Land Surface Conditions Research Articles

Towards ecohydrological drought monitoring and prediction using a land data assimilation system: A case study on the Horn of Africa drought (2010–2011)

AbstractDespite the importance of the ecological and agricultural aspects of severe droughts, no drought monitoring and prediction framework based on a land data assimilation system (LDAS) has been developed to monitor and predict vegetation dynamics in the middle of droughts. In this study, we applied a LDAS that can simulate surface soil moisture, root‐zone soil moisture, and vegetation dynamics to the Horn of Africa drought in 2010–2011 caused by the precipitation deficit in two consecutive rainy seasons. We successfully simulated the ecohydrological drought quantified by the model‐estimated soil moistures and leaf area index (LAI). The root‐zone soil moisture and LAI are good indicators of prolonged droughts because they reflect the long‐term effects of past precipitation deficit. The precipitation deficit in 2010 significantly affected the land surface condition of the next rainy season in 2011, which indicated the importance of obtaining accurate initial soil moisture and LAI values for prediction of multiseasonal droughts. In addition, the general circulation model‐based seasonal meteorological prediction showed good performance in predicting land surface conditions of the Horn of Africa drought.

Factors Controlling Stratocumulus Cloud Lifetime over Coastal Land

Abstract The breakup of stratocumulus clouds over coastal land areas is studied using a combination of large-eddy simulations (LESs) and mixed-layer models (MLMs) with a focus on mechanisms regulating the timing of the breakup. In contrast with stratocumulus over ocean, strong sensible heat flux over land prevents the cloud layer from decoupling during day. As the cloud thins during day, turbulence generated by surface flux becomes larger than turbulence generated by longwave cooling across the cloud layer. To capture this shift in turbulence generation in the MLM, an existing entrainment parameterization is extended. The MLM is able to mimic cloud evolution for a variety of Bowen ratios, but only after this modification of the entrainment parameterization. Cloud lifetime depends on a combination of the cloud-top entrainment flux, the Bowen ratio of the surface, and the strength of advection of cool ocean air by the sea breeze. For dry land surface conditions, the authors’ MLM suggests a breakup time a few hours after sunrise. For relatively wet land surface conditions, the cloud layer briefly breaks into partly cloudy conditions during midday, and the stratocumulus cloud reforms in the evening.

Separating the impacts of climate change and land surface alteration on runoff reduction in the Jing River catchment of China

The Loess Plateau of China is subject to severe water shortages, and the runoff reduction observed in most watersheds exacerbates the problem. Quantifying the contributions of environmental changes to runoff reduction is thus very important for water resources management. Taking the Jing River catchment as the study area, the changes in runoff for the period of 1961–2010 at three gauge stations—the catchment outlet (Zhang Jia Shan, ZJS) and the two others from the upper reach (Yang Jia Ping, YJP, and Yu Luo Ping, YLP) were analyzed in this study. Using the Budyko framework, the spatiotemporal variations of the contributions of precipitation (P), potential evapotranspiration (ET0), and land surface conditions (represented by the parameter n in the Choudhury–Yang equation) to runoff changes were evaluated. A significant downward trend in runoff was detected for the ZJS and YJP stations. The sensitivity of the runoff changes to the different environmental factors considered was different. The sensitivity coefficient was the greatest for P, intermediate for ET0, and smallest for the land surface condition (n). However, the sensitivity coefficients are becoming greater over time. The decrease in P was the dominant factor in the runoff reduction at the three stations, but its effect was largely offset by the increase in n at YLP. The contribution of land surface alteration to runoff reduction has been increasing in recent years, which indicates that the improvement of vegetation coverage and the construction of terraces and check dams have strengthened their impacts on runoff. Therefore, careful attention should be paid to the hydrological effects of soil conservation measures on runoff.

