Haiyang-2B (HY-2B) is the second marine dynamic environment satellite of China. Sea surface temperature (SST) products from the scanning microwave radiometer (SMR) onboard HY-2B satellite are evaluated against in situ measurements. Approximately, ten months of data are used for the initial evaluation, from January 15, 2019 to November 15, 2019. The temporal and spatial windows for collocation are 30 min and 25 km, respectively, which produce 450 416 matchup pairs between HY-2B/SMR and in situ SSTs. The statistical comparison of the entire data set shows that the mean bias is −0.13 °C (SMR minus buoy), and the corresponding root-mean-square error (RMSE) is 1.06 °C. Time series of collocations for the SST difference shows that a good agreement is found between HY-2B/SMR and in situ SSTs after June 15, revealing a mean bias and an RMSE of only 0.09 °C and 0.72 °C, respectively. A three-way error analysis is conducted between the SMR, Global Precipitation Measurement Microwave Imager (GMI), and in situ SSTs. Individual standard deviations are found to be 0.41 °C for the GMI SST, 0.15 °C for the in situ SST, and 1.03 °C for the SMR SST. The results indicate that the HY2B/SMR SST products need to be improved during the period from January 15, 2019 to June 15, 2019.

This study compares wind speeds derived from five satellite microwave radiometers with those directly observed by buoy-mounted anemometers and the global analyses produced by the European Center for Medium-Range Weather Forecasts (ECMWF) model. Buoy comparisons yield wind speed root mean square errors of 0.82 m/s for WindSat, 1.45 m/s for SSMIS F16, 1.39 m/s for SSMIS F17, 1.43 m/s for AMSR-E, and 1.45 m/s for AMSR2. The overall mean bias for each satellite is typically <0.25 m/s when averaged over all selected buoys for a given study time. The satellite wind speeds are underestimated with respect to the buoy observations at a band of the tropical Pacific Ocean from −8°S to 4°N. The mean buoy–satellite difference as a function of year is always <0.4 m/s, except for SSMIS F16. The selected satellite wind speeds show an obvious seasonal characteristic at high latitudes. In comparison with the ECMWF data, some obviously positive differences exist at high southern latitudes in January and at high northern latitudes in July.

Synthetic Aperture Radar (SAR), with its ability to function under any weather conditions and at any time of day, along with multi-polarization and frequent revisit capabilities, plays a crucial role in flood monitoring. However, SAR images face challenges such as coherent speckle noise, feature mixing, terrain undulation, and adverse weather, making flood monitoring difficult. To address these challenges, this paper proposes a high-precision flood mapping method from Sentinel-1 dual-polarization SAR data. We begin by generating false-color images through polarization combination and apply them to a multiscale segmentation approach, overcoming the limitations of single-polarization scattering and effectively reducing speckle noise. Digital elevation model and reference water datasets are integrated into the segmentation process to mask terrain shadowing and permanent water. To reduce feature mixing effects, the optimal SAR image with minimal feature mixing is selected for flood mapping using the Gaussian Mixture Model. In the subsequent two-step classification process, fuzzy sets of texture features are incorporated to assist in categorizing uncertain regions, further reducing interference from feature mixing and enhancing flood recognition accuracy. Additionally, integrating pixel-level and object-level analyses minimizes errors caused by improper segmentation. The proposed method is compared with several well-established algorithms, and the results demonstrate that our method outperforms the others in flood mapping accuracy. Analysis of years of flooding on the Leizhou Peninsula shows that Sentinel-1 SAR has the potential to effectively monitor the occurrence and development of floods.

The realistic and accurate estimation of hurricane intensity is highly desired in many scientific and operational applications. With the advance of passive microwave polarimetry, an alternative opportunity for retrieving wind speed in hurricanes has become available. A wind speed retrieval algorithm for wind speeds above 20 m/s in hurricanes has been developed by using the 6.8 and 10.7 GHz vertically and horizontally polarized brightness temperatures of WindSat. The WindSat measurements for 15 category 4 and category 5 hurricanes from 2003 to 2010 and the corresponding H*wind analysis data are used to develop and validate the retrieval model. In addition, the retrieved wind speeds are also compared to the Remote Sensing Systems (RSS) global all-weather product and stepped-frequency microwave radiometer (SFMR) measurements. The statistical results show that the mean bias and the overall root-mean-square (RMS) difference of the retrieved wind speeds with respect to the H*wind analysis data are 0.04 and 2.75 m/s, respectively, which provides an encouraging result for retrieving hurricane wind speeds over the ocean surface. The retrieved wind speeds show good agreement with the SFMR measurements. Two case studies demonstrate that the mean bias and RMS difference are 0.79 m/s and 1.79 m/s for hurricane Rita-1 and 0.63 m/s and 2.38 m/s for hurricane Rita-2, respectively. In general, the wind speed retrieval accuracy of the new model in hurricanes ranges from 2.0 m/s in light rain to 3.9 m/s in heavy rain.

Two new parameters (W6H and W6V) were defined that represent brightness temperature increments for different low-frequency channels due to ocean wind. We developed a new wind speed retrieval model inside hurricanes based on W6H and W6V using brightness temperature data from AMSR-E. The AMSR-E observations of 12 category 3–5 hurricanes from 2003 to 2011 and corresponding data from the H*wind analysis system were used to develop and validate the AMSR-E wind speed retrieval model. The results show that the mean bias and the overall root-mean-square (RMS) difference of the AMSR-E retrieved wind speeds with respect to H*wind (HRD Real-time Hurricane Wind Analysis System) analysis data were −0.01 m/s and 2.66 m/s, respectively. One case study showed that W6H and W6V were less sensitive to rain than the observed AMSR-E C-band and X-band brightness temperature data. The AMSR-E retrieval model was further validated by comparing the retrieved wind speeds against stepped-frequency microwave radiometer (SFMR) measurements. The comparison showed an RMS difference of 3.41 m/s and a mean bias of 0.49 m/s.