A system for detecting the onset of surface melting on Antarctic sea ice

Abstract

A system (100) for detecting the onset of surface melting on Antarctic sea ice, wherein the system (100) comprises: a scatterometer (102) for acquiring backscatter coefficient (σ0) data at Ku-band frequency from dual-polarized scatterometers, including horizontal (HH) and vertical (VV) polarization, and is configured to generate scatterometer image reconstruction images (SIR) for noise reduction; a passive microwave radiometer (104) connected to the scatterometer (102) and configured to detect the sea ice concentration (SIC) and brightness temperatures (Tb19H, Tb37H) at different polarizations and frequencies for surface melt detection; a data interpolation unit (106) connected to the scattering meter (102) and the passive microwave radiometer (104) and configured to fill data gaps in the scattering meter and radiometer data using multiple regression and median filtering; a processing unit (108) connected to the data interpolation unit (106), which is configured to determine the onset of melting based on changes in the patterns of the backscattering coefficient during the summer melt season; an atmospheric data unit (110) configured to retrieve surface air temperature, latent and sensible surface heat flux, net shortwave and longwave radiation, and net heat flux from reanalysis data sources; a unit (112) for verifying the onset of melting, configured to confirm the onset of melting by analyzing atmospheric data and comparing the derived SIC values ​​and brightness temperature fluctuations with the pre-thresholds of -10 K for the presence of liquid water in the snowpack; a motion tracking unit (114) configured to determine sea ice motion by tracking features such as floes using successive satellite imagery and weekly sea ice motion vectors; and a controller (116) configured to process and synchronize inputs from the scattering meter (102), radiometer (104) and atmospheric data unit (110) to detect and confirm the onset of surface melting across different spatial and temporal scales.

Description

FIELD OF THE INVENTION

[0001] The present invention relates to the field of climate science. More specifically, the present invention relates to a system for detecting the onset of surface melting of sea ice. BACKGROUND OF THE INVENTION

[0002] The global energy budget at the Earth's surface is significantly influenced by polar sea ice and its snow cover, which play a crucial role in regulating Earth's climate. Sea ice and snow, with their high albedo, reflect a significant portion of incoming solar radiation. Thick snow layers further increase the albedo and delay the onset of surface melting, while a reduced albedo leads to increased absorption of solar energy during the summer melt season. These processes underscore the importance of monitoring and understanding surface melting of sea ice, especially in the polar regions.

[0003] Satellite-based remote sensing has played a crucial role in studying surface melting processes over sea ice. Traditionally, passive microwave radiometry is used to detect surface melting by observing changes in brightness temperatures at specific frequencies. However, active microwave sensors such as radar scatterometers offer several advantages, including higher spatial resolution and the ability to detect melt-freezing transitions at regional to hemispheric scales. Scatterometers operate on the principle that microwave backscatter signatures decrease significantly in the presence of liquid water on the surface, thus providing a reliable indicator of the onset of melting.

[0004] The Antarctic region, characterized by its dynamic snow-ice interactions and seasonal variability, presents unique challenges for accurately determining the onset of melt. Southern Ocean sea ice cover plays a critical role in regulating heat and carbon exchange between the ocean and atmosphere. Regions such as the Weddell Sea and the Amundsen-Bellingshausen Sea are particularly dynamic during the summer melt season and are therefore important areas for scientific investigation. However, current methods for detecting surface melt, which rely primarily on passive microwave data, are limited by their relatively low spatial resolution and their inability to effectively capture the complexity of the snow cover.

[0005] Radar scatterometry has proven to be a promising approach for detecting melt onset by reducing backscatter associated with surface melting. Despite this potential, long-term, comprehensive Antarctic-wide studies using scatterometer data are still scarce.