Land surface and atmospheric conditions associated with heat waves over the Chickasaw Nation in the South Central United States

AbstractExposure to extreme heat was reconstructed based on regional land‐atmosphere processes from 1979 to 2010 in the South Central U.S. The study region surrounds the Chickasaw Nation (CN), a predominantly Native American population with a highly prevalent burden of climate‐sensitive chronic diseases. Land surface and atmospheric conditions for summer heat waves were analyzed during spring (March‐April‐May, MAM) and summer (June‐July‐August, JJA) based on the Climate and Ocean: Variability, Predictability, and Change maximum temperature definition for heat wave frequency (HWF). The spatial‐temporal pattern of HWF was determined using empirical orthogonal function (EOF) analysis and the corresponding principle component time series of the first EOF of HWF. Statistically significant analyses of observed conditions indicated that sensible heat increased and latent heat fluxes decreased with high HWF in the South Central U.S. The largest positive correlations of sensible heat flux to HWF and the largest negative correlations of latent heat flux to HWF were specifically observed over the CN. This is a significantly different energy transfer regime due to less available soil moisture during the antecedent MAM and JJA. The higher sensible heat from dry soil could cause significant warming from the near surface (>2.0°C) to the lower troposphere (>1.5°C), and accumulated boundary layer heat could induce the significant patterns of higher geopotential height and enhance anticyclonic circulations (negative vorticity anomaly) at the midtroposphere. Results suggested a positive land‐atmosphere feedback associated with heat waves and called attention to the need for region‐specific climate adaptation planning.

Statistical Analysis of the Effect of Mineralogical Composition on the Qualities of Compressed Stabilized Earth Blocks

AbstractCompressed stabilized earth blocks (CSEBs) are typically produced with laterites because of their abundance in the tropics. However, the formation of laterites is governed by the nature of the parent rock, the age of the land surface, climate, and topography and drainage conditions resulting in laterites with varying mineral compositions. Because the geotechnical composition of tropical residual soils appears to be largely controlled by their mineral composition, the first level of any classification system should take into account the factors giving rise to their development. This paper summarizes the results of an analysis of the effect of mineralogical composition on the performance characteristics of CSEBs. The results from the X-ray diffraction analysis show that the soil samples consist of varying proportions of quartz and kaolinite alongside other minerals. The results obtained from this investigation show that soils with higher quartz and kaolinite content produced blocks with better perfo...

VALIDATION OF THE ASTER GLOBAL DIGITAL ELEVATION MODEL VERSION 3 OVER THE CONTERMINOUS UNITED STATES

The ASTER Global Digital Elevation Model Version 3 (GDEM v3) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009 and GDEM Version 2 (v2) in 2011. The absolute vertical accuracy of GDEM v3 was calculated by comparison with more than 23,000 independent reference geodetic ground control points from the U.S. National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v3 is 8.52 meters. This compares with the RMSE of 8.68 meters for GDEM v2. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v3 mean error of −1.20 meters reflects an overall negative bias in GDEM v3. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover type to provide insight into how GDEM v3 performs in various land surface conditions. While the RMSE varies little across cover types (6.92 to 9.25 meters), the mean error (bias) does appear to be affected by land cover type, ranging from −2.99 to +4.16 meters across 14 land cover classes. These results indicate that in areas where built or natural aboveground features are present, GDEM v3 is measuring elevations above the ground level, a condition noted in assessments of previous GDEM versions (v1 and v2) and an expected condition given the type of stereo-optical image data collected by ASTER. GDEM v3 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v3 has elevations that are higher in the canopy than SRTM. The overall validation effort also included an evaluation of the GDEM v3 water mask. In general, the number of distinct water polygons in GDEM v3 is much lower than the number in a reference land cover dataset, but the total areas compare much more closely.

