the effect on the backscatter model simulations and soil moisture inversions. The estimated total uncertainty of the soil moisture retrievals with the optimum parameter set is 0.043 m3/m3, which is slightly higher than the root mean square error of 0.040 m3/m3of the retrievals compared to in situ soil moisture measurements.

A scatterometer is installed on a meadow over the Tibetan Plateau in August 2017 to measure the full polarimetric backscattering coefficient σ0over a wide frequency range (1 - 10 GHz) year-round. In this paper we describe the setup of scatterometer as well as the activities undertaken to test the reliability of the system. The measured radar cross section of a dihedral reflector matches the theoretical model for 3 - 10 GHz after the calibration. The temperature-induced systematic error in the radar return will be accounted for by using the antenna cross coupling as a reference. For the retrieval of σ0the frequency-dependent antenna radiation pattern and the site geometry are accounted for. Fading will be dealt with by using frequency agility techniques.

Abstract Virtual garment display plays an important role in fashion design for it can directly show the design effect of the garment without having to make a sample garment like traditional clothing industry. In this paper, we propose an end‐to‐end virtual garment display method based on Conditional Generative Adversarial Networks. Different from existing 3D virtual garment methods which need complex interactions and domain‐specific user knowledge, our method only need users to input a desired fashion sketch and a specified fabric image then the image of the virtual garment whose shape and texture are consistent with the input fashion sketch and fabric image can be shown out quickly and automatically. Moreover, it can also be extended to contour images and garment images, which further improves the reuse rate of fashion design. Compared with the existing image‐to‐image methods, the quality of images generated by our method is better in terms of color and shape.

The Soil Moisture Active Passive (SMAP) satellite mission launched in January 2015 provides worldwide soil moisture (SM) monitoring based on L-band brightness temperature (TBp) measurements at horizontal (TBH) and vertical (TBV) polarizations. This paper presents a performance assessment of SMAP soil emission model and SM retrieval algorithms for a Tibetan desert ecosystem. It is found that the SMAP emission model largely underestimates the SMAP measured THB(≈ 15 K), and the TBVis underestimated during dry-down episodes. A cold bias is noted for the SMAP effective temperature due to underestimation of soil temperature, leading to the TBpunderestimation (>5 K). The remaining TBHunderestimation is found to be related to the surface roughness parameterization that underestimates its effect on modulating the TBpmeasurements. Further, the topography and uncertainty of soil information are found to have minor impacts on the TBpsimulations. The SMAP baseline SM products produced by single-channel algorithm (SCA) using the TBVmeasurements capture the measured SM dynamics well, while an underestimation is noted for the dry-down periods because of TBVunderestimation. The products based on the SCA with TBHmeasurements underestimate the SM due to underestimation of TBH, and the dual-channel algorithm overestimates the SM. After implementing a new surface roughness parameterization and improving the soil temperature and texture information, the deficiencies noted above in TBpsimulation and SM retrieval are greatly resolved. This indicates that the SMAP SM retrievals can be enhanced by improving both surface roughness and adopted soil temperature and texture information for Tibetan desert ecosystem.

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W) mission. AMSR2 fills the void left by the loss of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) after almost 10 years. Both missions provide brightness temperature observations that are used to retrieve soil moisture. Merging AMSR-E and AMSR2 will help build a consistent long-term dataset. Before tackling the integration of AMSR-E and AMSR2 it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites. Three products that rely on different algorithms were evaluated; the JAXA Soil Moisture Algorithm (JAXA), the Land Parameter Retrieval Model (LPRM), and the Single Channel Algorithm (SCA). Results indicate that overall the SCA has the best performance based upon the metrics considered.