[0006] To address the limitations of existing systems, the present system uses scatterometer data from India's Oceansat series of satellites, particularly Oceansat-2 and SCATSAT-1, to detect the onset of surface melt over sea ice. The present system detects the onset of snowmelt through significant reductions in backscatter values ​​during the summer melt season and integrates normalized backscatter coefficients with additional atmospheric and radiometric data. The approach also addresses challenges such as data gaps, sea ice motions, and surface flooding, enabling the creation of melt onset maps for the entire Antarctic. Interannual variability and sector-specific analyses provide valuable insights into the spatial and seasonal dynamics of sea ice melt onset, contributing to a deeper understanding of polar climate systems and their global impacts.

[0007] The technical advances disclosed by the present invention overcome the limitations and disadvantages of existing and conventional systems and methods. SUMMARY OF THE INVENTION

[0008] The present invention relates to a system for detecting the onset of melting at the surface of sea ice using scatterometer data from the Indian Oceansat satellites (Oceansat-2 and SCATSAT-1) for the period 2010-2019. The invention utilizes the decrease in microwave backscatter signatures (σ 0), caused by the presence of liquid water at the surface, and uses a novel threshold-based technique to map melt onset dates across Antarctica. The system integrates normalized backscatter coefficients with additional atmospheric and radiometric data and addresses challenges such as data gaps, sea ice motion, and surface flooding through mitigation strategies. The robustness of the system is validated against ECMWF meteorological data and passive radiometric microwave measurements, ensuring reliable detection of surface melt onset. The present invention represents a valuable tool for climate monitoring and polar research, providing improved accuracy and insights into critical thermodynamic processes that affect global climate systems.

[0009] An object of the present invention is to provide a system for mapping melt onset dates over Antarctic sea ice using microwave backscatter signatures.

[0010] Another objective of the present invention is to analyze and process normalized backscatter coefficients from scatterometers of the Oceansat series (Oceansat-2 and SCATSAT-1) for the period 2010-2019 to derive melt onset maps for the entire Antarctic.

[0011] Another object of the present invention is to exploit the principle that microwave backscattering decreases sharply in the presence of liquid water, which enables accurate detection of the onset of melting.

[0012] Another objective of the present invention is to validate the melt onset results using ECMWF meteorological datasets, including air temperature and heat fluxes at 2 m height, to ensure robustness and accuracy.

[0013] Another objective of the present invention is to investigate variations in Antarctic sea ice melting periods using a multi-decade dataset (2010-2019) to better understand regional and temporal melting dynamics.

[0014] Another aim of the present invention is to contribute to the understanding of thermodynamic processes in polar regions and to assess the impacts of climate change on Antarctic sea ice.

[0015] To provide a system for detecting the onset of surface melting of sea ice, the system comprising: a scatterometer configured to collect data on the backscattering coefficient (σ 0)at Ku-band frequency from dual-polarized scatterometers, including horizontal (HH) and vertical (VV) polarization, and configured to generate scattermeter image reconstruction (SIR) images for noise reduction; a passive microwave radiometer connected to the scattermeter and configured to collect sea ice concentration (SIC) and brightness temperatures (Tb19H, Tb37H) at different polarizations and frequencies to detect surface melt; a data interpolation unit connected to the scattermeter and the passive microwave radiometer and configured to fill data gaps in the scattermeter and radiometer data using multiple regression and median filtering;a processing unit connected to the data interpolation unit and configured to determine the onset of melt based on changes in the patterns of backscattering coefficients during the summer melt season; an atmospheric data unit configured to retrieve data on surface air temperature, latent and sensible surface heat flux, net shortwave and longwave radiation, and net heat flux from reanalysis data sources; a melt onset verification unit configured to confirm the onset of melt by analyzing the atmospheric data and comparing the derived SIC values ​​and brightness temperature variations with the pre-threshold values ​​of -10 K for the presence of liquid water in snowpacks;a motion tracking unit configured to determine sea ice motion by tracking features such as floes using sequential satellite imagery and weekly sea ice motion vectors; and a controller configured to process and synchronize inputs from the scatterometer, radiometer, and atmospheric data unit to detect and confirm the onset of melting at the surface at various spatial and temporal scales.

