Your search found 5 records
1 Wagner, W.. 1995. Groundwater with low salinity in the dry regions of Western Asia, hydrochemical aspects. In Oman. Ministry of Water Resources, The Sultanate of Oman International Conference on Water Resources Management in Arid Countries, Muscat, Oman, 12-16 March 1995. Volume 2: Nizwa/Bahla Sessions, display papers. Muscat, Oman: The Ministry. pp.443-449.
(Location: IWMI-HQ Call no: 333.91 G728 OMA Record No: H016720)
2 Smith, A. M.; Scipal, K.; Wagner, W.. 2005. Active microwave systems for monitoring drought stress. In Boken, V. K.; Cracknell, A. P.; Heathcote, R. L. (Eds.), Monitoring and predicting agricultural drought: A global study. New York, NY, USA: OUP. pp.105-118.
(Location: IWMI-HQ Call no: 632.12 G000 BOK Record No: H036765)
3 Bartsch, A.; Kidd, R. A.; Pathe, C.; Scipal, K.; Wagner, W.. 2007. Satellite radar imagery for monitoring inland wetlands in boreal and sub-arctic environments. Aquatic Conservation: Marine and Freshwater Ecosystems, 17(3):305-317.
Remote sensing ; Satellite surveys ; Monitoring ; Wetlands ; Ecosystems ; Peatlands ; Methane ; Flooding / Siberia
(Location: IWMI HQ Call no: P 7887 Record No: H040098)
4 Brocca, L.; Crow, W. T.; Ciabatta, L.; Massari, C.; de Rosnay, P.; Enenkel, M.; Hahn, S.; Amarnath, Giriraj; Camici, S.; Tarpanelli, A.; Wagner, W.. 2017. A review of the applications of ASCAT [Advanced SCATterometer] soil moisture products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5):2285-2306. [doi: https://doi.org/10.1109/JSTARS.2017.2651140]
Soil moisture ; Hydrology ; Remote sensing ; Weather forecasting ; Radar ; Meteorological observations ; Satellite observation ; Hydrological cycle ; Climate change ; Rain ; Flooding ; Precipitation ; Evaporation ; Evapotranspiration ; Landslides
(Location: IWMI HQ Call no: e-copy only Record No: H048009)
Remote sensing of soil moisture has reached a level of good maturity and accuracy for which the retrieved products are ready to use in real-world applications. Due to the importance of soil moisture in the partitioning of the water and energy fluxes between the land surface and the atmosphere, a wide range of applications can benefit from the availability of satellite soil moisture products. Specifically, the Advanced SCATterometer (ASCAT) on board the series of Meteorological Operational (Metop) satellites is providing a near real time (and long-term, 9+ years starting from January 2007) soil moisture product, with a nearly daily (sub-daily after the launch of Metop-B) revisit time and a spatial sampling of 12.5 and 25 km. This study first performs a review of the climatic, meteorological, and hydrological studies that use satellite soil moisture products for a better understanding of the water and energy cycle. Specifically, applications that consider satellite soil moisture product for improving their predictions are analyzed and discussed. Moreover, four real examples are shown in which ASCAT soil moisture observations have been successfully applied toward: 1) numerical weather prediction, 2) rainfall estimation, 3) flood forecasting, and 4) drought monitoring and prediction. Finally, the strengths and limitations of ASCAT soil moisture products and the way forward for fully exploiting these data in real-world applications are discussed.
5 Kim, H.; Wigneron, J.-P.; Kumar, S.; Dong, J.; Wagner, W.; Cosh, M. H.; Bosch, D. D.; Collins, C. H.; Starks, P. J.; Seyfried, M.; Lakshmi, V. 2020. Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions. Remote Sensing of Environment, 251:112052. [doi: https://doi.org/10.1016/j.rse.2020.112052]
Soil moisture ; Estimation ; Irrigated farming ; Dry farming ; Satellite observation ; Vegetation ; Forests ; Evapotranspiration ; Precipitation ; Salinity ; Models
(Location: IWMI HQ Call no: e-copy only Record No: H050079)
(13.10 MB)
Over the past four decades, satellite systems and land surface models have been used to estimate global-scale surface soil moisture (SSM). However, in areas such as densely vegetated and irrigated regions, obtaining accurate SSM remains challenging. Before using satellite and model-based SSM estimates over these areas, we should understand the accuracy and error characteristics of various SSM products. Thus, this study aimed to compare the error characteristics of global-scale SSM over vegetated and irrigated areas as obtained from active and passive satellites and model-based data: Advanced Scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5), and Global Land Data Assimilation System (GLDAS). We employed triple collocation analysis (TCA) and caluclated conventional error metrics from in-situ SSM measurements. We also considered all possible triplets from 6 different products and showed the viability of considering the standard deviation of TCA-based numbers in producing robust results.
Over forested areas, it was expected that model-based SSM data might provide more accurate SSM estimates than satellites due to the intrinsic limitations of microwave-based systems. Alternately, over irrigated regions, observation-based SSM data were expected to be more accurate than model-based products because land surface models (LSMs) cannot capture irrigation signals caused by human activities. Contrary to these expectations, satellite-based SSM estimates from ASCAT, SMAP, and SMOS showed fewer errors than ERA5 and GLDAS SSM products over vegetated conditions. Furthermore, over irrigated areas, ASCAT, SMOS, and SMAP outperformed other SSM products; however, model-based data from ERA5 and GLDAS outperformed AMSR2. Our results emphasize that, over irrgated areas, considering satellite-based SSM data as alternatives to model-based SSM data sometimes produces misleading results; and considering model-based data as alternatives to satellite-based SSM data in forested areas can also sometimes be misleading. In addition, we discovered that no products showed much degradation in TCA-based errors under different vegetated conditions, while different irrigation conditions impacted both satellite and model-based SSM data sets.
The present research demonstrates that limitations in satellite and modeled SSM data can be overcome in many areas through the synergistic use of satellite and model-based SSM products, excluding areas where satellite-based data are masked out. In fact, when four satellite and model data sets are used selectively, the probability of obtaining SSM with stronger signal than noise can be close to 100%.
Over forested areas, it was expected that model-based SSM data might provide more accurate SSM estimates than satellites due to the intrinsic limitations of microwave-based systems. Alternately, over irrigated regions, observation-based SSM data were expected to be more accurate than model-based products because land surface models (LSMs) cannot capture irrigation signals caused by human activities. Contrary to these expectations, satellite-based SSM estimates from ASCAT, SMAP, and SMOS showed fewer errors than ERA5 and GLDAS SSM products over vegetated conditions. Furthermore, over irrigated areas, ASCAT, SMOS, and SMAP outperformed other SSM products; however, model-based data from ERA5 and GLDAS outperformed AMSR2. Our results emphasize that, over irrgated areas, considering satellite-based SSM data as alternatives to model-based SSM data sometimes produces misleading results; and considering model-based data as alternatives to satellite-based SSM data in forested areas can also sometimes be misleading. In addition, we discovered that no products showed much degradation in TCA-based errors under different vegetated conditions, while different irrigation conditions impacted both satellite and model-based SSM data sets.
The present research demonstrates that limitations in satellite and modeled SSM data can be overcome in many areas through the synergistic use of satellite and model-based SSM products, excluding areas where satellite-based data are masked out. In fact, when four satellite and model data sets are used selectively, the probability of obtaining SSM with stronger signal than noise can be close to 100%.
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