The SCATSAT-1 (Scatterometer Satellite) was launched by ISRO (Indian Space Research Organisation) on September 26, 2016 from the Satish Dhawan Space Centre, Sriharikota, India. With nearly five years of its journey, the Ku-band (13.5 GHz) based SCATSAT-1 made a profound impact on many scientific domains such as ocean-atmosphere dynamics, soil moisture and vegetation dynamics, climate change, hydrology and polar sea-ice and snowmelt studies. As a successor of the Oceansat-2 Scatterometer (OSCAT), the SCATSAT-1 supports the long-term analysis in climate studies, crop yield prediction, and forecasting analysis. In addition, the SCATSAT-1 provides the four different levels of data products at an enhanced resolution to improve the scope of the scatterometer in different applications. Recently the SCATSAT-1 has been explored in many emerging applications apart from oceanography e.g., crop growth, snow cover analysis, jute crop detection and river level estimation with advanced algorithms i.e., machine learning-based classification, information fusion, and super-resolution mapping. Therefore, it is desired to summarise all operational SCATSAT-1 products, applications, and their emerging trends at the global level in the various scientific domains. This paper has summarized the progress made by SCATSAT-1 in different scientific domains since its launch. A meta-analysis has also been conducted in this paper (using the SCOPUS database) to analyse the current research status of SCATSAT-1 in terms of study area targets. This study highlights the features, challenges, and future directions for the scatterometer improvements.

In the outer solar system, a growing number of giant planet satellites are now known to be abodes for global oceans hidden below an outer layer of ice. These planetary oceans are a natural laboratory for studying physical oceanographic processes in settings that challenge traditional assumptions made for Earth's oceans. While some driving mechanisms are common to both systems, such as buoyancy-driven flows and tides, others, such as libration, precession, and electromagnetic pumping, are likely more significant for moons in orbit around a host planet. Here, we review these mechanisms and how they may operate across the solar system, including their implications for ice-ocean interactions. Future studies should continue to advance our understanding of each of these processes as well as how they may act together in concert. This interplay also has strong implications for habitability as well as testing oceanic hypotheses with future missions.

Using long-term moorings data together with wind and sea ice measurements, we document the characteristics and variations of upwelling in Barrow Canyon and investigate the upwelled Atlantic Water (AW) on the Chukchi Sea shelf and how it impacts the ice cover. Driven by strong northeasterly winds, upwelling occurs more often in the cold months, and the occurrence tends to increase interannually since 2001. Over the 12-year mooring record at the mouth of Barrow Canyon, roughly 10% of the upwelling events can drive AW onto the Chukchi Sea shelf. Both AW and non-AW upwelling events have more occurrence and stronger strength in the cold months, but do not present a significant interannual trend. These variations are associated with the northeasterly winds. Comparing to the non-AW upwelling, the AW upwelling is generally characterized by more vertical displacement of the AW layer at the mouth of Barrow Canyon, and stronger up-canyon volume and heat transport. In the ice-covered period, these two types of upwelling have different consequences for forming polynyas on the shelf. Under similar wind forcing, the ice reduction appears confined in the coastal region in the non-AW upwelling events, while during AW upwelling events, the sea ice declines dramatically in the shelf interior with 15% more ice loss. It elucidates that the heat carried by the upwelled AW plays a considerable role in modulating the ice cover in the shelf interior.

Global warming increases the frequency and intensity of climate extremes, but the changes in climate extremes over the Antarctic Ice Sheet (AIS) during different periods are unknown. Changes in surface temperature extreme indices (TN10p, TX10p, TN90p, TX90p, CSDI, WSDI, TNn, TNx, TXn, TXx and DTR) are assessed during 2021-2050 and 2071-2100 under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, based on the multi-model ensemble mean (MMEM) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The extreme indices, excluding TXn and DTR, illustrate the opposite trend in the two periods in SSP1-2.6 over the AIS. Generally, the changes in extreme indices reflect the continued warming over AIS in the future, and the warming is projected to intensify in SSP3-7.0 and SSP5-8.5. The variations in the extreme indices exhibit regional differences. The Antarctic Peninsula displays rapid changes in TNn, TXn and DTR. In SSP5-8.5, the magnitudes of all climate index tendencies are greater during 2071-2100 than 2021-2050. The variations in TX10p, TX90p, TN10p, TN90p, WSDI and CSDI are faster in the Antarctic inland than in the other regions over the AIS. However, the decrease in the DTR is concentrated along the AIS coast and extends to the interior region, whereas the increasing trend occurs in the Antarctic inland. In West AIS, TX90p and TN90p rapidly increase during 2021-2050, whereas the rapid changing signals disappear in this region in 2071-2100. The dramatic changes in TNn, TXn and DTR occur at the Ross Ice Shelf during 2071-2100, indicating an increased risk of collapse. For TNx and TXx, the degree of warming in the later part of the 21st century is divided by the transantarctic mountains, and greater changes appear on the eastern side. Generally, Antarctic amplification of TNn, TXn and DTR is observed except under SSP1-2.6. In addition, TNx and TXx amplifications occur in SSP3-7.0 and SSP5-8.5.

Marine heatwaves (MHWs) are becoming increasingly frequent and intense around China, impacting marine ecosystems and coastal communities. Accurate forecasting of MHWs is crucial for their management and mitigation. In this study, we assess the forecasting ability of the global eddy-resolving ocean forecast system LICOM Forecast System (LFS) for the MHW events in October 2021 around China. Our results show that the 1-day lead forecast by the LFS accounts for up to 79% of the observed MHWs, with the highest skill during the initial and decay periods. The forecasted duration and intensity of the MHW event are consistent with observations but with some deviations in specific regions of the Yellow and South China seas. A detailed analysis of the heat budget reveals that the forecasted shortwave radiation flux is a key factor in the accuracy of the forecasted MHW duration and intensity. The oceanic dynamic term also greatly contributes to the accuracy in the southern Yellow Sea. In addition, the increasing bias of the forecasted duration and intensity with lead time are mainly caused by the underestimated shortwave radiation. Our findings suggest that improving the accuracy of oceanic dynamic processes and surface radiation fluxes in the LFS could be a promising direction to enhance the forecasting ability of marine extreme events such as MHWs.

The negative freeboard of sea ice (i.e., the height of ice surface below sea level) with subsequent flooding is widespread in the Southern Ocean, as opposed to the Arctic, due to the relatively thicker ice and thinner snow. In this study, we used the observations of snow and ice thickness from 103 ice mass balance buoys (IMBs) and NASA Operation IceBridge Aircraft Missions to investigate the spatial distribution of negative freeboard of Arctic sea ice. The Result showed that seven IMBs recorded negative freeboards, which were sporadically located in the seas around Northeast Greenland, the Central Arctic Ocean, and the marginal areas of the Chukchi-Beaufort Sea. The observed maximum values of negative freeboard could reach -0.12 m in the seas around Northeast Greenland. The observations from IceBridge campaigns also revealed negative freeboard comparable to those of IMBs in the seas around North Greenland and the Beaufort Sea. We further investigated the large-scale distribution of negative freeboard using NASA CryoSat-2 radar altimeter data, and the result indicates that except for the negative freeboard areas observed by IMBs and IceBridge, there are negative freeboards in other marginal seas of the Arctic Ocean. However, the comparison of the satellite data with the IMB data and IceBridge data shows that the Cryosat-2 data generally overestimate the extent and magnitude of the negative freeboard in the Arctic.