[0016] To further clarify the advantages and features of the present disclosure, a more detailed description of the invention will be given with reference to specific embodiments thereof, which are illustrated in the accompanying drawings. It should be noted that these drawings represent only typical embodiments of the invention and are therefore not to be considered limiting its scope. The invention will be described and explained in additional detail and in greater detail with reference to the accompanying drawings. SHORT DESCRIPTION OF THE FIGURE

[0017] These and other features, aspects, and advantages of the present disclosure will be better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. Where: Fig.1 shows a block diagram of a system for detecting the onset of surface melt on sea ice according to an embodiment of the present disclosure.

[0018] Furthermore, those skilled in the art will appreciate that elements in the drawings are shown for convenience and may not necessarily be drawn to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawing may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art who would benefit from the description herein. DETAILED DESCRIPTION:

[0019] To facilitate an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that no limitation on the scope of the invention is intended thereby, since such changes and further modifications of the illustrated system and such further applications of the principles of the invention as illustrated therein are contemplated as would normally occur to one skilled in the art to which the invention pertains.

[0020] It will be understood by one skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0021] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the phrase "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not all refer to the same embodiment.

[0022] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps not only includes those steps, but may also include other steps not expressly listed or inherent in such process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises..." does not preclude, without further limitation, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.

[0023] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0024] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0025] Fig. 1 shows a block diagram of a system (100) for detecting the onset of surface melt on Antarctic sea ice, the system (100) comprising: a scatterometer (102) configured to collect data of the backscattering coefficient (σ 0)at Ku-band frequency from dual-polarized scatterometers, including horizontal (HH) and vertical (VV) polarization, and configured to generate scatterometer image reconstruction (SIR) images for noise reduction; a passive microwave radiometer (104) connected to the scattermeter (102) and configured to detect sea ice concentration (SIC) and brightness temperatures (Tb19H, Tb37H) at different polarizations and frequencies for surface melt detection; a data interpolation unit (106) connected to the scattermeter (102) and the passive microwave radiometer (104) and configured to fill data gaps in the scattermeter and radiometer data using multiple regression and median filtering;a processing unit (108) connected to the data interpolation unit (106) and configured to determine the onset of melting based on changes in the patterns of backscattering coefficients during the summer melting season; an atmospheric data unit (110) configured to retrieve data on surface air temperature, latent and sensible surface heat flux, net shortwave and longwave radiation, and net heat flux from reanalysis data sources; a melt onset verification unit (112) configured to confirm the onset of melting by analyzing the atmospheric data and comparing the derived SIC values ​​and brightness temperature variations with the pre-threshold values ​​of -10 K for the presence of liquid water in a snowpack;a motion tracking unit (114) configured to determine sea ice motion by tracking features such as floes using sequential satellite imagery and weekly sea ice motion vectors; and a controller (116) configured to process and synchronize inputs from the scatterometer (102), radiometer (104), and atmospheric data unit (110) to detect and confirm the onset of surface melt across various spatial and temporal scales.

[0026] In one embodiment, the scatterometer (102) uses an egg-based SIR imaging technique, improving spatial resolution to approximately 8-10 km, thus improving image quality and analysis of temporal variations.

[0027] In one embodiment, the passive microwave radiometer (104) acquires sea ice concentration data using polarization differences at a frequency of 89 GHz, with the data provided in polar stereographic projection at resolutions of 3,125 km and 6.25 km.

[0028] In one embodiment, the data interpolation unit (106) identifies missing data in scattered light images and uses linear interpolation to restore data continuity across the time series.

[0029] In one embodiment, the melt onset verification unit (112) uses brightness temperature measurements (Tb19H and Tb37H) with a set threshold of -10 K and confirms melt onset when liquid water is detected within the snowpack.

[0030] In one embodiment, the atmosphere data unit (110) processes heat flow components and net surface heat budget data at hourly intervals, applying a 7-day moving average to capture trends relevant to surface melting.

[0031] In one embodiment, the movement tracking unit (114) calculates sea ice movement vectors with a resolution of 25 km and averages daily movements over weekly periods to identify shifts in floes and sea ice extent.

