C‐band high‐resolution radar (synthetic aperture radar [SAR]) is the only spaceborne instrument able to probe at very high resolution and over all ocean basins the sea surface under extreme weather conditions. When coanalyzed with Stepped Frequency Microwave Radiometer wind estimates, the radar backscatter signals acquired in major hurricanes from Sentinel‐1 and Radarsat‐2 SAR reveal high sensitivity in the cross‐polarized channel for wind speeds up to 75 m/s. The combination of the two copolarized and cross‐polarized channels can then be used to derive high‐resolution surface wind estimates. The retrieval methods and impacts of intense rainfall are discussed in the context of a Hurricane Irma (2017) case study. On 7 September 2017, Sentinel‐1 measurements intercepted Hurricane Irma when it was at category 5 intensity. When compared to Stepped Frequency Microwave Radiometer, SAR‐derived wind speeds yield bias and root‐mean‐square of about 1.5 and 5.0 m/s, respectively. The retrieved wind structure parameters for the outer core are found to be in agreement with the Best‐Track and combined satellite‐ and aircraft‐based analyses. SAR measurements uniquely describe the inner core and provide independent measurements of the maximum wind speed and the radius of maximum wind. Near the radius of maximum wind a 65‐m/s increase in wind speed in less than 10 km is detected, corresponding to an instantaneous absolute vorticity of order 210 times the Coriolis parameter. Using a parametric Holland model and the environmental surface pressure (1,011 hPa), SAR‐derived wind speeds correspond to a central surface pressure of 918 hPa (921 hPa from the Best‐Track) in Irma's eye.

Co-located over extreme events, C-band co-polarized and cross-polarized normalized radar cross sections (NRCS) and L-band ocean surface roughness brightness temperature (TB,rough) are directly compared to analyze the similarities and differences between these two parameters at medium resolution (about 25 km). NRCS in VH-polarization and VV-polarization (σ0,VH, σ0,VV) were acquired by Sentinel-1 C-band synthetic aperture radar. TB,rough is estimated from brightness temperatures (TB) measured by the L-band radiometer on-board the Soil Moisture Active Passive mission. When the rain rate is less than 20 mm/h, a striking linear relationship is found between active C-Band cross-polarized NRCS and passive L-Band TB,rough: σ0,VH(θSAR) ∝ tan(θ SAR) x TB,rough(θ SMAP= 40°), without any apparent saturation for TB,rough ranging from 3.5 to 17 K. Compared to both high TB,rough and σ 0,VH, co-polarized σ 0,VV measurements saturate. As interpreted, this can correspond to a regime change of the air-sea interactions during extreme events. In heavy rain conditions, C-band co-polarized NRCS decreases for extreme situations. In these cases, the covariation between C-band cross-polarized NRCS and L-band TB,rough is less evident. An accurate and unambiguous assessment of the impact of rain will deserve further investigations.

Lakes are a crucial component of inland water bodies, and changes in their water levels serve as key indicators of global climate change. Traditional methods of lake water level monitoring rely heavily on hydrological stations, but there are problems such as regional representativeness, data stability, and high maintenance costs. The satellite altimeter is an essential tool in lake research, with the Synthetic Aperture Radar (SAR) altimeter offering a high spatial resolution. This enables precise and quantitative observations of lake water levels on a large scale. In this study, we used Sentinel-3A SAR Radar Altimeter (SRAL) data to establish a more reasonable lake height inversion algorithm for satellite-derived lake heights. Subsequently, using this technology, a systematic analysis study was conducted with Qinghai Lake as the case study area. By employing regional filtering, threshold filtering, and altimeter range filtering techniques, we obtained effective satellite altimeter height measurements of the lake surface height. To enhance the accuracy of the data, we combined these measurements with GPS buoy-based geoid data from Qinghai Lake, normalizing lake surface height data from different periods and locations to a fixed reference point. A dataset based on SAR altimeter data was then constructed to track lake surface height changes in Qinghai Lake. Using data from the Sentinel-3A altimeter’s 067 pass over Qinghai Lake, which has spanned 96 cycles since its launch in 2016, we analyzed over seven years of lake surface height variations. The results show that the lake surface height exhibits distinct seasonal patterns, peaking in September and October and reaching its lowest levels in April and May. From 2016 to 2023, Qinghai Lake showed a general upward trend, with an increase of 2.41 m in lake surface height, corresponding to a rate of 30.0 cm per year. Specifically, from 2016 to 2020, the lake surface height rose at a rate of 47.2 cm per year, while from 2020 to 2022, the height remained relatively stable.

