The soil phase-transition temperature (PTT) is critical for accurately determining the soil freeze–thaw (FT) state, which is essential for developing surface FT state identification algorithms based on remote sensing technology and reliably validating FT products. In this study, we develop an innovative approach for determining soil PTT in the region of the dense soil moisture and soil temperature monitoring network in the Qinghai Lake Basin (QLB-NET). We employ four advanced models—polynomial regression (PR), random forest (RF), feedforward neural network (FNN), and transformer (XFMR) models—to establish relationships between soil PTT and various environmental factors for daily and site-specific soil PTT estimation. Soil PTT values derived from asigmoidcurve fitting (SCF) method that combines physical theory with empirical modeling are used in the models as the target data considering soil properties and topographic features as predictors. Additionally, we systematically evaluate the models’ performance and compare the results against those of the fixed 0°C threshold methods. The results indicate that most soil freezing temperatures range between -1°C and 0°C, and thawing temperatures range between -0.5°C and 0.5°C, with occasional freezing temperatures above 0°C. Model-derived soil PTT significantly outperforms the traditional 0°C threshold in terms of soil FT state classification accuracy, particularly during the frozen and FT transitional seasons. The XFMR model achieves superior performance in an internal comparative analysis, highlighting its particular effectiveness for soil PTT determination. These results provide valuable insights for improving remote sensing-based FT product validation and permafrost monitoring.

Due to the lack of in-situ measurements, direct validations can only be carried out at limited pixels and cannot be expanded to a regional or global scale. The three-cornered hat (TCH) method is essentially an optimal solution based on the numerical method, allowing an arbitrary number of datasets (at least three) to simultaneously participate in the calculation, with a tolerance to the error cross-correlation to a certain extent. However, in TCH, only a basic error model is adopted, which describes the products as mutually differing only in an additive random error. This violates our common recognition of the error model (i.e., affine error model) applied to describing products like soil moisture (SM). This work aims to improve the TCH method by updating the basic error model with the affine error model, and the reliability of the improved TCH method is evaluated using simulated soil moisture datasets and multisource soil moisture products against in-situ SM measurements.

Vegetation gravimetric water content (GWC) is a key variable in land-atmosphere interactions and plays an important role in agriculture, climate, and hydrology. Aiming at vegetation GWC estimating, this work proposed a dual-channel (co- and cross-polarization) method based on the water cloud model (WCM-based DCM) with an explicit inversion expression. The WCM-based DCM was used to derive corn GWC during the HiWATER2012 and Baoding2015 experiments using RADARSAT-2 and Sentinel-2 SAR imageries. Validation against ground-measured corn GWC shows that the correlation coefficient (R) between the model-estimated and ground-measured corn GWC is 0.514, with a root mean square error (RMSE) of 0.0901 kg/kg and a mean absolute error (MAE) of 0.075 kg/kg. The results indicate that the proposed WCM-based DCM can estimate corn GWC with acceptable accuracy and is a practical way to retrieve GWC over regional areas.

The transition of soil freeze/thaw (FT) state plays a critical role in ecosystem, hydrological, and biogeochemical processes. However, obtaining representative soil FT state datasets with a long time sequence and fine spatiotemporal resolution remains challenging. Here, a decision-level spatiotemporal data fusion algorithm based on Convolutional Long Short-Term Memory networks (ConvLSTM) for soil FT product is proposed to enhance the enhanced L3 freeze/thaw product of the Soil Moisture Active Passive Mission (SMAP_E_FT). In the algorithm, the feature of long-time sequence from the Freeze/Thaw Earth System Data Record product (ESDR_FT) and the land surface dataset produced from the fifth generation ECMWF atmospheric reanalysis (ERA5-land) are sucked in. The results of the validation based on eight dense in-situ networks in Genhe, Maqu, Naqu, Ngari, Pali, Saihanba, Shandian river basin (SDR), and Tianjun show that the correlation coefficient between the fusion product (ConvLSTM_FT) and SMAP_E_FT is 0.9498, and the deviation is 0.0140, which maintains the classification accuracy. This study provides a possible way of temporally extending the SMAP_E_FT.

