Microwave Polarization Difference Index Research Articles

Retrieving forest soil moisture from SMAP observations considering a microwave polarization difference index (MPDI) to τ-ω model

Estimating soil moisture from microwave brightness temperature is extremely challenging in densely vegetated areas. The soil moisture retrieved from the Soil Moisture Active Passive (SMAP) measurements tends to be consistently overestimated, sometimes exceeding the saturation level of mineral soils. Therefore, the retrieved soil moisture cannot detect or monitor climate extremes, such as floods and droughts for forests, natural resource management, and climate change research. We hypothesize that the main issue is that the scattering albedo (ω) and the optical depth (τ) are parameterized solely with NDVI (Normalized Difference Vegetation Index), neglecting the polarization characteristics from vegetation structure. This study proposes a weighting factor between scattering and optical thickness, a function of MPDI (Microwave Polarization Difference Index), and applies it to both parameters simultaneously to increase the scattering effect and decrease the attenuation effect in high MPDI. The validation results based on the Climate Reference Network revealed that considering MPDI is critical in reducing soil moisture overestimation errors and obtaining more accurate soil moisture over forested regions. This results in correlation improving from 0.36 to 0.44, a decrease in ubRMSE from 0.179 to 0.125 cm³cm-³, and bias lowering from 0.127 to 0.060 cm³cm-³ in comparison with the SMAP measurements over forested regions.

Surface Properties of Global Land Surface Microwave Emissivity Derived from FY-3D/MWRI Measurements

Land surface microwave emissivity is crucial to the accurate retrieval of surface and atmospheric parameters and the assimilation of microwave data into numerical models over land. The microwave radiation imager (MWRI) sensors aboard on Chinese FengYun-3 (FY-3) series satellites provide valuable measurements for the derivation of global microwave physical parameters. In this study, an approximated microwave radiation transfer equation was used to estimate land surface emissivity from MWRI by using brightness temperature observations along with corresponding land and atmospheric properties obtained from ERA-Interim reanalysis data. Surface microwave emissivity at the 10.65, 18.7, 23.8, 36.5, and 89 GHz vertical and horizontal polarizations was derived. Then, the global spatial distribution and spectrum characteristics of emissivity over different land cover types were investigated. The seasonal variations of emissivity for different surface properties were presented. Furthermore, the error source was also discussed in our emissivity derivation. The results showed that the estimated emissivity was able to capture the major large-scale features and contains a wealth of information regarding soil moisture and vegetation density. The emissivity increased with the increase in frequency. The smaller surface roughness and increased scattering effect may result in low emissivity. Desert regions showed high emissivity microwave polarization difference index (MPDI) values, which suggested the high contrast between vertical and horizontal microwave signals in this region. The emissivity of the deciduous needleleaf forest in summer was almost the greatest among different land cover types. There was a sharp decrease in the emissivity at 89 GHz in the winter, possibly due to the influence of deciduous leaves and snowfall. The land surface temperature, the radio-frequency interference, and the high-frequency channel under cloudy conditions may be the main error sources in this retrieval. This work showed the potential capabilities of providing continuous and comprehensive global surface microwave emissivity from FY-3 series satellites for a better understanding of its spatiotemporal variability and underlying processes.

A new model for an improved AMSR2 satellite soil moisture retrieval over agricultural areas

