scholarly journals Estimating the Long-Term Hydrological Budget over Heterogeneous Surfaces

2006 ◽  
Vol 7 (1) ◽  
pp. 203-214 ◽  
Author(s):  
J. Song ◽  
M. L. Wesely ◽  
D. J. Holdridge ◽  
D. R. Cook ◽  
J. Klazura
Keyword(s):  
Remote Sensing ◽  
Climate Model ◽  
Root Zone ◽  
Regional Scale ◽  
Grid Cell ◽  
Remote Sensing Data ◽  
Subgrid Scale ◽  
Surface Conditions ◽  
Sensing Data ◽  
Meteorological Observations

Abstract Estimates of the hydrological budget in the Walnut River Watershed (WRW; ∼5000 km2) of southern Kansas were made with a parameterized subgrid-scale surface (PASS) model for the period 1996–2002. With its subgrid-scale distribution scheme, the PASS model couples surface meteorological observations with satellite remote sensing data to update root-zone available moisture and to simulate surface evapotranspiration rates at high resolution over extended areas. The PASS model is observationally driven, making use of extensive parameterizations of surface properties and processes. Heterogeneities in surface conditions are spatially resolved to an extent determined primarily by the satellite data pixel size. The purpose of modeling the spatial and interannual variability of water budget components at the regional scale is to evaluate the PASS model's ability to bridge a large grid cell of a climate model with its subgrid-scale variation. Modeled results indicate that annual total evapotranspiration at the WRW is about 66%–88% of annual precipitation—reasonable values for southeastern Kansas—and that it varies spatially and temporally. Seasonal distribution of precipitation plays an important role in evapotranspiration estimates. Comparison of modeled runoff with stream gauge measurements demonstrated close agreement and verified the accuracy of modeled evapotranspiration at the regional scale. In situ measurements of energy fluxes compare favorably with the modeled values for corresponding grid cells, and measured surface soil moisture corresponds with modeled root-zone available moisture in terms of temporal variability despite very heterogeneous surface conditions. With its ability to couple remote sensing data with surface meteorology data and its computational efficiency, PASS is easily used for modeling surface hydrological components over an extended region and in real time. Thus, it can fill a gap in evaluations of climate model output using limited field observations.

scholarly journals A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

2020 ◽  
Vol 12 (3) ◽  
pp. 455 ◽  
Author(s):  
Yaokui Cui ◽  
Xi Chen ◽  
Wentao Xiong ◽  
Lian He ◽  
Feng Lv ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
The Tibetan Plateau ◽  
Sensing Data ◽  
Spatio Temporal ◽  
Input Variables

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

scholarly journals INTEGRATION OF REMOTE SENSING DATA AND BASIC GEODATA AT DIFFERENT SCALE LEVELS FOR IMPROVED LAND USE ANALYSES

2015 ◽  
Vol XL-3/W3 ◽  
pp. 85-89 ◽  
Author(s):  
G. Waldhoff ◽  
S. Eichfuss ◽  
G. Bareth
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Resolution ◽  
Geographical Information Systems ◽  
High Spatial Resolution ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Sensing Data ◽  
Very High Spatial Resolution

The classification of remote sensing data is a standard method to retrieve up-to-date land use data at various scales. However, through the incorporation of additional data using geographical information systems (GIS) land use analyses can be enriched significantly. In this regard, the Multi-Data Approach (MDA) for the integration of remote sensing classifications and official basic geodata for a regional scale as well as the achievable results are summarised. On this methodological basis, we investigate the enhancement of land use analyses at a very high spatial resolution by combining WorldView-2 remote sensing data and official cadastral data for Germany (the Automated Real Estate Map, ALK). Our first results show that manifold thematic information and the improved geometric delineation of land use classes can be gained even at a high spatial resolution.

scholarly journals Behind the scenes of streamflow model performance

2021 ◽  
Vol 25 (2) ◽  
pp. 1069-1095
Author(s):  
Laurène J. E. Bouaziz ◽  
Fabrizio Fenicia ◽  
Guillaume Thirel ◽  
Tanja de Boer-Euser ◽  
Joost Buitink ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Root Zone ◽  
Remote Sensing Data ◽  
Root Water Uptake ◽  
Data Sources ◽  
Annual Number ◽  
Hydrological Models ◽  
Sensing Data ◽  
Snow Storage

Abstract. Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did not reject models given the uncertainties in these data sources and their changing relevance for the system under investigation.

Quantification of Yield, Water, and Carbon budget at intra-field scale using the AgriCarbon-EO tool

2021 ◽  
Author(s):  
Ahmad Al Bitar ◽  
Taeken Wijmer ◽  
Ludovic Arnaud ◽  
Remy Fieuzal ◽  
Gaetan Pique ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Water Budget ◽  
Carbon Budget ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Transfer Model ◽  
Sensing Data ◽  
The Impact