Mountains account for approximately 24% of the global land area and are crucial in regulating regional and global climate. However, the terrain not only introduces observation errors in the microwave remote sensing data acquisition process but also affects the microwave scattered radiation on the surface and the redistribution of hydrothermal energy, thereby influencing the accuracy of surface parameter inversion. To address this issue, this article incorporates roughness and vegetation models into the mountain radiation model to simulate the changes in surface microwave radiation in mountainous areas. By considering terrain parameters, soil moisture, and temperature, we can use Rugosity (RU) to accurately estimate the topographic effects on the brightness temperature of mountainous area. A higher RU value indicates a more substantial impact of soil moisture and temperature on the brightness temperature difference.

Thermokarst lake is a periglacial landfom that is widely distributed in permafrost area and seasonally frozen ground. Remote senisng data has been used to mapping the existance of thermokarst lakes. In this study, we used Sentinel-2 data, DEM data and the shape criteria to identify thermokarst lakes on Qinghai-Tibetan Plateau(QTP). The results were compared with former study and shows that the fomer study over estimated the area and number of thermokarst lakes in the study area.

Here, four normalized difference water index (NDWI) variants, i.e., NDWI(860,970), NDWI(860,1240), NDWI(860,1640), and NDWI(1240,1640)are generated from the corn-oriented PROSAIL radiative transfer model. It is found that, instead of the linear relationship derived in previous studies, corn canopy water content (CWC) is best approximated as an exponential function of NDWI. Following the analysis of the PROSAIL-generated results, a newly optimized NDWI-based scheme is proposed for estimating corn CWC according to variations in the performance of the four NDWI variants under different CWC conditions. Validation results based on independent field data from the SMEX02, HiWATER2012, and Baoding2018 field experiments verify that this optimized NDWI-based corn CWC estimating scheme has a higher accuracy ($R = 0.87\,\,\pm \,\,0.03$, RMSE = 0.2068 ± 0.0145 kg/m2) than existing NDWI-based strategies for corn CWC retrieval. The feasibility of retrieving corn vegetation water content (VWC) based on the optimized NDWI-based scheme is also investigated, and the superiority of the optimized NDWI-based scheme for retrieving corn VWC is assessed. By comparing with four other NDWI-based corn VWC estimating methods, as well as the corn VWC parameterization scheme applied in the SMAP soil moisture algorithm, it is shown that our optimized NDWI-based scheme has the best VWC estimation accuracy, with the highest$R$of 0.89 ± 0.02 and the lowest RMSE of 0.7179 ± 0.0555 kg/m2.

High-quality and long-term soil moisture (SM) products are crucial for studying the climate change over the Qinghai-Tibet Plateau. Five passive microwave remotely sensed SM products were compared against in-situ SM observations from five networks, i.e., Heihe, Naqu, Pali, Maqu, and Ngari. Five SM products include the Soil Moisture Active Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), the Fengyun-3B (FY3B), the Land Parameter Retrieval Model (LPRM), and the Japan Aerospace Exploration Agency (JAXA). The results show that SMAP performs better than other four products in every network, but it has a slightly underestimate and poor dynamic range in Heihe network, which mainly due to the significant heterogeneity of topography and land cover. Moreover, the underestimate for JAXA, the overestimate for LPRM, the noisy temporal variations for SMOS, good dynamic changes for FY3B but lacks perfect absolute accuracy, all of these can be reflected from the validation results.

Vegetation water content (VWC) is a key variable in land-atmosphere interactions and plays an important role in agriculture, climate and hydrology. Based on the first-order scattering model, simulation database of corn backscattering coefficients at L-band was established. The simulations were used to train an artificial neural network (ANN) to establish an inverse model for corn VWC estimation during corn growth periods. The inverse accuracy of the trained ANN was evaluated using ground corn samplings and radar data acquired by the Passive and Active L- and S-band (PALS) airborne microwave sensor during the Soil Moisture Experiments in 2002 (SMEX02). Moreover, the corn VWC inversion results were compared to those obtained from an empirical method using the radar vegetation index (RVI). Result showed that the ANN method is superior to the RVI method and capable of estimating corn VWC with a correlation coefficient (R) of 0.7987, a root mean square error (RMSE) of 0.6033 kg/m2and a mean absolute relative error (MARE) of 12.00%.