This study evaluates the potential of AMSR2 (Advance Microwave Scanning Radiometer2) data for the estimation of Volumetric Soil Moisture (VSM) for bare and agricultural areas. At the first step, the sensitivity of the Microwave Polarization Difference Index (MPDI) to variations in soil and vegetation characteristics were examined at different frequencies. At lower frequencies, the signal attenuation due to vegetation is minimal and thus, denser vegetation usually depolarizes the soil emission. Interestingly, the results also reveal that at higher frequencies, the sensitivity of V and H polarizations over relatively dense vegetation covers is not the same at all. Therefore, MPDI at both low and high frequencies can be a good indicator of the soil moisture and Vegetation Water Content (VWC), respectively. After evaluation of AMSR2 datasets, a model called Multi-channel/MPDI-based Land Parameters Retrieval Model (MMLPRM) is proposed. The MMLPRM optimizes optical depth of vegetation and soil dielectric constant, with simultaneous retrieval of soil moisture and surface temperature by using the AMSR2 brightness temperature data. This algorithm also includes the surface roughness parameters to increase the soil moisture retrieval efficiency. In this way, calibration and validation have been done, using in situ observations of 50 monitoring stations obtained from the International Soil Moisture Network (ISMN) over the United States. Consequently, the analysis on the MMLPRM retrieval model demonstrates its potential and usefulness for soil moisture retrieval. The outcome of this study will help in estimating the accurate soil moisture to optimize the irrigation management strategies and help in water conservation.

A synergistic use of AMSR2 and MODIS images to detect saline soils (Study Area: Iran)

Soil salinity is a critical environmental problem especially in arid and semiarid regions. Then, the objective of this study is to detect saline soils by synergistic use of the Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) images. In this regard, the Total Precipitable Water (TPW) Vapor parameter obtained from AMSR2 and MODIS, the Microwave Polarization Difference Index (MPDI), and a vertical to horizontal brightness temperature ratio (TB v /TB h ) in the 6 GHz channel of AMSR2 were used in two procedures. In procedure 1, the thresholding on the TPW and MPDI, and in procedure 2, the thresholding on the TPW and the TB v /TB h in the 6 GHz channel were investigated. The overall accuracy and Kappa coefficient of the produced saline soil map by the procedure 1 were acquired as 0.865 and 0.715, and for the procedure 2 were 0.809 and 0.607, respectively.

Simulation of Passive Microwave Data Toward Efficient Assimilation Over Indian Subcontinent

Brightness temperature (Tb) is sensitive to soil moisture (SM) estimates and has the advantage of increasing the spatial coverage of SM measurements. This letter focuses on the simulation of Tb from the land surface variables generated by Noah with multiparameterized (Noah-MP) and a forward observation operator, community microwave emission model (CMEM) over the Indian subcontinent with a spatial resolution of 0.25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> ×0.25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> . Traditionally, soil dielectric constant and vegetation optical depth are the most important parameters that affect the sensitivity of the top of the atmosphere Tb. Hence, the results of the simulated Tb are presented for a total of 12 configurations and the one with better result (C12) is compared against the microwave polarization difference index (MPDI)-based Tb and observed SM and ocean salinity (SMOS) Tb. The simulated SM from the land surface model (LSM) is also compared with the observed SMOS SM to examine the sensitivity of the simulated Tb with SM. The results reveal that MPDI approach has immense potential in simulating the observed Tb and can be used toward the development of an efficient Tb assimilation system.

Estimation of Daily Actual Evapotranspiration Using Vegetation Coefficient Method for Clear and Cloudy Sky Conditions

Actual evapotranspiration (AET) can be studied and estimated using remote-sensing-based methods at multiple spatial and temporal scales. Reflectance and Land surface temperature are essential in these methods. However optical and thermal sensors fail to provide these data under overcast conditions and this creates gap in the AET product. Besides, there is a necessity of the AET method that requires less data and estimates AET with better accuracy. In this regard, AET was estimated for all-sky conditions using the vegetation coefficient (VI-Kv) method utilizing microwave, thermal, and optical data. Essential reference evapotranspiration (ET0) under cloudy conditions was estimated using LST-based Penman-Monteith temperature (PMT) and Hargreaves-Samani equations. Furthermore, LST predicted using the microwave polarization difference index (PLST) and LST of moderate resolution imaging spectroradiometer (MODIS) cloud product (MLST) were evaluated with in-situ air temperature (Ta) under cloudy sky conditions. Results revealed that the PLST correlated better with Ta than MLST with correlation coefficient (r) values of 0.71 and 0.81 for day and night times, respectively. Hence, PLST-based solar radiation (Rs) estimation yielded better accuracy with observed Rs with r and root mean square error values of 0.864 and 0.07 for Berambadi station under cloudy conditions, respectively. PMT-based ET0 values corresponded well with the observed ET0 under cloudy sky condition during this study. In addition, AET estimated using the VI-Kv method was compared with the simple two-source energy balance (TSEB) method under clear sky conditions. It was found that the improved VI-Kv method performed better than the TSEB method and could also fairly estimate AET even under cloudy sky conditions.