<p>Achieving the United Nations Sustainable Development Goal 2 that addresses food security and sustainable agriculture requires the promotion of readily transferable and scalable agronomical solutions. The combination of high-resolution remote sensing data, field information, and physical models is identified as a robust way of answering this requirement.  Here, we present the AgriCarbon-EO tool, a decision support system that provides the yield, biomass, water and carbon budget components of agricultural fields at a 10m resolution and at a regional scale. The tool assimilates high resolution optical remote sensing data from Copernicus Sentinel-2 satellites into a  radiative transfer model and a crop model. First, the application of a spatial Bayesian retrieval approach to the PROSAIL radiative transfer model provides Leaf Area Index (LAI) with its associated uncertainty. Second, LAI is assimilated into the SAFYE-CO2 crop model using a temporal Bayesian retrieval that enables the calculation of the yield, biomass, carbon and water budgets components with their associated uncertainties. In addition to remote sensing data, input datasets of crop types, weather and soil data are used to constrain the system. The concise weather data is provided from local weather stations or weather forecasts and is used to force the crop model (SAFYE-CO2) dynamics. The soil data are used in two folds. First to better parametrize the soil emissions in the radiative model retrievals and second to parametrise the water infiltration in the soil module of the crop model. The AgriCarbon-EO tool has been optimized to enable the computation of the yield, carbon, and water budget at high spatial resolution (10m) and large scale (100km2). The model is applied over the South-West of France covered by 3 Sentinel-2 tiles for major crops (wheat, maize,  sunflower). The outputs are validated over experimental plots for biomass, yield, soil moisture, and CO2 fluxes located all in the South-West of France. The experimental sites include the FR-AUR and FR-LAM ICOS sites and 22 cropland fields (biomass sampling). The validation exercise is done for the 2017-2018 and 2019-2020 cultural years. We show the added value of the use of high resolution in driving the crop model to take into account the impact of complex processes that are embedded in the LAI signal like vegetation water stress, disease, and agricultural practices. We show that the system is capable of providing the yield, carbon, and water budget of major crops accurately.  At the regional scale, we give global estimates of the carbon budget, water needs, and yields per crop type. We present the impact of intra-plot heterogeneity in the estimation of yield and the annual carbon and water budget showing the added value for high-resolution intra-plot modeling.</p>

Linking thermal UAS-based imagery and Cosmic-Ray Neutron Sensing data

2020 ◽  
Author(s):  
Veronika Döpper ◽  
Tobias Gränzig ◽  
Michael Förster ◽  
Birgit Kleinschmit
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Hydrological Modeling ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Cosmic Ray ◽  
Added Value ◽  
Innovative Technology ◽  
Sensing Data ◽  
Local Point

<p>Soil moisture content (SMC) is of fundamental importance to many hydrological, biological, biochemical and atmospheric processes. Common soil moisture measurements range from local point measurements to global remote sensing-based SMC datasets. Nevertheless, they always compromise between temporal and spatial resolution. Thus, it is still challenging to quantify spatially and temporally distributed SMC at a regional scale which is extremely relevant for hydrological modeling or agricultural management. The innovative technology Cosmic-Ray Neutron Sensing (CRNS) shows significant potential to fill this gap by quantifying the present hydrogen pools within footprints larger than 0.1 ha.</p><p>Owing to the difference in scale between the ground resolution of satellites used to retrieve soil moisture and the common point scale of ground-based soil moisture instruments, the large footprint of the CRNS poses a high potential for the validation of SMC remote sensing products. When linking the CRNS measurements with remote sensing data, the vertical and horizontal characteristics of its footprint need to be considered.</p><p>To examine the influence of the CRNS footprint characteristics on the linkage of CRNS and remote sensing data, we couple CRNS measurements with high-resolution UAS-based thermal imagery acquired at two sites in Bavaria and Brandenburg (Germany) using a radiometrically calibrated FLIR Tau 2 336 (FLIR Systems, Inc., Wilsonville, OR, USA) with a focal length of 9 mm. Within this context, we evaluate the added value of applying a horizontal weighting function to the spatially distributed thermal data in comparison to an unweighted mean when statistically representing the corrected neutron counting rates.</p><p>The project is part of the DFG-funded research group Cosmic Sense, which aims to provide interdisciplinary new representative insights into hydrological changes at the land surface.</p>

scholarly journals Modeling Evapotranspiration during SMACEX: Comparing Two Approaches for Local- and Regional-Scale Prediction

2005 ◽  
Vol 6 (6) ◽  
pp. 910-922 ◽  
Author(s):  
H. Su ◽  
M. F. McCabe ◽  
E. F. Wood ◽  
Z. Su ◽  
J. H. Prueger
Keyword(s):  
Remote Sensing ◽  
Surface Energy ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
High Quality ◽  
Sensing Data ◽  
Mean Square Errors

Abstract The Surface Energy Balance System (SEBS) model was developed to estimate land surface fluxes using remotely sensed data and available meteorology. In this study, a dual assessment of SEBS is performed using two independent, high-quality datasets that are collected during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX). The purpose of this comparison is twofold. First, using high-quality local-scale data, model-predicted surface fluxes can be evaluated against in situ observations to determine the accuracy limit at the field scale using SEBS. To accomplish this, SEBS is forced with meteorological data derived from towers distributed throughout the Walnut Creek catchment. Flux measurements from 10 eddy covariance systems positioned on these towers are used to evaluate SEBS over both corn and soybean surfaces. These data allow for an assessment of modeled fluxes during a period of rapid vegetation growth and varied hydrometeorology. Results indicate that SEBS can predict evapotranspiration with accuracies approaching 10%–15% of that of the in situ measurements, effectively capturing the temporal development of surface flux patterns for both corn and soybean, even when the evaporative fraction ranges between 0.50 and 0.90. Second, utilizing high-resolution remote sensing data and operational meteorology, a catchment-scale examination of model performance is undertaken. To extend the field-based assessment of SEBS, information derived from the Landsat Enhanced Thematic Mapper (ETM) and data from the North American Land Data Assimilation System (NLDAS) were combined to determine regional surface energy fluxes for a clear day during the field experiment. Results from this analysis indicate that prediction accuracy was strongly related to crop type, with corn predictions showing improved estimates compared to those of soybean. Although root-mean-square errors were affected by the limited number of samples and one poorly performing soybean site, differences between the mean values of observations and SEBS Landsat-based predictions at the tower sites were approximately 5%. Overall, results from this analysis indicate much potential toward routine prediction of surface heat fluxes using remote sensing data and operational meteorology.

Sign in / Sign up

Export Citation Format

Share Document