Since the beginning of its routine science operation in March 2015, the NASA SMAP observatory has been returning interference-mitigated brightness temperature observations at L-band (1.41 GHz) frequency from space. The resulting data enable frequent global mapping of soil moisture with a retrieval uncertainty below 0.040 m3/m3at a 36 km spatial scale. This paper describes the development and validation of an enhanced version of the current standard soil moisture product. Compared with the standard product that is posted on a 36 km grid, the new enhanced product is posted on a 9 km grid. Derived from the same time-ordered brightness temperature observations that feed the current standard passive soil moisture product, the enhanced passive soil moisture product leverages on the Backus-Gilbert optimal interpolation technique that more fully utilizes the additional information from the original radiometer observations to achieve global mapping of soil moisture with enhanced clarity. The resulting enhanced soil moisture product was assessed using long-term in situ soil moisture observations from core validation sites located in diverse biomes and was found to exhibit an average retrieval uncertainty below 0.040 m3/m3. As of December 2016, the enhanced soil moisture product has been made available to the public from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived standard soil moisture product (L2SMP) provides soil moisture estimates posted on a 36-km fixed Earth grid using brightness temperature observations and ancillary data. A beta quality version of L2SMP was released to the public in October, 2015, Version 3 validated L2SMP soil moisture data were released in May, 2016, and Version 4 L2SMP data were released in December, 2016. Version 4 data are processed using the same soil moisture retrieval algorithms as previous versions, but now include retrieved soil moisture from both the 6 am descending orbits and the 6 pm ascending orbits. Validation of 19 months of the standard L2SMP product was done for both AM and PM retrievals using in situ measurements from global core cal/val sites. Accuracy of the soil moisture retrievals averaged over the core sites showed that SMAP accuracy requirements are being met.

Soil freeze–thaw transition monitoring is essential for quantifying climate change and hydrologic dynamics over cold regions, for instance, the Third Pole. We investigate the L-band (1.4 GHz) microwave emission characteristics of soil freeze–thaw cycle via analysis of tower-based brightness temperature ($T_{{{{\mathrm {B}}}}}^{p}$) measurements in combination with simulations performed by a model of soil microwave emission considering vertical variations of permittivity and temperature. Vegetation effects are modeled using Tor Vergata discrete emission model. The ELBARA-III radiometer is installed in a seasonally frozen Tibetan grassland site to measure diurnal cycles of L-band$T_{{{{\mathrm {B}}}}}^{p}$every 30 min, and supporting micrometeorological as well as volumetric soil moisture ($\theta $) and temperature profile measurements are also conducted. Soil freezing/thawing phases are clearly distinguished by using$T_{{{{\mathrm {B}}}}}^{p}$measurements at two polarizations, and further analyses show that: 1) the four-phase dielectric mixing model is appropriate for estimating permittivity of frozen soil; 2) the soil effective temperature is well comparable with the temperature at 25 cm depth when soil liquid water is freezing, while it is closer to the one measured at 5 cm when soil ice is thawing; and 3) the impact on$T_{{{{\mathrm {B}}}}}^{p}$caused by diurnal changes of ground permittivity is dominating the impact of changing ground temperature. Moreover, the simulations performed with the integrated Tor Vergata emission model and Noah land surface model indicate that the$T_{{{{\mathrm {B}}}}}^{p}$signatures of diurnal soil freeze–thaw cycle is more sensitive to the liquid water content of the soil surface layer than thein situmeasurements taken at 5 cm depth.

GCOM-W AMSR2 provides continuity following AMSR-E and the opportunity to generate a global long-term satellite soil moisture data record from the same instrument type. Various soil moisture products are being developed using AMSR observations. The JAXA soil moisture along with the Single Channel Algorithm (SCA) product were evaluated using in situ observations from different geographical domains. Both the JAXA and SCA soil moisture estimates capture the overall climatological features and the overall spatial structure of the two products is similar. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture. The SCA performs well over low and moderately vegetated areas. This study focuses on the development of the AMSR2 soil moisture product. Validation results using in situ observations from diverse climate and land cover conditions will be presented.

Estimating forest above-ground biomass (AGB) and monitoring its variation are relevant for sustainable forest management, monitoring global change, carbon accounting, particularly for the Qilian Mountains (QMs), a water resource protection zone. In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. An optimized k-Nearest Neighbor (k-NN) method was determined by varying both the mathematical formulation of the algorithm and remote sensing data input which resulted in 3,000 different model configurations. Following the sun-canopy-sensor plus C (SCS+C) topographic correction, performance of the optimized k-NN method was satisfied (R2=0.59, RMSE=24.92 ton/ha) which indicated that the optimized k-NN is capable of operational applications of forest AGB estimates in regions where only a few inventory data are available. Afterwards, the calibrated BIOME-BGC was applied to simulate the carbon fluxes over QMs forests with satisfactory accuracy. Finally, the dynamic analysis and modeling of forest AGB was conducted based on the remotely sensed estimation of forest AGB and the annual forest AGB increment from the ecological process model.