Improving the AMSR-E/NASA Soil Moisture Data Product Using In-Situ Measurements from the Tibetan Plateau

The daily AMSR-E/NASA (the Advanced Microwave Scanning Radiometer-Earth Observing System/the National Aeronautics and Space Administration) and JAXA (the Japan Aerospace Exploration Agency) soil moisture (SM) products from 2002 to 2011 at 25 km resolution were developed and distributed by the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) and JAXA archives, respectively. This study analyzed and evaluated the temporal changes and accuracy of the AMSR-E/NASA SM product and compared it with the AMSR-E/JAXA SM product. The accuracy of both AMSR-E/NASA and JAXA SM was low, with RMSE (root mean square error) &gt; 0.1 cm3 cm−3 against the in-situ SM measurements, especially the AMSR-E/NASA SM. Compared with the AMSR-E/JAXA SM, the dynamic range of AMSR-E/NASA SM is very narrow in many regions and does not reflect the intra- and inter-annual variability of soil moisture. We evaluated both data products by building a linear relationship between the SM and the Microwave Polarization Difference Index (MPDI) to simplify the AMSR-E/NASA SM retrieval algorithm on the basis of the observed relationship between samples extracted from the MPDI and SM data. We obtained the coefficients of this linear relationship (i.e., A0 and A1) using in-situ measurements of SM and brightness temperature (TB) data simulated with the same radiative transfer model applied to develop the AMSR-E/NASA SM algorithm. Finally, the linear relationships between the SM and MPDI were used to retrieve the SM monthly from AMSR-E TB data, and the estimated SM was validated using the in-situ SM measurements in the Naqu area on the Tibetan Plateau of China. We obtained a steeper slope, i.e., A1 = 8, with the in-situ SM measurements against A1 = 1, when using the NASA SM retrievals. The low A1 value is a measure of the low sensitivity of the NASA SM retrievals to MPDI and its narrow dynamic range. These results were confirmed by analyzing a data set collected in Poland. In the case of the Tibetan Plateau, the higher value A1 = 8 gave more accurate monthly AMSR-E SM retrievals with RMSE = 0.065 cm3 cm−3. The dynamic range of the improved retrievals was more consistent with the in-situ SM measurements than with both the AMSR-E/NASA and JAXA SM products in the Naqu area of the Tibetan Plateau in 2011.

Estimation of daily vegetation coefficients using MODIS data for clear and cloudy sky conditions

ABSTRACTSpatially distributed vegetation coefficients () data with high temporal resolution are in demand for actual evapotranspiration estimation, crop condition assessment, irrigation scheduling, etc. Traditional remotely sensed based data application gets hindered because of two main reasons i.e 1) spectral reflectance based accounts only for transpiration factor, but fails to account for total evapotranspitration. 2) required optical spectral reflectances are available only during clear sky conditions, which creates gaps in the data. Hence there is a necessity of a model which accounts for both transpiration and evpaoration factors and also for a gap filling method, which can produce accurate continuous quantification of values. Therefore, in this study, different combinations of enhanced vegetation index (EVI), global vegetation moisture index (GVMI) and temperature vegetation dryness index (TVDI) have been employed in linear and non linear regression techniques to obtain the best model. To fill the gaps in the data, initially, temporal fitting of values have been examined using Savitsky-Goley (SG) filter for 3 years of data (2012–2014), but this fails when sufficient high quality values are unavailable. In this regard, three gap filling techniques namely regression, artificial neural networks (ANN) and interpolation techniques have been employed over Cauvery basin. Microwave polarization difference index (MPDI) has been employed in ANN technique to estimate values under cloudy sky conditions. The results revealed that the combination of GVMI and TVDI using linear regression technique performed better than other combinations with correlation coefficient (r) and root mean square error (RMSE) values of 0.824 and 0.204 respectively. Furthermore, the results indicated that SG filter can be used for temporal fitting and for gap filling regression technique performed better than other techniques with the r and RMSE values of 0.68 and 0.25 for Berambadi station.

