Recent flood events have demonstrated a demand for satellite-based inundation mapping in near real-time (NRT). Simulating and forecasting flood extent is essential for risk mitigation. While numerical models are designed to provide such information, they usually lack reference at fine spatiotemporal resolution. Remote sensing techniques are expected to fill this void. Unlike optical sensors, synthetic aperture radar (SAR) provides valid measurements through cloud cover with high resolution and increasing sampling frequency from multiple missions. This study reviews theories and algorithms of flood inundation mapping using SAR data, together with a discussion of their strengths and limitations, focusing on the level of automation, robustness, and accuracy. We find that the automation and robustness of non-obstructed inundation mapping have been achieved in this era of big earth observation (EO) data with acceptable accuracy. They are not yet satisfactory, however, for the detection of beneath-vegetation flood mapping using L-band or multi-polarized (dual or fully) SAR data or for urban flood detection using fine-resolution SAR and ancillary building and topographic data.

An extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using a minimum of 6 years (within the period of 2000–13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cévennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.25° spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP). SBR products are categorized into those that include gauge adjustment versus unadjusted. Results show that performance of SBR is highly dependent on the rainfall variability. Many SBR products usually underestimate wet season and overestimate dry season precipitation. The performance of gauge adjustment to the SBR products varies by region and depends greatly on the representativeness of the rain gauge network.

An extensive evaluation of two global-scale high-resolution satellite rainfall products is performed using 8 yr (2003–10) of reference rainfall data derived from a network of rain gauges over Europe. The comparisons are performed at a daily temporal scale and 0.25° spatial grid resolution. The satellite rainfall techniques investigated in this study are the Tropical Rainfall Measuring Mission (TRMM) 3B42 V6 (gauge-calibrated version) and the Climate Prediction Center morphing technique (CMORPH). The intercomparison and validation of these satellite products is performed both qualitatively and quantitatively. In the qualitative part of the analysis, error maps of various validation statistics are shown, whereas the quantitative analysis provides information about the performance of the satellite products relative to the rainfall magnitude or ground elevation. Moreover, a time series analysis of certain error statistics is used to depict the temporal variations of the accuracy of the two satellite techniques. The topographical and seasonal influences on the performance of the two satellite products over the European domain are also investigated. The error statistics presented herein indicate that both orography and seasonal variability affect the efficiency of the satellite rainfall retrieval techniques. Specifically, both satellite techniques underestimate rainfall over higher elevations, especially during the cold season, and their performance is subject to seasonal changes. A significant difference between the two satellite products is that TRMM 3B42 V6 generally overestimates rainfall, while CMORPH underestimates it. CMORPH’s mean error is shown to be of higher magnitude than that of 3B42 V6, while in terms of random error variance, CMORPH exhibits lower (higher) values than those of 3B42 V6 in the winter (summer) months.

The great success of the Tropical Rainfall Measuring Mission (TRMM) and its successor Global Precipitation Measurement (GPM) has accelerated the development of global high-resolution satellite-based precipitation products (SPP). However, the quantitative accuracy of SPPs has to be evaluated before using these datasets in water resource applications. This study evaluates the following GPM-era and TRMM-era SPPs based on two years (2014–2015) of reference daily precipitation data from rain gauge networks in ten mountainous regions: Integrated Multi-SatellitE Retrievals for GPM (IMERG, version 05B and version 06B), National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center Morphing Method (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Multi-Source Weighted-Ensemble Precipitation (MSWEP), which represents a global precipitation data-blending product. The evaluation is performed at daily and annual temporal scales, and at 0.1 deg grid resolution. It is shown that GSMaPV07 surpass the performance of IMERGV06B Final for almost all regions in terms of systematic and random error metrics. The new orographic rainfall classification in the GSMaPV07 algorithm is able to improve the detection of orographic rainfall, the rainfall amounts, and error metrics. Moreover, IMERGV05B showed significantly better performance, capturing the lighter and heavier precipitation values compared to IMERGV06B for almost all regions due to changes conducted to the morphing, where motion vectors are derived using total column water vapor for IMERGV06B.

Since the successful launch of the Tropical Rainfall Measuring Mission (TRMM) satellite, measurements of a wide variety of precipitating systems have been obtained with unprecedented detail from the first space-based radar [precipitation radar (PR)]. In this research, a methodology is developed that matches coincident PR and ground-based volume scanning weather radar observations in a common earth parallel three-dimensional Cartesian grid. The data matching is performed in a way that minimizes uncertainties associated with the type of weather seen by the radars, grid resolution, and differences in radar sensitivities, sampling volumes, viewing angles, and radar frequencies. The authors present comparisons of reflectivity observations from the PR and several U.S. weather surveillance Doppler radars (WSR-88D) as well as research radars from the TRMM field campaigns in Kwajalein Atoll and the Large Biosphere Atmospheric (LBA) Experiment. Correlation values above 0.8 are determined between PR and gro... Abstract Since the successful launch of the Tropical Rainfall Measuring Mission (TRMM) satellite, measurements of a wide variety of precipitating systems have been obtained with unprecedented detail from the first space-based radar [precipitation radar (PR)]. In this research, a methodology is developed that matches coincident PR and ground-based volume scanning weather radar observations in a common earth parallel three-dimensional Cartesian grid. The data matching is performed in a way that minimizes uncertainties associated with the type of weather seen by the radars, grid resolution, and differences in radar sensitivities, sampling volumes, viewing angles, and radar frequencies. The authors present comparisons of reflectivity observations from the PR and several U.S. weather surveillance Doppler radars (WSR-88D) as well as research radars from the TRMM field campaigns in Kwajalein Atoll and the Large Biosphere Atmospheric (LBA) Experiment. Correlation values above 0.8 are determined between PR and gro...