This study explores retrieval of wall-to-wall forest above-ground biomass (AGB) over Jilin province in Northeast China, using the optimized non-parametric k-NN method, the 7thNational Forest Inventory (NFI) data, and the remote sensing data: Landsat-TM/ETM+ images. For pixel-based validation, the estimated result was compared to the NFI data by leave-one-out process and R2= 0.40 and RMSE = 54.29 tons/hm2. For county-scale validation, the result was verified by the intensive forest sub-compartment data of eight county and R2= 0.80 and RMSE = 34.26 tons/hm2.

Earth observation x in the second half of the twentieth century. ITC, established in the fifties of the 20thcentury as a postgraduate institute, has been evolving together with the EO discipline. Its activity focuses on education and capacity building of professionals and institutions of emerging economies, i.e. facilitating the use of advanced technologies in less developed environments.

A stochastic approach is developed to resolve the scale variability between point and aerospace measurements in ocean color match-up sites. The model used the differences between in situ and aerospace-observed spectra and ocean color model inversion to estimate the subscale variability of apparent and inherent optical properties (IOPs). The model was tested and validated against three sets of ocean color data: simulated, in situ measured, and satellite data sets. The results showed that the variability of chlorophyll-a absorption was derived with high accuracy. Errors in derived subscale variability of detritus-gelbstoff absorption and particle scattering were larger than those of chlorophyll-a. The subscale radiometric variability was found to be proportional to that of IOPs and decreased with increasing water turbidity. The subpixel variability of reduced resolution ocean color image was derived with less than 12% of relative errors in clear and moderate turbid waters. Larger errors were obtained in estuarine turbid waters. Better accuracy was obtained for match-up sites with high internal contrast, i.e., spatial variability.

With the arisen spaceborne multi-parameter Synthetic Aperture Radar (SAR) systems, such as Envisat ASAR, TerraSAR-X, ALOS PALSAR, and RADARSAT-2, the interest of crop mapping has been increasing. The present study compares the capabilities of the multi-parameter SAR in discriminating the main crop types by object-based classification in Haian county of Jiangsu province, South China. Two kinds of information, SAR intensity based and SAR statistical properties based are used for Maximum Likelihood Classification (MLC) and Minimum Distance Classification (MDC) respectively. The results show that, the L-band SAR can uniquely identify mulberry from dry-land crops, such as maize and vegetable and C-band SAR has some advantages in mapping rice. Specifically, the polarimetric RADARASAT-2 data can identify the rice with accuracy about 75% ~ 80% which is similar as the result from X-band TerraSAR-X Spotlight data but higher than that from C-band dual-polarization Envisat ASAR data. Nevertheless, both of X- and C-band can hardly separate the mulberry from the other dry-land crops.

Foliage and soil temperatures are key variables for assessing the exchanges of turbulent heat fluxes between vegetated land and the atmosphere. Using multiple-view-angle thermal-infrared (TIR) observations, the temperatures of soil and vegetation may be retrieved. However, particularly for sparsely vegetated areas, the soil and vegetation component temperatures in the sun and in the shade may be very different depending on the solar radiation, the physical properties of the surface, and the meteorological conditions. This may interfere with a correct retrieval of component temperatures, but it might also yield extra information related to canopy structure. Both are strong reasons to investigate this phenomenon in some more detail. To this end, the relationship between the TIR radiance directionality and the component temperatures has been analyzed. In this paper, we extend the four-stream radiative transfer (RT) formalism of the Scattering by Arbitrarily Inclined Leaves model family to the TIR domain. This new approach enables us to simulate the multiple scattering and emission inside a geometrically homogenous but thermodynamically heterogeneous canopy for optical as well as thermal radiation using the same modeling framework. In this way top-of-canopy thermal radiances observed under multiple viewing angles can be related to the temperatures of sunlit and shaded soil and sunlit and shaded leaves. In this paper, we describe the development of this unified optical-thermal RT theory and demonstrate its capabilities. A preliminary validation using an experimental data set collected in the Shunyi remote sensing field campaign in China is briefly addressed