The matrix doubling (MD) model is a numerical solution to the radiative transfer equation. It can achieve better accuracy in simulating microwave signals from vegetated terrain by considering multiple-scattering effects. However, it is difficult to apply the MD model to retrieving work due to its high complexity. This letter presents a case study performed on corn to demonstrate a multiangular (5°-65°), multiband (1.4/6.925/10.65 GHz) microwave emission model considering multiple-scattering effects by parameterizing the MD model. The simulated emissivity differences between the theoretical model and parameterized model are small. The mean absolute percent errors are all less than 1%, and the root mean square errors (RMSEs) are all within the range of 10-3. Validations using airborne polarimetric L-band microwave radiometer data and ground-based trunk-mounted multifrequency microwave radiometer data indicate that the parameterized model achieves good accuracy with overall RMSEs within 8K at all three bands.

In this study, based on in-situ measurements collected in the North China Plain, adjusted vegetation water indexes were introduced on the basis of traditional vegetation water indexes to weak soil and background influence and propose multiscale winter wheat moisture inversion model that is suitable in North China Plain. The main conclusions are as follows: 1) The adjusted water indexes have a good correlation with VWC; 2) For a fixed spatial resolution and indices with same form, the longer wavelength remote sensing data were used, the better reversion performance would be achieved; 3) The trend of spatial variability of multiscale wheat moisture is consistent, which indicates that the adjusted vegetation water indexes have certain applicability in the North China Plain.

According to the microwave radiation characteristics, this paper introduced a new frozen soil dielectric model to calculate the dielectric constant of frozen and thawed soil based on the Helsinki University of Technology (HUT) microwave snow emission model. The Advanced Integrated Emission Model (AIEM) was used to calculate surface emissivity. The multi-frequency microwave radiation model and soil freeze/thaw discriminant algorithm were improved. The classification accuracies of original and improved soil freeze/thaw discriminant algorithms were validated using AMSR2 Level 3 daily gridded 0.25° brightness temperature products and the measured values obtained by ground-based microwave radiometer. The results showed that compared to the original discriminant algorithm, the frozen soil classification accuracy of the improved discriminant algorithm was effectively improved and the overall classification accuracy reached 82%. It turned out to be a comparatively reliable mode of discrimination.

A new method was proposed in this paper to estimate optical depth (τ) and single scattering albedo (ω) of short vegetation over north China plain based on the refined physical expressions of Microwave Vegetation Indices derived from the parameterized first-order emission model. Comparisons with MODIS 16-day Normalized Difference Vegetation Index (NDVI) showed that, although there were some differences between the variations of single scattering albedo, optical depth and NDVI, the variation trends of the three parameters are very similar to each other. They all mainly expressed two regular variations during the whole year of 2010 that firstly increased and then decreased. The first one happened during February to late June, and the second one happened during early July to late September. They are closely consisted with the phenology of winter wheat and summer corn, which are the mainly two crop types in north China plain.

Soil moisture is a critical factor in cosmopolitan meteorological and hydrological processes. Microwave remote sensing brightness temperature is sensitive to soil moisture through the effects of moisture on the dielectric constant and hence emissivity of the soil [1]. It was found that the brightness temperature at L-band is useful for retrieving near-surface soil moisture [2-3]. The Soil Moisture and Ocean Salinity (SMOS) satellite, which carries an L-band passive microwave radiometer in the 1400-1427 MHz protected band, was successfully launched in November 2, 2009 and it has become a useful tool monitoring soil moisture [4-5]. However, it is very important to assess the performance of soil moisture product before using it and the validation is still a challenging task to validate these soil moisture retrievals due to the coarse spatial resolution of passive microwave remote sensing [6]. During the past few years, the SMOS soil moisture products have been evaluated over several areas of the world, e.g., Europe [7-8], North America [9] and Oceania [10]. But the evaluations were limited in the Tibet area in China [8,11]. More assessments need to be performed in other areas of China.