VALIDATION OF THE ASTER GLOBAL DIGITAL ELEVATION MODEL VERSION 3 OVER THE CONTERMINOUS UNITED STATES

Abstract. The ASTER Global Digital Elevation Model Version 3 (GDEM v3) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009 and GDEM Version 2 (v2) in 2011. The absolute vertical accuracy of GDEM v3 was calculated by comparison with more than 23,000 independent reference geodetic ground control points from the U.S. National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v3 is 8.52 meters. This compares with the RMSE of 8.68 meters for GDEM v2. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v3 mean error of −1.20 meters reflects an overall negative bias in GDEM v3. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover type to provide insight into how GDEM v3 performs in various land surface conditions. While the RMSE varies little across cover types (6.92 to 9.25 meters), the mean error (bias) does appear to be affected by land cover type, ranging from −2.99 to +4.16 meters across 14 land cover classes. These results indicate that in areas where built or natural aboveground features are present, GDEM v3 is measuring elevations above the ground level, a condition noted in assessments of previous GDEM versions (v1 and v2) and an expected condition given the type of stereo-optical image data collected by ASTER. GDEM v3 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v3 has elevations that are higher in the canopy than SRTM. The overall validation effort also included an evaluation of the GDEM v3 water mask. In general, the number of distinct water polygons in GDEM v3 is much lower than the number in a reference land cover dataset, but the total areas compare much more closely.

Detecting significant decreasing trends of land surface soil moisture in eastern China during the past three decades (1979–2010)

AbstractUnderstanding the historical trends and driving mechanism of China's soil moisture change is an important step in combating climate change. Using the time series satellite‐derived Essential Climate Variable Soil Moisture (ECV_SM) product, we detected a significant decrease trend in land surface soil moisture in eastern China over a 32 year period (1979–2010). Theoretical sensitivity analysis suggested that soil moisture is regulated collectively by precipitation (P), potential evapotranspiration (PET), land surface conditions such as land cover/use changes, landscape features, irrigation and urban expansion, (m), and the water balance between input and output water supplies O (the input water supplies minus the output). The change in spatial pattern and temporal trend of P/PET is highly consistent with the corresponding change in soil moisture. The magnitude of soil moisture variation is also well correlated with that of P/PET (R2 = 0.43; p < 0.001). Therefore, P/PET is believed to be the dominant factor in determining the temporal trends of soil moisture change. Among the 29 drainage basins with significant decreasing trend of soil moisture change, the areas of forest cover increased by 36.08% and the average topographic slope was twice steeper than that of other regions. Therefore, besides the climate factor (P/PET variable), land surface conditions (such as land cover changes and topographic) also played important roles in regulating the trend of regional soil moisture change.

Lung Cancer Mortality and Topography: A Xuanwei Case Study.

The epidemic of lung cancer in Xuanwei City, China, remains serious despite the reduction of the risk of indoor air pollution through citywide stove improvement. The main objective of this study was to characterize the influences of topography on the spatiotemporal variations of lung cancer mortality in Xuanwei during 1990–2013. Using the spatially empirical Bayes method, the smoothed mortality rate of lung cancer was obtained according to the mortality data and population data collected from the retrospective survey (1990–2005) and online registration data (2011–2013). Spatial variations of the village-level mortality rate and topographic factors, including the relief degree of land surface (RDLS) and dwelling conditions (VDC), were characterized through spatial autocorrelation and hotspot analysis. The relationship between topographic factors and the epidemic of lung cancer was explored using correlation analysis and geographically weighted regression (GWR). There is a pocket-like area (PLA) in Xuanwei, covering the clustered villages with lower RDLS and higher VDC. Although the villages with higher mortality rate (>80 per 105) geographically expanded from the center to the northeast of Xuanwei during 1990–2013, the village-level mortality rate was spatially clustered, which yielded a persistent hotspot area in the upward part of the PLA. In particular, the epidemic of lung cancer was closely correlated with both RDLS and VDC at the village scale, and its spatial heterogeneity could be greatly explained by the village-level VDC in the GWR model. Spatiotemporally featured lung cancer mortality in Xuanwei was potentially influenced by topographic conditions at the village scale.

Hybrid macro–micro visual analysis for city-scale state estimation

We address the task of estimating large-scale land surface conditions using overhead aerial (macro-level) images and street view (micro-level) images. These two types of images are captured from orthogonal viewpoints and have different resolutions, thus conveying very different types of information that can be used in a complementary way. Moreover, their integration is necessary to enable an accurate understanding of changes in natural phenomena over massive city-scale landscapes. The key technical challenge is devising a method to integrate these two disparate types of image data in an effective manner, to leverage the wide coverage capabilities of macro-level images and detailed resolution of micro-level images. The strategy proposed in this work uses macro-level imaging to learn the extent to which the land condition corresponds between land regions that share similar visual characteristics (e.g., mountains, streets, buildings, rivers), whereas micro-level images are used to acquire high resolution statistics of land conditions (e.g., the amount of debris on the ground). By combining macro- and micro-level information about regional correspondences and surface conditions, our proposed method is capable of generating detailed estimates of land surface conditions over an entire city.

Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau

AbstractSoil moisture is a key state variable in many hydrological processes. The Global Land Data Assimilation System (GLDAS) can produce global and continuous soil moisture data sets which have been used in many applications. In this study, simulated soil moisture from four land surface models (LSM) (Mosaic, Noah, Community Land Model, and Variable Infiltration Capacity) in GLDAS‐1 and the more recent GLDAS‐2 were evaluated against in situ soil moisture measurements collected from two soil moisture networks located on the Tibetan Plateau at different soil depths. The two networks provide a representation of different climates and land surface conditions on the Tibetan Plateau which can make the evaluation results more robust and reliable. The results show that all the LSMs can well capture the temporal variation of observed soil moisture with the correlation coefficients mostly being above 0.5. However, they all display biases with the surface soil moisture being systematically underestimated in both of two network regions, and the Mosaic model always shows the largest bias that even reaches 0.192 m3/m3. The causes of the biases were investigated in detail, and we found that the biases may mainly be caused by the soil stratification phenomenon over the Tibetan Plateau. Moreover, errors in model parameters, especially the soil properties data, deficiencies in model structures, and mismatch of the spatial scale and soil depth between LSM simulations and in situ measurements may contribute to the biases as well. Additionally, it was found that GLDAS‐2 nearly does not show superior performance than GLDAS‐1 over the Tibetan Plateau.

Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data.

Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (< 100m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high- burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will thus play an important role in characterizing the carbon cycle and ecosystem processes of high latitude systems.

Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data

Abstract Wide field view (WFV) sensor on board the Chinese GF-1, the first satellite of the China High-resolution Earth Observation System, is acquiring multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for environment monitoring. The objective of this study is to develop a general and reliable fractional vegetation cover (FVC) estimation algorithm for GF-1 WFV data under various land surface conditions. The algorithm is expected to estimate FVC from GF-1 WFV reflectance data with spatial resolution of 16 m and temporal resolution of four dates. The proposed algorithm is based on training back propagation neural networks (NNs) using PROSPECT + SAIL radiative transfer model simulations for GF-1 WFV canopy reflectance and corresponding FVC values. Green, red and near-infrared bands' reflectances of GF-1 WFV data are the input variables of the NNs, as well as the corresponding FVC is the output variable, and finally 842,400 simulated samples covering various land surface conditions are used for training the NNs. A case study in Weichang County of China, having abundant land cover types, was conducted to validate the performance of the proposed FVC estimation algorithm for GF-1 WFV data. The validation results showed that the proposed algorithm worked effectively and generated reasonable FVC estimates with R 2 = 0.790 and root mean square error of 0.073 based on the field survey data. The proposed algorithm can be operated without prior knowledge on the land cover and has the potential for routine production of high quality FVC products using GF-1 WFV surface reflectance data.

Application of remote sensing-based two-source energy balance model for mapping field surface fluxes with composite and component surface temperatures