Soil Moisture Retrieval From SMAP: A Validation and Error Analysis Study Using Ground-Based Observations Over the Little Washita Watershed

The newest soil moisture-dedicated satellite, the Soil Moisture Active Passive (SMAP) mission, provides global maps of soil moisture using concurrent L-band radar and radiometer acquisitions. To support the ongoing validation activities of SMAP soil moisture products, in this paper, we examined the retrieval accuracy of four SMAP soil moisture products by using well-calibrated and dense in situ measurements from the Little Washita Watershed network, one of the SMAP core validation sites with intensive ground sampling. The four SMAP products include the active (3 km), passive (36 km), active-passive (9 km), and the enhanced passive product which is a newly released soil moisture data set with a grid resolution of 9 km. Efforts on identifying the possible error sources of these products were also made for the purpose of improving the SMAP soil moisture algorithms. The results show that the passive and active-passive products can well capture the temporal dynamic of ground soil moisture with overall unbiased root-mean-square error (ubRMSE) values of 0.032 and $0.041~\text {m}^{3}\cdot ~\text {m}^{-3}$ , respectively, which generally meet their mission requirement of $0.04~\text {m}^{3}\cdot ~\text {m}^{-3}$ . In contrast, some irregular fluctuations exist in the active product, leading to an overall wet bias, which makes its accuracy a little poorer than its expected retrieval accuracy of $0.06~\text {m}^{3}\cdot ~\text {m}^{-3}$ . The new enhanced passive product shows the lowest ubRMSE value of $0.026 ~\text {m}^{3}\cdot ~\text {m}^{-3}$ though it underestimates in situ measurements with a bias of $0.059 ~\text {m}^{3}\cdot ~\text {m}^{-3}$ , revealing its great potential to substitute the active-passive product to provide global soil moisture measurements at a medium resolution of 9 km. The underestimation of SMAP surface temperature data may be one of the reasons that contribute to the dry bias of SMAP passive, active-passive, and enhanced passive products. The microwave polarization difference index and HV-polarized backscatter show good response to in situ soil moisture and may be considered in SMAP algorithms to further improve the accuracy of soil moisture retrievals. We expect that our findings can be fed back to improve the SMAP soil moisture algorithms and thus promote the application of SMAP soil moisture products in terrestrial water, energy, and carbon cycles.

Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region

Live fuel moisture content (LFMC) is an important factor in fire risk management in the Mediterranean region. Drawing upon a large network of stations (the Réseau Hydrique) measuring LFMC for operational fire danger assessment in the south-eastern region of France, this study assesses the ability of several long-term passive microwave remote sensing indices to capture the LFMC temporal dynamic of various Mediterranean shrub species. Microwave remote sensing has a high potential for monitoring LFMC independently of several constraints (e.g., atmospheric and cloud contamination effects) associated with optical-infrared and thermal remote sensing observations. The following four microwave-derived indices are considered: (1) the Essential Climate Variable near-surface soil moisture (ECV_SM); (2) the root-zone soil moisture (ECV_RZSM) derived from ECV_SM; (3) the microwave polarization difference index (MPDI) computed from five microwave frequencies (C, X, Ku, K and Ka-band corresponding to 6.9, 10.7, 18.7, 23.8 and 36.5GHz respectively); and (4) the vegetation optical depth (VOD) at C- and X-band (from the Advanced Microwave Scanning Radiometer for the Earth observing system, AMSR-E). Firstly, an evaluation of the root-zone soil moisture ECV_RZSM against a network of soil moisture measurements (SMOSMANIA in southern France) gave satisfactory results. For most of the Réseau Hydrique sites, the present study found good agreement between LFMC and individual microwave indices, including root-zone soil moisture, VOD at X-band, and MPDI at X and Ku-bands, all averaged over the 15days preceding the in-situ LFMC measurements. VOD at X-band showed the best agreement with the in situ LFMC data (median of correlation coefficients over all in situ sites=0.43). Further comparisons between LFMC data and several optical indices computed from the Moderate Resolution Imaging Spectrometer (MODIS) data including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), visible atmospheric resistant index (VARI), normalized difference water index (NDWI), normalized difference infrared index 6 (NDII6), normalized difference infrared index 7 (NDII7) and global vegetation moisture index (GVMI) were made. The comparisons showed that VARI and SAVI, as optical greenness indices, outperform the microwave indices and other optical indices with median of correlation coefficients of 0.66 and 0.65, respectively. Overall, this study shows that passive microwave indices, particularly VOD, are efficient proxies for LFMC of Mediterranean shrub species and could be used along with optical indices to evaluate fire risks in the Mediterranean region.