Soil moisture (SM) is a key variable in describing land surface characteristics. However, most passive microwave sensed soil moisture products are spatially and temporally discontinuous. In this study, a recurrent autoregressive neural network was investigated for its capability to reconstruct time-series soil moisture. The train dataset was collected from the observations of AMSR2 and SMOS, along with the daily NDVI, land surface temperature (LST), precipitation (PRC) and DEM information. Then, the trained neural network was used to predict time-series soil moisture at a spatial resolution of 0.25°. Result shows that this approach is promising in providing time-series soil moisture. Moreover, compared to ground soil moisture measurements, the predicted dataset tends to have lower root-mean-square error (rmse) and higher correlation coefficient (R) than the original soil moisture product of AMSR2 and SMOS.

In this study, we developed a new algorithm to retrieve the gravimetric vegetation water content (GVWC, %) of corn using L-band bi-angular dual-polarized passive microwave TB, corn LAI, height of the corn stalks and areal density of the corn stalks. Based on in-situ measured corn-related three-dimensional structure parameters during various growth stages, simulation databases of corn LAI and optical depth in L-band were established. A quantitative relationship between corn GVWC, corn optical depth in L-band, corn LAI, height and areal density of the corn stalks was constructed based on the simulation databases. The corn optical depth in this quantitative relationship was calculated by the retrieval method of low-vegetation optical depth proposed by Wang et al. We used Polarimetric L-band Microwave Radiometer (PLMR) airborne data obtained in the 2012 Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, and the leaf area index (LAI) product of LAINet wireless network to retrieve the GVWC of corn in study area. In-situ measured corn GVWC were used to validate the accuracy of the retrieved corn GVWC. The results show that the GVWC retrieval method proposed in this study is feasible for monitoring corn GVWC in real time. Moreover, the method is promising to apply to the satellite observations.

Vegetation plays an important role in the terrestrial ecosystems. Monitoring vegetation is of great importance in understanding the climate change. Passive microwave remote sensing behaves as an attractive technique due to its penetrability and comprehensive macro scale. It is of great significance to develop a good forward model with simple form and high accuracy for the inversion. The commonly used T-co model takes very simple form and is good for fast inversion. However, it is always limited at low frequency or for sparse vegetation. In this study, we present a parameterized model based on the Matrix Doubling (MD) model. The result shows that the parameterized model achieves rather accuracy with the theoretical MD model, and the RMSE is 0.0077 in V polarization and 0.018 in H polarization, and the correlation coefficient is above 95%.

Determination of the component soil moisture content within one pixel using passive microwave remote sensing data is important for predicting soil moisture contents in ecohydrological research within the Heihe River Basin. The Heihe Watershed Allied Telemetry Experimental Research was conducted in 2012 to address this issue. An airborne polarimetric L-band microwave radiometer (PLMR) instrument was used to measure surface emissions over the middle stream of the Heihe River Basin. Extensive ground-based soil moisture content and temperature data were obtained during the PLMR flights. In this letter, an algorithm for estimating the component soil moisture content was developed using biangular PLMR observations. Based on a theoretical analysis, the linear relationship between the soil emissivities at two different incidence angles was obtained. Therefore, the component soil moisture could be derived based on the tau-omega model. In addition, the component soil moisture contents determined over the bare surface were lower than those over the vegetated surface. The root-mean-square errors between the calculated soil moisture contents and the measured soil moisture contents over the bare and vegetated surfaces were 0.050 and 0.051 cm3/cm3, respectively. Overall, the results indicate that the component soil moisture contents can be estimated using biangular observations from airborne radiometer data.