Evaluating sea surface temperature (SST) products is essential before their application in marine environmental monitoring and related studies. SSTs from the in situ SST Quality Monitor (iQuam) system, Advanced Microwave Scanning Radiometer 2 (AMSR2) aboard the Global Change Observation Mission 1st-Water, and the Microwave Radiation Imager (MWRI) aboard the Chinese Fengyun-3D satellite are intercompared utilizing extended triple collocation (ETC) and direct comparison methods. Additionally, error characteristic variations with respect to time, latitude, SST, sea surface wind speed, columnar water vapor, and columnar cloud liquid water are analyzed comprehensively. In contrast to the prevailing focus on SST validation accuracy, the random errors and the capability to detect SST variations are also evaluated in this study. The result of ETC analysis indicates that iQuam SST from ships exhibits the highest random error, above 0.83 °C, whereas tropical mooring SST displays the lowest random error, below 0.28 °C. SST measurements from drifters, tropical moorings, Argo floats, and high-resolution drifters, which possess random errors of less than 0.35 °C, are recommended for validating remotely sensed SST. The ability of iQuam, AMSR2, and MWRI to detect SST variations diminishes significantly in ocean areas between 0°N and 20°N latitude and latitudes greater than 50°N and 50°S. AMSR2 and iQuam demonstrate similar random errors and capabilities for detecting SST variations, whereas MWRI shows a high random error and weak capability. In comparison to iQuam SST, AMSR2 exhibits a root-mean-square error (RMSE) of about 0.51 °C with a bias of −0.05 °C, while MWRI shows an RMSE of about 1.26 °C with a bias of −0.14 °C.

Haiyang 2B (HY-2B), the second Chinese ocean dynamic environment monitoring satellite, has been operational for nearly six years. The scanning microwave radiometer (SMR) onboard HY-2B provides global sea surface temperature (SST) observations. Comprehensive validation of these data is essential before they can be effectively applied. This study evaluates the operational SST product from the SMR, covering the period from 1 January 2019 to 31 August 2024, using direct comparison and extended triple collocation (ETC) methods. The direct comparison assesses bias and root mean square error (RMSE), while ETC analysis estimates the random error of the SST measurement systems and evaluates their ability to detect SST variations. Additionally, the spatial and temporal variations in error characteristics, as well as the crosstalk effects of sea surface wind speed, columnar water vapor, and columnar cloud liquid water, are analyzed. Compared with iQuam SST, the total RMSE of SMR SST for ascending and descending passes are 0.88 °C and 0.85 °C, with total biases of 0.1 °C and −0.08 °C, respectively. ETC analysis indicates that the random errors for ascending and descending passes are 0.87 °C and 0.80 °C, respectively. The SMR’s ability to detect SST variations decreases significantly at high latitudes and near 10°N latitude. Error analysis reveals that the uncertainty in SMR SSTs has increased over time, and the presence of crosstalk effects in SMR SST retrieval has been confirmed.

Microwave Radiation Imagers (MWRIs) onboard the FY-3B, FY-3C, and FY-3D satellites are the primary sensors for sea surface temperature (SST) observation. Benefiting from the resolution of several key calibration issues in brightness temperature products, MWRI SST records spanning more than a decade have been reprocessed. In this study, these reprocessed SST products are evaluated using direct comparison and the extended triple collocation (ETC) method, along with additional error analyses. Compared with iQuam SST, the reprocessed MWRI SST products from the three satellites show total root mean square errors (RMSEs) of 0.80–0.82 °C and total biases of −0.12 °C to 0.00 °C. ETC analyses based on MWRI, ERA5, and Argo SSTs indicate random errors of 0.76–0.78 °C. Furthermore, the reprocessed MWRI SST products demonstrate temporal stability and exhibit minimal crosstalk effects from sea surface wind speed, columnar water vapor, and columnar cloud liquid water in SST retrievals. Compared with previous versions, the reprocessed products show significant improvements, with consistent performance across FY-3B, FY-3C, and FY-3D. However, differences in SST observations due to the varying local times of the ascending nodes among the three satellites should be corrected in practical applications.