Operational application of a remote sensing-based two source energy balance model (TSEB) to estimate evaportranspiration (ET) and the components evaporation (E), transpiration (T) at a range of space and time scales is very useful for managing water resources in arid and semiarid watersheds. The TSEB model uses composite land surface temperature as input and applies a simplified Priestley–Taylor formulation to partition this temperature into soil and vegetation component temperatures and then computes subsequent component energy fluxes. The remote sensing-based TSEB model using component temperatures of the soil and canopy has not been adequately evaluated due to a dearth of reliable observations. In this study, soil and vegetation component temperatures partitioned from visible and near infrared and thermal remote sensing data supplied by advanced scanning thermal emission and reflection radiometer (ASTER) are applied as model inputs (TSEBCT) to assess and refine the subsequent component energy fluxes estimation in TSEB scheme under heterogeneous land surface conditions in an advective environment. The model outputs including sensible heat flux (H), latent heat flux (LE), component LE from soil and canopy from the TSEBCT and original model (TSEBPT) are compared with ground measurements from eddy covariance (EC) and larger aperture scintillometers (LAS) technique, and stable isotopic method. Both model versions yield errors of about 10% with LE observations. However, the TSEBCT model output of H and LE are in closer agreement with the observations and is found to be generally more robust in component flux estimation compared to the TSEBPT using the ASTER data in this heterogeneous advective environment. Thus given accurate soil and canopy temperatures, TSEBCT may provide more reliable estimates of plant water use and values of water use efficiency at large scales for water resource management in arid and semiarid landscapes.

Numerical Simulation of Land Surface Hydrological Process Over Huaihe River Basin

The BATS (Biosphere Atmosphere Transfer Scheme) model was used to simulate the thunderstorm and flood events in the mountains and plains of the Huaihe River Basin in 1998. And the inherent relationship among runoff, soil moisture content, soil texture and vegetation distribution were exposed via the simulation of water exchange between land and atmosphere. Results show that the effect of soil moisture content in root layer, soil texture, and soil color on the runoff in the mountains is similar to that in the plains, whereas with different sensitivities. However, the effect of soil moisture content in deep layer and vegetation canopy ratio on the runoff in the mountains is opposite to that in the plains. The meteorological and land surface conditions are different between the mountains and the plains, so their hydrological characteristics are different, showing the different atmospheric-hydrological relationship.

Improving Noah land surface model performance using near real time surface albedo and green vegetation fraction

The current operational Noah land surface model (LSM) uses multi-year climatology of monthly green vegetation fraction (GVF) and the multi-year averages of land surface albedo data for several numerical weather predictions at National Centers for Environmental Predictions of National Oceanic and Atmospheric Administration. However, these static GVF and albedo data can only prescribe the multiannual means and lack the ability to capture near real time (NRT) vegetation status and land surface condition. In this study, the impact of NRT GVF and albedo on Noah LSM (version 3.2) performances are examined against in situ measurements of surface net long wave radiation and net short wave radiation from 7 U.S. Surface Radiation Budget Network stations, and soil temperature and soil moisture from 9 USDA Soil Climate Analysis Network sites. Large differences between the NRT GVF/surface albedo and their climatological averages are found over the global, which have significant influences on Noah LSM simulations. With respect to in situ measurements, the Noah LSM simulation improvements from using the weekly GVF data are 19.3% for surface soil moisture, 9.3% for surface soil temperature. The benefits from the weekly GVF and monthly albedo can reach to 2.7Wm−2 for surface net long wave radiation and 2.6Wm−2 for surface net short wave radiation. The results suggest to Noah model developers and users that the NRT GVF and albedo should be used for better model performance.

Supplementary data of "Impacts of mesic and xeric urban vegetation on outdoor thermal comfort and microclimate in Phoenix, AZ".

An advanced Markov-Chain Monte Carlo approach called Subset Simulation is described in Au and Beck (2001) [1] was used to quantify parameter uncertainty and model sensitivity of the urban land-atmospheric framework, viz. the coupled urban canopy model-single column model (UCM-SCM). The results show that the atmospheric dynamics are sensitive to land surface conditions. The most sensitive parameters are dimensional parameters, i.e. roof width, aspect ratio, roughness length of heat and momentum, since these parameters control the magnitude of sensible heat flux. The relative insensitive parameters are hydrological parameters since the lawns or green roofs in urban areas are regularly irrigated so that the water availability for evaporation is never constrained.