The dielectric property of vegetation has a considerable effect on the characteristics of the microwave radiation of vegetation. In frozen environments, when the temperature is colder than normal, changes such as increased soluble sugar and decreased moisture content (MC) can occur in the vegetation. The dielectric property of vegetation, which is almost entirely controlled by its free and bound water content, will also change. To characterize the dielectric behavior of vegetation in frozen regions, a sensitive experiment was conducted on holly leaves with a high-performance coaxial probe over a frequency range from 0.5 to 40 GHz and a temperature range from 0°C to -20°C. Based on the measurements and the physical properties of the constituent substances of vegetation, a semiempirical dielectric model for holly leaves in low temperature environments was developed. In this model, a decrease in MC, which causes a reduction in the complex permittivity, was described as an increase in the ice content. The complex permittivity of bound water was measured using a saturated sucrose solution at -6.5°C. The research will provide a reference for the dielectric property study of the vegetation in frozen environments.

Soil moisture is an important parameter in many fields. Since the dielectric constant of soil is directly related with its moisture content, many soil dielectric constant models have been established and used in the application of soil moisture inversion. As an effective composition of soil, organic matter could increase the adsorption of soil particles and affect the dielectric constant. However, due to its little content, it was seldom considered in soil moisture inversion and brightness simulation. In this study, a semi-empirical organic dielectric model was used in the forward simulation of brightness temperature in Genhe River basin. The results show that it has a higher accuracy about 1.6k~2.4k than using TMD model at C-band and X-band.

In this study, winter wheat water content was retrieved using a parameterized relationship between the winter wheat water content and its structure parameters, i.e. radius, length and thickness of the leaves. The parameterized relationship was constructed from a large quantity of simulated attenuation cross section (Qe) of the winter wheat leaves based on the Generalized Rayleigh-Gans approximation (GRG) and the Mätzler vegetation dielectric constant model. The study was conducted at 6.925GHz and under the assumption that there was no significant polarization difference between the vegetation signals. This new method were applied to ground-based brightness temperature observations to retrieve winter wheat water contents, which were further compared with the corresponding filed measurements. Results show that the retrieved winter wheat water contents are consisted with the field measurements. It proves that the winter wheat inversion method proposed in this study is feasible. Moreover, the method is promising to apply to the satellite observations.

A useful indicator to evaluate the soil freeze-thaw intensity is the amount of phase transition water content (PTWC) in soil pores. In this research, a power function relation between soil phase transition water content (PTWC) and the variation rate of land surface temperature (VTS) was found by analyzing of ground measured soil moisture and temperature data obtained in the Tibet plateau during the winter of 2012. Then a downscaling approach combining MODIS VTsand AMSR2 products was employed to retrieve high resolution PTWC. The downscaled result was tested using in situ observations from the CTP-SMTMN network and found that it quite followed the trend of ground data with a RMSE of 0.0034 (m3/m3) and MAE of 0.0025 (m3/m3). The comparisons indicate that PTWC-VTSmodel has combined the advantage of microwave remote sensing and optical remote sensing; it has a high precision and can generate PTWC in small scale.

Freeze-thaw erosion is the third largest soil erosion type after water erosion and wind erosion, which is a serious threat to agricultural land and various buildings, especially for water projects. In this paper, a new method based on the passive microwave remote sensing technique was proposed to classify and assess the freeze-thaw erosion. The core of this new method is at two important indices: the freeze-thaw cycling days per year, and the phase transition water content per day. The first index can be used to determine the freeze-thaw erosion region and the second index can be used to evaluate the degree of freeze-thaw erosion. The application of this new method in China shows good result. It indicates that the freeze-thaw erosion regions in China are mainly distributed in Tibet, Mongolia and the province of Qinghai, Xinjiang, Gansu, Sichuan and Heilongjiang. Furthermore, the comparably serious freeze-thaw erosion region is located in Tibet Plateau.

Dielectric constant is an important parameter in microwave remote sensing. The microwave scattering/radiation signal of vegetation is closely related to its dielectric constant. Many related models have been established by now. However, most of them can only be used in room temperature. Therefore, it brings errors in the research of vegetation in frozen environment. In this study, a new dielectric model, which can be used at frequencies ranged from 3GHz to 40GHz under temperatures between -20°C and -4°C, has been established. It was developed based on Debye-Cole dual-dispersion model. The validation shows it has an acceptable precision.