Land surface temperature retrieval from AMSR-E passive microwave data.

The land surface temperature (LST) is a key parameter for energy balance, evapotranspiration and climate change. In this study, two new methods of LST retrieval from passive microwave data are developed: one is deriving LST only using single-channel dual-polarized data based on the relationship between the emissivity and microwave polarization difference index (MPDI) (denoted as Method 1); the other one is deriving LST using the traditional multi-channel method with prior knowledge of the normalized difference vegetation index (NDVI) (denoted as Method 3). Taking Moderate Resolution Imaging Spectroradiometer (MODIS) LST products as the actual LSTs, the coefficients for these algorithms are determined. From the results for the year 2008, it is demonstrated that the root mean square errors (RMSEs) for the LST retrieval using Method 3 are the smallest and range from 2.92 K to 3.44 K, the RMSEs for the LST retrieval using traditional multi-channel method (denoted as Method 2) range from 3.07 K to 4.05 K, and the worst results come from Method 1, whose RMSEs range from 3.11 K to 4.13 K at a frequency of 89 GHz. This could be caused by the fact that the NDVI provides substantial emissivity knowledge in Method 3, and much richer vegetation could result in a more accurate emissivity estimation.

The Microwave Temperature Vegetation Drought Index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010)

Satellite-based drought indices have been proved to be effective and convenient in detecting drought conditions at regional and global scales. However, most current drought indices are based on the visible/near infrared/thermal remote sensing, which might be influenced greatly by cloud, atmospheric water content and rain-fall. Microwave sensors can overcome the shortages of visible/near infrared/thermal remote sensing and show to be another important approach for drought monitoring due to its all-weather working advantages. But to date, the application of microwave vegetation drought indices in drought monitoring has not been thoroughly investigated. Here, for the first time we constructed a microwave derived Temperature Vegetation Drought Index (TVDI) - MTVDI based on the theory of optical TVDI using the brightness temperatures (Tb) from the Advanced Microwave Scanning Radiometer (AMSR‐E) onboard Aqua satellite. Firstly, we built a new land surface temperature (Ts) inversion model based on the AMSR‐E 18.7GHz horizontal, 23.8GHz and 89.0GHz vertical polarized Tb, and then developed the Microwave Normalized Difference Vegetation Index (MNDVI) from the AMSR‐E 23.8GHz Microwave Polarization Difference Index (MPDI). After that, we constructed three versions of MTVDI: original MTVDI using Ts and MNDVI; Imp‐MTVDI (Improved MTVDI) using the Ts‐Tair (the difference between land surface temperature and air temperature) to replace the Ts; and NonL‐MTVDI (Nonlinear MTVDI) using nonlinear equation to fit the dry and wet edges, respectively. Finally, we used precipitation, soil moisture (SM) and P/PET (the ratio of precipitation to potential evapotranspiration) to validate the performances of MTVDI, Imp‐MTVDI, NonL‐MTVDI, MODIS derived TVDI and iTVDI (improved TVDI). The time-series drought assessments across China from 2003 to 2010 indicated that the trends of the proposed MTVDI showed the most negative correlations with the variations of precipitation, P/PET and SM, and showed best performances of significance test in most regions of China. Moreover, the MTVDI could better separate the drought levels in different degrees than MODIS-derived TVDI. However, the proposed MTVDI still has some uncertainties in regions widely covered by desert, Gobi and large water surfaces. In addition, this paper mainly focuses on large spatial scale and long term drought monitoring and only uses satellite data for model validation. Further studies are needed to develop a higher spatial- and temporal-resolution MTVDI for short-term and small spatial-scale drought monitoring.

Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN

Land Surface Temperature (LST) with high spatio-temporal resolution is in demand for hydrology, climate change, ecology, urban climate and environmental studies, etc. Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the most commonly used sensors owing to its high spatial and temporal availability over the globe, but is incapable of providing LST data under cloudy conditions, resulting in gaps in the data. In contrast, microwave measurements have a capability to penetrate under clouds. The current study proposes a methodology by exploring this property to predict high spatio-temporal resolution LST under cloudy conditions during daytime and nighttime without employing in-situ LST measurements. To achieve this, Artificial Neural Networks (ANNs) based models are employed for different land cover classes, utilizing Microwave Polarization Difference Index (MPDI) at finer resolution with ancillary data. MPDI was derived using resampled (from 0.25° to 1km) brightness temperatures (Tb) at 36.5GHz channel of dual polarization from Advance Microwave Scanning Radiometer (AMSR)-Earth Observing System and AMSR2 sensors. The proposed methodology is tested over Cauvery basin in India and the performance of the model is quantitatively evaluated through performance measures such as correlation coefficient (r), Nash Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE). Results revealed that during daytime, AMSR-E(AMSR2) derived LST under clear sky conditions corresponds well with MODIS LST resulting in values of r ranging from 0.76(0.78) to 0.90(0.96), RMSE from 1.76(1.86)K to 4.34(4.00)K and NSE from 0.58(0.61) to 0.81(0.90) for different land cover classes. During nighttime, r values ranged from 0.76(0.56) to 0.87(0.90), RMSE from 1.71(1.70)K to 2.43(2.12)K and NSE from 0.43(0.28) to 0.80(0.81) for different land cover classes. RMSE values found between predicted LST and MODIS LST during daytime under clear sky conditions were within acceptable limits. Under cloudy conditions, results of microwave derived LST were evaluated with air temperature (Ta) and indicate that the approach performed well with RMSE values lesser than the results obtained under clear sky conditions for land cover classes for both day and nighttimes.

Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 1: Satellite data analysis

Abstract A study is performed to analyze the daily retrieval of soil moisture from the synergy of active and passive microwave data using observations from the ASCAT scatterometer and AMSR-E radiometer. The objective is to identify the information provided by each sensor and to analyze preprocessing methods – such as the day/night average, diurnal difference and microwave polarization difference index for AMSR-E and the incidence angle normalization and backscatter temporal index for ASCAT – to maximize the amount of soil moisture information extracted. Additionally, the data fusion and a posteriori synergy methodologies are compared to determine how to optimally exploit this combined information. This study is performed using a neural network (NN) to estimate soil moisture from a set of AMSR-E and ASCAT observations. ERA-interim/Land surface soil moisture fields are used to train the NN as well as to evaluate the performance of the different retrieval input datasets. It is shown that using the AMSR-E 7 GHz, 11 GHz, 19 GHz and 37 GHz channels with the three preprocessing methods highlights various surface contributions in the signal, in particular the soil moisture and surface temperature information, and greatly helps the retrieval in disentangling them. For ASCAT, the synergy effect is less significant and data preprocessed with the two methods analyzed yields very similar information. The information provided by the active and passive microwave sensors is found to be very complementary, such that a soil moisture retrieval using the combined active and passive information shows a significant improvement between 5% and 19% in the temporal correlation and a reduction of the retrieval uncertainty by 7%. The improvement in the spatial structure is smaller with a correlation increase of 2%. It is demonstrated that the choice of synergy method strongly impacts the retrieval improvement that can be achieved. Data fusion methods are shown to be better suited than a posteriori combination methods, due to their ability to exploit information complementarity. These results could help improve future active/passive soil moisture retrievals, such as from the Soil Moisture Active/Passive (SMAP) mission, through the application of similar preprocessing and synergy methods in order to better extract the soil moisture information provided.

