Z. Vekerdy

Maqu network for validation of satellite-derived soil moisture products

Abstract Soil moisture monitoring of the Tibetan Plateau is of primary importance for understanding land–atmosphere interactions of this region and their effects on the climate of eastern and South-East Asia. Operational satellite-derived soil moisture products, such as those obtained from AMSR-E data by VUA–NASA and ASCAT data by TU-Wien, as well as that to become available in the near future (such as data from SMOS and SMAP), can provide the information required, but their accuracy for this region needs to be evaluated before further application. For this reason, a soil moisture and temperature monitoring network was set up in the water source region of the Yellow River, in the north-eastern region of the Tibetan Plateau (Maqu county). It consists of 20 stations distributed, according to a stratified sampling, over an area of approximately 40xa0kmxa0×xa080xa0km. This study describes the Maqu network and presents the first set of data measured from July 2008 to December 2009, which shows the capability of the network to monitor the spatial and temporal soil moisture variability of the area with a high degree of accuracy. Temporal stability analyses revealed that the soil moisture spatial patterns are not always stable in time. The sites that show the highest and the most variable bias with respect to the average are located in regions with extreme soil properties, covering relatively small areas. The weighted spatial average of measured soil moisture was successfully used as ground reference for the validation of the AMSR-E soil moisture products and ASCAT soil wetness index products. For the monsoon season, overall good agreement was found between in situ time series and AMSR-E products, with a linear fit between the two datasets close to the 1:1 line and a standard error of the regression lower than 0.05. The agreement between ASCAT and in situ data was affected by several large variations of the former corresponding to little changes of the latter, thus the standard error of the regression was higher than 0.07.

Monitoring coal fires using multi-temporal night-time thermal images in a coalfield in north-west China

China has the largest coal resources in the world but these are seriously endangered by coal fires. Though the problem of coal fires is long standing and not only limited to China, little has been done for regular monitoring of these fires. This Letter proposes the use of multi-temporal night-time thermal images acquired from Landsat Thematic Mapper band 6 for establishing a coal fire monitoring system for a coalfield in north-west China. Other images and map data are fused with the thermal images to provide a comprehensive picture of the fires through the years. Finally the fires are classified into different categories based on multi-temporal changes.

Statistical and analytical study of the propagation of flood-induced groundwater rise in an alluvial aquifer

The propagation of stage rises of the river Danube in an adjoining alluvial aquifer (Kisalfold, NW Hungary) has been studied by preparing a map which shows the lag times corresponding to maximum correlation values between the hydrographs of groundwater observation wells and the river stages. As expected, the lag times for the unconfined aquifer generally exceed those for the semi-confined part. The complex pattern suggests that other rivers in the area also play a role and that transmissivities and resistances of a cover layer are not sufficiently well known, despite the presence of a relatively dense network of bore holes. A systematic increase of the lag times with the distance from the river was noted at two sections. Nine flood events have been selected to study how well observed head rises could be predicted using equations for the calculation of the propagation of the head for phreatic and for confined conditions. The results suggest that the water levels in the complex flood plain and the river bed conductance of silted up branches during the rise of the flood play an important role.

Assimilation of satellite observed snow albedo in a land surface model

This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado) that are part of the 2002/03 Cold Land Processes Field Experiment (CLPX). The assimilated snow albedo products are 1) the standard Moderate Resolution Imaging Spectrometer (MODIS) product (MOD10A1) and 2) retrievals from MODIS observations with the recently developed Pattern-Based Semiempirical (PASS) approach. The performance of the Noah simulations, with and without assimilation, is evaluated using the in situ measurements of snow albedo, upward shortwave radiation, and snow depth. The results show that simulations with albedo assimilation agree better with the measurements. However, because of the limited impact of snow albedo updates after subsequent snowfall, the mean (or seasonal) error statistics decrease significantly for only two of the three CLPX sites. Though the simulated snow depth and duration for the snow season benefit from the assimilation, the greatest improvements are found in the simulated upward shortwave radiation, with root mean squared errors reduced by about 30%. As such, this study demonstrates that assimilation of satellite-observed snow albedo can improve LSM simulations, which may positively affect the representation of hydrological and surface energy budget processes in runoff and numerical weather prediction models.