In this study, the dielectric constants of 12 types of soil with different organic matter content were measured using the coaxial probe method by network analyzer (0.5-40 GHz) at room temperature (approx. 23°C). The observed dielectric constant increases only slowly with soil volumetric water content up to a transition point. Beyond the transition point, it increases rapidly with volumetric water content. It was found that the value of the transition point was higher and the observed dielectric constant was lower at the same soil volumetric water content and frequency for soil with higher organic matter content. A simple semi-empirical model was proposed to describe the dielectric behavior of soil with organic matter. This model was developed based on the refractive mixing dielectric model (RMDM).

A new dielectric model for vegetation in frozen environment based on the Debye-Cole dual-dispersion model was already developed in part I. This model can be used at a wide frequency range (0.5GHz - 40GHz) and even applicable for negative temperatures reached -20°C. In this paper, a matrix-doubling microwave emission model was used to evaluate vegetation effects in a frozen environment at 6.925, 10.65, 18.7 and 36.5GHz (V and H polarization). To verify the new developed dielectric model, a kind of young tree named Populus tomentosas was measured based on the Truck-mounted Multi-frequency Microwave Radiometer in December of 2012. In the experiment, the ground was irrigated to get rid of the soil signals. Also, the row-structure's effect on the trees can be eliminated when water covered the whole ground surface. Comparisons and analysis between model simulations and field measurements showed the dielectric model can be applied to microwave emission model as input data. Furthermore, the characteristics of microwave radiation of vegetation in frozen environment were evaluated and how the vegetation dielectric constant affected the electric field and physical property of vegetation layer was explained.

In this study, a parameterized first-order radiative transfer (RT) model for short vegetation layer is employed to retrieve the single scattering albedo of winter wheat by combining passive microwave AMSR-E data with optical MODIS data. The microwave vegetation indices (MVIs) with two adjacent frequencies of AMSR-E at H/V polarization derived from the parameterized model are used to cancel out the ground surface emission signals. Then a simulating database based on field measured parameters is established to figure out the relationship of the optical thickness, single scattering albedo at C and X band, respectively. Finally the characteristics of retrieved single scattering albedo are analyzed and the daily NDVI was utilized for evaluating the retrieved results.

Penetration depth of passive microwave remote sensing indicates the area below the land surface where singles come from. A reliable penetration depth is helpful not only in understanding the mechanism of microwave remote sensing but also in validating the retrieved results of land parameters. In this paper, we defined that the penetration depth in passive microwave remote sensing is the soil depth above which contributes more than 99% power of the total signal. Based on a three-layer non-coherent model (air-soil-aluminum), sensitive analysis was conducted to find out parameters which have more effect on penetration depth. Then three parameters including soil moisture, frequency and soil temperature were selected to build a simple statistic model to calculate the penetration depth of passive microwave remote sensing. To validate the statistic model, two controlled experiments were designed and carried out using a Truck-mounted Multi-frequency Microwave Radiometer (TMMR). It was shown that the penetration depth calculated from field experiment data coincided well with the statistic model.

The technique of microwave remote sensing has been used to monitor the soil frozen/thawed status for many years. The dielectric constant of frozen soil is relatively lower than that of unfrozen soil, so that microwave could penetrate deeper into frozen soil. However, we are still lack of the knowledge of the Microwave Penetration Depth (MPD) of frozen soil. In this study, a noncoherent microwave radiation was used to find the factors that influence the MPD of frozen soil and then validated by experiments. The results showed that the frequency of microwave, the temperature of frozen soil and the soil texture are the main factors that determine the MPD of frozen soil. An empirical model that estimates the MPD of frozen soil was proposed based on the simulation data.

