Using the space-borne NASA scatterometer (NSCAT) to determine the frozen and thawed seasons

We hypothesize that the strong sensitivity of radar backscatter to surface dielectric properties, and hence to the phase (solid or liquid) of any water near the surface should make space-borne radar observations a powerful tool for large-scale spatial monitoring of the freeze/thaw state of the land surface, and thus ecosystem growing season length. We analyzed the NASA scatterometer (NSCAT) backscatter from September 1996 to June 1997, along with temperature and snow depth observations and ecosystem modeling, for three BOREAS sites in central Canada. Because of its short wavelength (2.14 cm), NSCAT was sensitive to canopy and surface water. NSCAT had 25 km spatial resolution and approximately twice-daily temporal coverage at the BOREAS latitude. At the northern site the NSCAT signal showed strong seasonality, with backscatter around −8 dB in winter and −12 dB in early summer and fall. The NSCAT signal for the southern sites had less seasonality. At all three sites there was a strong decrease in backscatter during spring thaw (4–6 dB). At the southern deciduous site, NSCAT backscatter rose from −11 to −9.2 dB during spring leaf-out. All sites showed 1–2 dB backscatter shifts corresponding to changes in landscape water state coincident with brief midwinter thaws, snowfall, and extreme cold (Tmax\u3c−25°C). Freeze/thaw detection algorithms developed for other radar instruments gave reasonable results for the northern site but were not successful at the two southern sites. We developed a change detection algorithm based on first differences of 5-day smoothed NSCAT backscatter measurements. This algorithm had some success in identifying the arrival of freezing conditions in the autumn and the beginning of thaw in the spring. Changes in surface freeze/thaw state generally coincided with the arrival and departure of the seasonal snow cover and with simulated shifts in the directions of net carbon exchange at each of the study sites

Relating surface backscatter response from TRMM precipitation radar to soil moisture: Results over a semi-arid region

The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σº) of the surface. σº is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σº primarily depends on the soil water content. In this study we relate TRMMPR σº measurements to soil water content (m(s)) in the Lower Colorado River Basin (LCRB). σº dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σº that couples incidence angle, m(s), and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated m(s) is estimated using the Variable Infiltration Capacity (VIC) model and measured m(s) is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σº model is calibrated using VIC and WGEW m(s) data during 1998 and the calibrated model is used to derive m(s) during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with a correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σº derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σº dependence on soil water content in the arid regions

Development and Evaluation of a Multi-Year Fractional Surface Water Data Set Derived from Active/Passive Microwave Remote Sensing Data

abstract: The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of methane gas are key global change questions. We present a microwave satellite-based approach for mapping fractional surface water (FW) globally at 25-km resolution. The approach employs a land cover-supported, atmospherically-corrected dynamic mixture model applied to 20+ years (1992–2013) of combined, daily, passive/active microwave remote sensing data. The resulting product, known as Surface WAter Microwave Product Series (SWAMPS), shows strong microwave sensitivity to sub-grid scale open water and inundated wetlands comprising open plant canopies. SWAMPS’ FW compares favorably (R[superscript 2] = 91%–94%) with higher-resolution, global-scale maps of open water from MODIS and SRTM-MOD44W. Correspondence of SWAMPS with open water and wetland products from satellite SAR in Alaska and the Amazon deteriorates when exposed wetlands or inundated forests captured by the SAR products were added to the open water fraction reflecting SWAMPS’ inability to detect water underneath the soil surface or beneath closed forest canopies. Except for a brief period of drying during the first 4 years of observation, the inundation extent for the global domain excluding the coast was largely stable. Regionally, inundation in North America is advancing while inundation is on the retreat in Tropical Africa and North Eurasia. SWAMPS provides a consistent and long-term global record of daily FW dynamics, with documented accuracies suitable for hydrologic assessment and global change-related investigations.The final version of this article, as published in Remote Sensing, can be viewed online at: http://www.mdpi.com/2072-4292/7/12/1584

The ASCAT soil moisture product: a review of its specifications, validation results, and emerging applications

Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT) that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm), its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in applications. To provide a comprehensive overview of the major characteristics and caveats of the ASCAT soil moisture product, this paper describes the ASCAT instrument and the soil moisture processor and near-real-time distribution service implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). A review of the most recent validation studies shows that the quality of ASCAT soil moisture product is – with the exception of arid environments –comparable to, and over some regions (e.g. Europe) even better than currently available soil moisture data derived from passive microwave sensors. Further, a review of applications studies shows that the use of the ASCAT soil moisture product is particularly advanced in the fields of numerical weather prediction and hydrologic modelling. But also in other application areas such as yield monitoring, epidemiologic modelling, or societal risks assessment some first progress can be noted. Considering the generally positive evaluation results, it is expected that the ASCAT soil moisture product will increasingly be used by a growing number of rather diverse land applications.The Austrian Science Fund (FWF) through the Vienna Doctoral Programme on Water Resource Systems (http://www.waterresources.at/,DK-plusW1219-N22

Characterization of the spatial and temporal variability in pan-Arctic, terrestrial hydrology

Arctic hydrology represents an important component of the larger global climate system, and there are signs that significant water-cycle changes, involving complex feedbacks, have occurred. This dissertation explores the methods to estimate components of the arctic hydrological cycle, the numerous biases and uncertainties associated with the techniques, and suggestions for future research needs. The studies described here focus on quantitative models and methods for predicting the spatial and temporal variability in pan-Arctic hydrology. This dissertation discusses pan-Arctic water budgets drawn from a hydrological model which is appropriate for applications across the terrestrial Arctic. Including effects from soil-water phase changes results in increases in simulated annual runoff of 7% to 27%. A sensitivity analysis reveals that simulated runoff is far more sensitive to the time-varying climate drivers than to parameterization of the landscape. When appropriate climate data are used, the Pan-Arctic Water Balance Model (PWBM) is able to capture well the variability in seasonal river discharge at the scale of arctic sea basins. This dissertation also demonstrated a method to estimate snowpack thaw timing from radar data. Discrepancies between thaw timing inferred from the microwave backscatter data and the hydrological model are less than one week. The backscatter signal-to-noise values are highest in areas of higher seasonal snow accumulation, low to moderate tree cover and low topographic complexity. An evaluation of snow water equivalent (SWE) estimates drawn from land surface models and microwave remote sensing data suggests that simulated SWE from a hydrological model like PWBM, when forced with appropriate climate data, is far superior to current snow mass estimate derived from passive microwave data. Biases arising from interpolations from sparse, uneven networks can be significant. A bias of well over +10 mm yr-1 was found in the early network representations of spatial precipitation across Eurasia. When examining linkages between precipitation and river discharge, these biases limit our confidence in the accuracy of historical precipitation reconstructions. This dissertation assess our current capabilities in estimating components of arctic water cycle and reducing the uncertainties in predictions of arctic climate change