Crop Monitoring Based on SPOT-5 Take-5 and Sentinel-1A Data for the Estimation of Crop Water Requirements

Optical and microwave images have been combined for land cover monitoring in different agriculture scenarios, providing useful information on qualitative and quantitative land cover changes. This study aims to assess the complementarity and interoperability of optical (SPOT-5 Take-5) and synthetic aperture radar (SAR) (Sentinel-1A) data for crop parameter (basal crop coefficient (Kcb) values and the length of the crop’s development stages) retrieval and crop type classification, with a focus on crop water requirements, for an irrigation perimeter in Angola. SPOT-5 Take-5 images are used as a proxy of Sentinel-2 data to evaluate the potential of their enhanced temporal resolution for agricultural applications. In situ data are also used to complement the Earth Observation (EO) data. The Normalized Difference Vegetation Index (NDVI) and dual (VV + VH) polarization backscattering time series are used to compute the Kcb curve for four crop types (maize, soybean, bean and pasture) and to estimate the length of each phenological growth stage. The Kcb values are then used to compute the crop’s evapotranspiration and to subsequently estimate the crop irrigation requirements based on a soil water balance model. A significant R2 correlation between NDVI and backscatter time series was observed for all crops, demonstrating that optical data can be replaced by microwave data in the presence of cloud cover. However, it was not possible to properly identify each stage of the crop cycle due to the lack of EO data for the complete growing season.

Use of Radarsat-2 and Landsat TM images for spatial parameterization of Manning's Roughness Coefficient in hydraulic modeling

Vegetation resistance influences water flow in floodplains. Characterization of vegetation for hydraulic modeling includes the description of the spatial variability of vegetation type, height and density. In this research, we explored the use of dual polarized Radarsat-2 wide swath mode backscatter coefficients (σ°) and Landsat 5 TM to derive spatial hydraulic roughness. The spatial roughness parameterization included four steps: (i) land use classification from Landsat 5 TM; (ii) establishing a relationship between σ° statistics and vegetation parameters; (iii) relative surface roughness (Ks) determination from Synthetic Aperture Radar (SAR) backscatter temporal variability; (iv) derivation of the spatial distribution of the spatial hydraulic roughness both from Manning’s roughness coefficient look up table (LUT) and relative surface roughness. Hydraulic simulations were performed using the FLO-2D hydrodynamic model to evaluate model performance under three different hydraulic modeling simulations results with different Manning’s coefficient parameterizations, which includes SWL1, SWL2 and SWL3. SWL1 is simulated water levels with optimum floodplain roughness (np) with channel roughness nc = 0.03 m−1/3/s; SWL2 is simulated water levels with calibrated values for both floodplain roughness np = 0.65 m−1/3/s and channel roughness nc = 0.021 m−1/3/s; and SWL3 is simulated water levels with calibrated channel roughness nc and spatial Manning’s coefficients as derived with aid of relative surface roughness. The model performance was evaluated using Nash-Sutcliffe model efficiency coefficient (E) and coefficient of determination (R2), based on water levels measured at a gauging station in the wetland. The overall performance of scenario SWL1 was characterized with E = 0.75 and R2 = 0.95, which was improved in SWL2 to E = 0.95 and R2 = 0.99. When spatially distributed Manning values derived from SAR relative surface values were parameterized in the model, the model also performed well and yielding E = 0.97 and R2 = 0.98. Improved model performance using spatial roughness shows that spatial roughness parameterization can support flood modeling and provide better flood wave simulation over the inundated riparian areas equally as calibrated models.

Assessing Groundwater Storage Changes Using Remote Sensing–Based Evapotranspiration and Precipitation at a Large Semiarid Basin Scale