The SEBS model (the surface energy balance system) based on land surface energy balance equation is used to estimate sensible heat flux and latent heat flux using remotely sensed data. In This work, The SEBS model is validated with two sets of data collected in two field experiment on winter-wheat field in Shunyi county of Beijing (116/spl deg/ 26' E-117/spl deg/ E; 40/spl deg/ N-40/spl deg/ 21' N) and on bare soil in Changping county of Beijing (116/spl deg/ 26' E- 116/spl deg/ 28' E; 40/spl deg/ 10' N-40/spl deg/ 12' N),China. Sensible and latent heat flux measured by eddy correlation method are compared with these SEBS estimates. The results show: (1) diurnal variant of Sensible and latent heat flux estimated bv SEBS basically agreed with the measured by eddy correlation method both on winter-wheat field and on bare soil, but the performance on winter-wheat field is better than on bare soil, the performance of sensible flux is better than that of latent flux. (2) Both on winter-wheat field and on bare soil, the precision of sensible heat flux estimated by SEBS is higher than that of latent heat flux, while the SEBS model performs better on winter-wheat field than on bare soil. (3) The sensitivity of SEBS to even' parameter is different. The SEBS model is most sensitive to the available energy, up to 0.3, while it is more sensitive to surface-air temperature difference and aerodynamic resistance, up to 0.1 and 0.09 respectively.

This paper introduces the surface energy balance system (SEBS) and its application for spatial-temporal estimation of actual evaporation using remote sensing data. Two case studies are presented, one for a large inland basin, the Urumqi River Basin, in Northwest China and the other for the whole Netherlands. After the presentation of the theory and processing procedures, NOAA/AVHRR scenes are used to derive land surface parameters as the inputs of SEBS. The HANTS (harmonic analysis of time series) algorithm is then used for reconstructing regional and temporal monthly and annual evaporation over the study areas. Time series of evaporative fraction and actual evaporation are successfully reconstructed for the two study areas and the estimated values are verified with the in-situ data. The results of this study demonstrate that SEBS algorithm can be used for spatial-temporal estimation of actual evaporation with an acceptable accuracy.

Roughness height for heat transfer is a crucial parameter in estimation of heat transfer between the land surface and the atmosphere, especially when radiometric measurements are used. Although many empirical formulations have been proposed, the uncertainties associated with these formulations are shown to be large, especially over sparse canopies. In this contribution, a simple physically based formula is derived for the estimation of the roughness height for heat transfer for bare soil surfaces. The new formula is validated with measurements collected in a filed campaign in Xiaotangshan, near Beijing, China in 2002. The present model in further shown to be able to explain the diurnal variation in the roughness height for heat transfer. The turbulent heat fluxes estimated using radiometric measurements as inputs are markedly improved when this new formula is used.

Evaporation provides the link between the energy and water budgets at the land surface. Accurate measurements of evaporation rates at large spatial scales are central to understanding land and atmosphere feedback. However, with the paucity of available surface observations for many portions of the globe, the use of modeled evaporation using satellite-based remotely sensed inputs is a potentially viable surrogate. The surface energy balance system (SEBS), which estimates atmospheric turbulent heat fluxes and evaporative fraction using satellite derived radiation fluxes and surface temperatures coupled with near-surface meteorological variables, is used to estimate surface energy fluxes over the Oklahoma region of the USA during the warm season. These simulations are assessed by comparison with observations from the ARM-CART energy balance Bowen ratio (EBBR).

A mixture of foliage and soil is thermally heterogeneous, so the radiometric temperature of the mixture depends on view direction. A simple linear mixture model was applied to estimate the component surface temperatures of foliage and soil temperatures. The potential of directional observations in the thermal infrared region for land surface studies is a largely uncharted area of research. The availability of the dual-view Along Track Scanning Radiometer (ATSR) observations led to explore new opportunities in this direction. In the context of studies on heat transfer at heterogeneous land surfaces, multiangular thermal infrared (TIR) observations offer the opportunity of overcoming fundamental difficulties in modeling sparse canopies. Three case studies were performed on the estimation of the component temperatures of foliage and soil. The first one included the use of multi-angular field measurements at view angles of 0/spl deg/, 23/spl deg/ and 52/spl deg/. The second and third one were done with directional ATSR observations at view angles of 0/spl deg/ and 53/spl deg/ only. Different models have been proposed in literature to interpret observations of directional exitance: (1) simple geometric (deterministic) models of the system, (2) radiative transfer within a complete canopy, and (3) radiative transfer in an inhomogeneous thick layer of vegetation. Our approach is based on the third modeling concept. A target comprising a mixture of foliage and soil is characterized by the gap fraction, and observed radiance is described as a weighted sum of foliage radiance and soil radiance, with the weights being the gap fraction and its complement, respectively.