Aquarius L-band scatterometer and radiometer observations over a Tibetan Plateau site

In this paper, the impact of freeze–thaw, soil moisture and vegetation on L-band backscatter and emission is studied using Aquarius scatterometer/radiometer measurements collected from August 2011 to May 2013 over the northeastern part of the Tibetan Plateau. The study area is the Maqu region that holds a regional-scale monitoring network consisting of twenty soil moisture/temperature stations, which is selected as one of the core international Calibration/Validation (Cal/Val) sites for NASA’s Soil Moisture Active Passive (SMAP) mission. Comparisons of Aquarius scatterometer/radiometer measurements with soil moisture recorded by capacitance probes installed at a 5-cm soil depth illustrate that (i) L-band microwave observations are also sensitive to the amount of liquid water in soil below freezing point, and (ii) the sensitivity of Aquarius observations over the Maqu area dissipates above soil moisture contents of 0.3m3m−3. Further effects of vegetation become directly noticeable only within passive microwave observations at moisture levels larger than 0.4m3m−3.The impact of vegetation is studied in more detail through analysis of the Radar Vegetation Index (RVI). Although seasonal variability is captured, the dynamic range of the RVI is insufficient for a meaningful signal-to-noise. Further vegetation optical depth (τ) is estimated using the τ–ω concept by reconstructing the Microwave Polarization Difference Index (MPDI) derived from Aquarius radiometer data. Peaks in the τ estimates are noted in the months January/February and July/August. Evidence suggests that the magnitude of τ is a measure for the frost depth when temperatures are below freezing point whereas the behavior of τ in the warm season is in line with the vegetation dynamics.

Evaluation of temporal variations in soil moisture based on the microwave polarization difference index using in situ data over agricultural areas in China

Soil moisture plays a critical role in the energy exchange and water redistribution of the land-atmosphere system. Knowledge of the temporal variations in soil moisture is vital in agricultural applications. Microwave indices are often used to characterize the temporal variations in soil moisture. In this study, we evaluate the temporal variations in soil moisture based on the microwave polarization difference index (MPDI) using ground-based measurements in China. In situ soil moisture at six test sites during the crop-growing season from 2009 to 2011 is obtained. The consistency of the temporal variations between the MPDI values and the in situ soil moisture is analysed in terms of (1) microwave frequencies, (2) satellite overpass times, and (3) measurement depths of soil moisture. The results show that the accuracies of the consistency vary from approximately 40% to 90%. Compared with the in situ soil moisture at 0–10 cm, the temporal variations in soil moisture are best characterized by the 6.9 GHz MPDI values from the ascending overpasses (MPDI_06A). Furthermore, the accuracies of the consistency between MPDI_06A and the in situ soil moisture at 0–10 cm are greater than those between MPDI_06A and the in situ soil moisture at 10–20 cm.

Retrieving China’s surface soil moisture and land surface temperature using AMSR-E brightness temperatures