A method is presented that uses remote sensing (RS)-based evapotranspiration (ET) and precipitation estimates with improved accuracies under semiarid conditions to quantify a spatially distributed water balance, for analyzing groundwater storage changes due to supplementary water uses. The method is tested for the semiarid Konya basin (Turkey), one of the largest endorheic basins in the world. Based on the spatially distributed water balance estimation, the mean irrigation for croplands was 308 mm yr−1, which corresponds to a total reduction of 2270 million cubic meters per year (106 m3 yr−1, or MCM yr−1) in the groundwater storage during the study period 2005–09. The storage change estimated as the residual of the spatially distributed water balance was confirmed by the volume change calculated from groundwater table observations. To obtain an improved precipitation distribution, the monthly Tropical Rainfall Measuring Mission (TRMM) rainfall product was assessed. After a bias removal, TRMM data were combined with the snow water equivalent estimated by a multivariate analysis using snow gauge observations, the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product, and the digital elevation model. With respect to the distribution of ET, the standard SEBS and the soil moisture integrated SEBS-SM models were compared; SEBS-SM proved to better reflect the water-limited evapotranspiration regime of semiarid regions. The RS-based distributed water balance calculation as presented in this study can be applied in other large basins, especially in semiarid and arid regions. It is capable of estimating spatially distributed water balances and storage changes, which otherwise, by ground-based point measurements, would not be feasible

Report of Working Group II

This report summarizes the discussions of the Working Group on a Spatial Data Laboratory Network that were held as part of the NATO Advanced Study Institute on “Deposit and Geoenvironmental Models for Resource Exploitation and Environmental Security.” The Working Group was made up of experts who represented 14 countries and had expertise in such fields as the geosciences, geoinformatics and natural resources. The discussions focused on the scope of spatial data acquisition, handling and analysis tools in the assessment and monitoring of the environmental impact of mining. After analyzing the needs of our target group, the working group suggested the set-up of a home page for facilitating the information exchange. A brief outline of the home page was also presented.

Seasonality and autocorrelation of satellite-derived soil moisture products

This study presents an analysis of temporal behaviour of in situ and satellite-derived soil moisture data. The main objective is to evaluate the temporal reliability of the satellite products, comparing them with in situ data, for applications that would benefit from the use of consistent time series of soil moisture, such as studies on climate and hydrological cycle. The time series, seasonalities, and anomalies of Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) soil moisture and European Remote Sensing (ERS) satellite soil wetness index data sets were analysed over five test sites. The agreement of temporal behaviours and autocorrelation functions and the correlation with in situ data were investigated. A good agreement between the seasonalities of both satellite data sets and in situ data with high correlations (i.e. 0.9) was found over the sites with a large soil moisture variability range and short vegetation cover. Noisier seasonalities were found over sites with small soil moisture variability ranges, affected by radiofrequency interference (RFI) and characterized by croplands. In spite of ERS soil moisture being characterized by a longer time series, the seasonality is much noisier than the AMSR-E products due to the numerous gaps in the data set. The correlation among the anomalies is lower than 0.6, mainly due to the noise in the satellite products. However, the autocorrelation functions show that the anomalies are not random, although noisy. Although the stability of the anomaly correlograms is affected by the relatively short time series available for this study, the analysis shows that there are statistical similarities between the satellite soil moisture anomalies and the in situ data anomalies. The results show that AMSR-E and ERS products are consistent over long time periods and do contain useful information about soil moisture seasonality and anomaly behaviour, although they are affected by noise.

Improving modeled snow albedo estimates during the spring melt season

Snow albedo influences snow-covered land energy and water budgets and is thus an important variable for energy and water fluxes calculations. Here, we quantify the performance of the three existing snow albedo parameterizations under alpine, tundra, and prairie snow conditions when implemented in the Noah land surface model (LSM)—Noahs default and ones from the Biosphere-Atmosphere Transfer Scheme (BATS) and the Canadian Land Surface Scheme (CLASS) LSMs. The Noah LSM is forced with and its output is evaluated using in situ measurements from seven sites in U.S. and France. Comparison of the snow albedo simulations with the in situ measurements reveals that the three parameterizations overestimate snow albedo during springtime. An alternative snow albedo parameterization is introduced that adopts the shape of the variogram for the optically thick snowpacks and decreases the albedo further for optically thin conditions by mixing the snow with the land surface (background) albedo as a function of snow depth. In comparison with the in situ measurements, the new parameterization improves albedo simulation of the alpine and tundra snowpacks and positively impacts the simulation of snow depth, snowmelt rate, and upward shortwave radiation. An improved model performance with the variogram-shaped parameterization can, however, not be unambiguously detected for prairie snowpacks, which may be attributed to uncertainties associated with the simulation of snow density. An assessment of the model performance for the Upper Colorado River Basin highlights that with the variogram-shaped parameterization Noah simulates more evapotranspiration and larger runoff peaks in Spring, whereas the Summer runoff is lower.