The Impact of Local Acquisition Time on the Accuracy of Microwave Surface Soil Moisture Retrievals over the Contiguous United States

Satellite-derived soil moisture products have become an important data source for the study of land surface processes and related applications. For satellites with sun-synchronous orbits, these products are typically derived separately for ascending and descending overpasses with different local acquisition times. Moreover, diurnal variations in land surface conditions, and the extent to which they are accurately characterized in retrieval algorithms, lead to distinct systematic and random error characteristics in ascending versus descending soil moisture products. Here, we apply two independent evaluation techniques (triple collocation and direct comparison against sparse ground-based observations) to quantify (correlation-based) accuracy differences in satellite-derived surface soil moisture acquired at different local acquisition times. The orbits from different satellites are separated into two overpass categories: AM (12:00 a.m. to 11:59 a.m. Local Solar Time) and PM (12:00 p.m. to 11:59 p.m. Local Solar Time). Results demonstrate how patterns in the accuracy of AM versus PM retrieval products obtained from a variety of active and passive microwave satellite sensors vary according to land cover and across satellite products with different local acquisition times.

Assessment of land cover change and desertification using remote sensing technology in a local region of Mongolia

Desertification is a serious ecological, environmental, and socio-economic threat to the world, and there is a pressing need to develop a reasonable and reproducible method to assess it at different scales. In this paper, the Hogno Khaan protected area in Mongolia was selected as the study area, and a quantitative method for assessing land cover change and desertification assessment was developed using Landsat TM/ETM+ data on a local scale. In this method, NDVI (Normalized Difference Vegetation Index), TGSI (Topsoil Grain Size Index), and land surface albedo were selected as indicators for representing land surface conditions from vegetation biomass, landscape pattern, and micrometeorology. A Decision Tree (DT) approach was used to assess the land cover change and desertification of the Hogno Khaan protected area in 1990, 2002, and 2011. Our analysis showed no correlation between NDVI and albedo or TGSI but high correlation between TGSI and albedo. Strong correlations (0.77–0.92) between TGSI and albedo were found in the non-desertification areas. The TGSI was less strongly correlated with albedo in the low and non desertification areas, at 0.70 and 0.92; respectively. The desertification of the study area is increasing each year; in the desertification map for 1990–2002, there is a decrease in areas of zero and low desertification, and an increase in areas of high and severe desertification. From 2002 to 2011, areas of non desertification increased significantly, with areas of severe desertification also exhibiting increase, while areas of medium and high desertification demonstrated little change.

Standing water effect on soil moisture retrieval from L-band passive microwave observations

Abstract Passive microwave remote sensing at L-band has been widely acknowledged as the most promising technique to observe the spatial distribution of near surface (top ~ 5 cm) soil moisture at regional to global scales. The launch of the ESA's Soil Moisture and Ocean Salinity (SMOS) mission in 2009 now means that global space-borne brightness temperature observations are available at L-band (1.41 GHz) to estimate soil moisture every 2 to 3 days with a target accuracy of 0.04 m 3 /m 3 . Moreover, NASA's Soil Moisture Active Passive (SMAP) satellite has been launched on 31st January 2015, also carrying an L-band radiometer, together with an L-band radar for downscaling the brightness temperature observations to better than 10 km resolution. At the SMOS/SMAP radiometer scale of ~ 40 km, the presence of water bodies potentially induces an overestimation of retrieved soil moisture, if not carefully accounted for in retrieval models. Such water fraction effects on brightness temperature and soil moisture retrieval accuracy were investigated in this study, using airborne L-band brightness temperature data collected during three Australian field experiments. The water induced brightness temperature effect and water fraction were compared under different resolutions, sampling days, and land surface conditions, showing that the water fraction impact on retrieved soil moisture is independent of scale, but heavily dependent on the soil water content status. Subsequently, the highest water fraction threshold that can be tolerated in order to achieve the 0.04 m 3 /m 3 target accuracy without correction has been determined as 0.08 (actual range is from 0.02 for dry bare soil to 0.08 for wet vegetated soil). Using a MODIS derived water fraction dataset, the water fraction dynamics were also studied over Australia during the ten years from 2001 to 2010. The results show that if the mean water fraction map was used as a static water map to flag or correct water effects, the water body induced soil moisture retrieval error would have exceeded the 0.04 m 3 /m 3 target more than once for 13.5% of the Australian land 40 km sized radiometer pixels; only 0.6% Australian land pixels would have exceeded this target with a frequency of 10 times or more per year.