Based on the passive microwave radiative transfer equation, we developed a single-band model for retrieving surface soil moisture () and land surface temperature () using Aqua satellite advanced microwave scanning radiometer (AMSR-E) C-band brightness temperatures () without any ancillary data. National-scale and of China in 2009 were jointly mapped. The average and retrieval errors are 0.037 g cm−3 and 2.00K when validated by in situ measurements and approximately 0.025 g cm−3 and 2.34K when compared with AMSR-E L3 and moderate-resolution imaging spectroradiometer daily products. In particular, most and estimation errors are under 0.37 g cm−3 and 2.00K, respectively, for regions with the microwave polarization difference index (MPDI) between 0.01 and 0.05, but increased clearly larger for those with MPDI smaller than 0.01 or larger than 0.05, which are mostly dense-vegetated regions in Southwestern China or plateaus and dry deserts such as Inner Mongolia plateau, Qinghai-Tibet Plateau, Taklimakan Desert and Turpan basin. In summary, the proposed retrieval algorithm might provide a new way to monitor and conditions for penetrable vegetation canopy regions, but further investigations are still needed, especially for specific plateau, desert and frozen regions.

A simplified physically-based algorithm for surface soil moisture retrieval using AMSR-E data

A simplified physically-based algorithm for surface soil moisture inversion from satellite microwave radiometer data is presented. The algorithm is based on a radiative transfer model, and the assumption that the optical depth of the vegetation is polarization independent. The algorithm combines the effects of vegetation and roughness into a single parameter. Then the microwave polarization difference index (MPDI) is used to eliminate the effects of surface temperature, and to obtain soil moisture, through a nonlinear iterative procedure. To verify the present algorithm, the 6.9 GHz dual-polarized brightness temperature data from the Advanced Microwave Scanning Radiometer (AMSR-E) were used. Then the soil moisture values retrieved by the present algorithm were validated by in-situ data from 20 sites in the Tibetan Plateau, and compared with both the NASA AMSR-E soil moisture products, and Soil Moisture and Ocean Salinity (SMOS) soil moisture products. The results show that the soil moisture retrieved by the present algorithm agrees better with ground measurements than the two satellite products. The advantage of the algorithm is that it doesn’t require field observations of soil moisture, surface roughness, or canopy biophysical data as calibration parameters, and needs only single-frequency brightness temperature observations during the whole retrieval process.

Retrieving High-Resolution Surface Soil Moisture by Downscaling AMSR-E Brightness Temperature Using MODIS LST and NDVI Data

A method to retrieve soil moisture at high spatial resolution is presented in this paper. The method is based on soil moisture retrieval with passive brightness temperature. The method of retrieving land surface temperature with passive microwave is combined with the relationship between the microwave polarization difference index (MPDI) and normalized difference of vegetation index (NDVI) to obtain high-resolution microwave brightness temperature and soil moisture. Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) 18.7-GHz brightness temperature at 25-km resolution is downscaled to 1-km using high-resolution MODIS visible/infrared (VIS/IR) data. High-resolution soil moisture is retrieved with the downscaled microwave brightness temperature using a single-channel algorithm (SCA) and the Qp model to deal with the influence of roughness. The method is applied to an area in northwest of China. The downscaled high-resolution soil moisture is tested with ground data collected at three sites within the Maqu monitoring network from July 1, 2008 to June 30, 2009. The trend of the time series of the downscaled soil moisture is similar to the ground measurements during this period with root mean-square error (RMSE) less than 0.12. The results show that the method is more suitable to moderate to drier soil conditions with bare surface or covered by sparse vegetation.

Soil moisture inversion from aircraft passive microwave observations during SMEX04 using a single-frequency algorithm

Soil moisture plays a key role in global water cycles. In the study, soil moisture retrievals from airborne microwave radiometer observations using a single-frequency algorithm were presented. The algorithm is based on a simplified radiative transfer (tau-omega) model and the influence of both the roughness and vegetation is combined into a single parameter in the algorithm. The microwave polarization difference index (MPDI) is used to eliminate the effects of temperature. Then soil moisture is obtained through a nonlinear iterative procedure by making the absolute value of the differences between the simulated and observed MPDI minimum. The algorithm was validated with aircraft passive microwave data from the Polarimetric Scanning Radiometer (PSR) at the Arizona during the Soil Moisture Experiment 2004 (SMEX04). The results show that the soil moisture retrieved by the algorithm is in good agreement with ground measurements with a small bias and an overall accuracy of 0.037m3m−3.