One of the most important characteristics of crop is row-structure, whose periodic property can result in different scattering features comparing with other types of vegetation. In this paper, we'll take wheat for example to analyze these special features based on field experiments. Theoretical foundation was first introduced and a field experiment was carried out for comparison and analysis. In the experiment, wheat land was scanned at different angles in both parallel-to-row and perpendicular-to-row directions based on a Truck-mounted Multi-frequency Microwave Radiometer (TMMR). With its support, we have done relevant sensitivity analysis of some characteristic parameters to explore the regular pattern of emissivity at these two azimuths. It has shown that row-structure of wheat has obvious effects on passive microwave emissivity at different observation azimuths, especially parallel-to-row and perpendicular-to-row. Soil moisture can influence these characteristics distinctly when the wheat was shot and sparse.

This paper employs a method based on a parameterized first-order radiative transfer (RT) model at L-band to evaluate the aboveground biomass of forest area using Soil Moisture and Ocean Salinity (SMOS) data. A comprehensive database, including forest structure information based on L-system and the corresponding scattering properties, was established. Then the parameterized first-order RT retrieval model was used to acquire the forest parameters. The Look-Up Table method was used to find the proper biomass value. We retrieved the single scattering albedo and the optical thickness and then found the biomass value which approximated to the reference dataset.

Vegetation is the most important part of the terrestrial ecosystems which results in a large proportion of studies on vegetation parameters, such as coverage, biomass, water content and so on. Since the ultimate goal of remote sensing is to accurately and efficiently inverse land surface parameters, it is of great significance to find a good forward vegetation model with simple form and high accuracy for the inversion. Though the zeroth-order model is good for fast inversion with its simple form, it always underestimates the total emission at high frequency or for dense vegetation. The first-order model has higher accuracy due to the consideration of volume scattering contribution, but it is complex and computationally intensive. In this regard, we developed a parameterized model base on emissivity simulations from the first-order model for short vegetation covered ground in this paper. This parameterized model takes a similar form as that of the zeroth-order model. It is of great significance for accurate and efficient inversion.

Spectral gradient is one of the indicators of soil freeze and thaw. A negative spectral gradient was a sign of frozen soil. A truck-mounted microwave radiometer observed the frozen ground in two experiments. However, the measured spectral gradient was positive when the surface is frozen. From experiment measured and model simulated result, the relationship between the amount of unfrozen water of frozen soil and SG was found. It indicates that the amount of unfrozen water is a very important factor that determines the microwave radiation of frozen soil.

Vegetation biomass is an important parameter in the carbon cycle study. In this paper, a new technique to estimate aboveground vegetation wet biomass based on the Microwave Vegetation Indices (MVIs), which are computed through the observed brightness temperature of AMSR-E/Aqua under two adjacent frequencies, has been developed. The MVIs can provide significant new information compared with the conventional optical vegetation indices since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy where the microwave sensor can ¿see¿ through. We know that the absorption effect of vegetation canopy is mostly controlled by the total wet biomass. In this technique, we first retrieve the single scattering albedo and the optical thickness based on model simulations under AMSR-E configuration. Then, the estimated above two properties are used to derive the absorption fraction of vegetation. Finally, it can be related to the aboveground vegetation wet biomass.

As the global warming intensifies, the environment changes in cold land receive more attention. In this paper, a combined microwave emission model is established for cold land researches. Through field observation experiment, the b-factor of winter wheat during winter is obtained to simulate radiation accurately from this typical ground object in China. Furthermore, the impacts of snow and vegetation cover on frozen soil radiation are investigated by sensitivity analysis.

In this paper, an artificial neural network approach to estimate LAI from the combination of CYCLOPES and MODIS products over the 2001 to 2003 period is described in detail. Reflectances in RED, NIR and SWIR band and LAI with good quality were chosen according to the Quality Control information and the temporal consistency between the two LAI products. Four different reflectance and LAI combinations from both sensors were used as the input and output variables of the ANNs with different land cover types for training. The prediction abilities of the trained ANNs were validated using the datasets which were not used in the training process. It is observed that the ANNs can be well trained and have promising prediction abilities. The time series LAI derived from the trained ANNs is charactered by better temporal consistency compared with the original MODIS LAI product.