[0032] In one embodiment, the scatterometer (102) is configured to operate with scatterometer data from Oceansat-2, SCATSAT-1, and Oceansat-3, enabling enhanced monitoring and adaptability of the scatterometer data.

[0033] The proposed system utilizes data from multiple sources to analyze surface melting and sea ice dynamics. Scatterometer data from OSCAT-1 and OSCAT-2 onboard the Oceansat-2 and SCATSAT-1 satellites, operating at the Ku-band frequency with HH and VV polarization, are used for their all-weather remote sensing capability. Enhanced ovoid images with a resolution of approximately 8–10 km are generated using the Scatterometer Image Reconstruction (SIR) technique, using Level 1B products calibrated for high accuracy. Data gaps are minimized by linear interpolation and median filtering, with further processing applied to identify the onset of melting during the summer melt season from 2010 to 2019.

[0034] To address potential errors such as sea ice advection and surface flooding, passive microwave datasets are incorporated. Sea ice concentration (SIC) is derived from polarization differences in brightness temperatures at a frequency of 89 GHz and processed with high spatial resolution. Weekly sea ice motion vectors and daily gridded brightness temperatures are used to analyze sea ice motion and confirm the onset of melting. The study uses threshold-based differences in brightness temperatures to detect the presence of liquid water in the snowpack.

[0035] Atmospheric data from ECMWF Reanalysis Version 5 (ERA5) provide surface air temperature and heat flux components, processed at a resolution of 0.25°×0.25° with a 7-day moving average. Key heat flux components, including latent and sensible heat fluxes, shortwave solar radiation, and longwave thermal radiation, are analyzed to understand their role in the heat budget. All heat flux values ​​are expressed in W / m². 2 , with the net heat flux calculated as the sum of these components, with a positive value indicating a net downwelling flow. This comprehensive dataset integration enables a detailed understanding of surface melt dynamics and sea ice processes.

[0036] In one embodiment, melt onset detection using OSCAT data focuses on the sensitivity of Ku-band frequencies to surface and subsurface variations, effectively capturing melt signatures under sea ice conditions. By monitoring fluctuations in backscatter intensity, it is possible to determine the onset of summer melt and the duration of the melt season. Sea ice and snow, as complex mixtures with distinct dielectric properties, interact with electromagnetic waves through reflection, refraction, and scattering, with dry snow being largely transparent to Ku-band radiation. Wet snow with increased liquid water content significantly alters dielectric properties, reducing microwave penetration and subsurface scattering, resulting in a reduced backscatter coefficient during melt onset.

[0037] A sliding time window technique is used, analyzing seven consecutive days of horizontally polarized backscatter coefficients. A decrease in backscatter during this period, particularly a threshold of 3.0 dB between the first and last day, indicates the onset of melting. Additional thresholds, such as daily differences above 1.0 dB and increased standard deviation values ​​on the first day, improve detection accuracy. Polarization ratios between horizontally and vertically polarized backscatter further help distinguish sea ice from open water. Error mitigation measures, such as filtering pixels with excessive ice movement, wind-related effects, and low ice concentration, improve detection reliability.

[0038] For example, one case study identifies a melt onset on December 8, 2010, which was confirmed by synthetic aperture radar images showing melt events in the region. Techniques such as masking pixels with excessive ice drift or concentrations below 70% account for noise caused by wind-blown regions and deteriorating ice conditions, ensuring accurate melt detection. This comprehensive approach captures melt dynamics while accounting for variability and potential errors in the dataset.

[0039] In one embodiment, the scatterometer-derived melt onset interface is applied to OSCAT datasets from October 1, 2010, to March 31, 2011, and determines melt onset dates for the 2010-2011 austral summer season. These data are validated against atmospheric conditions, including air temperature and 2-m heat fluxes, to assess their consistency. A false-color composite of the OSCAT scatterometer-σ0 - Images highlight the periods before and after the onset of melting. Regions with σ0 - Variations above 10 dB are identified, particularly in peripheral marginal ice zones and the Ross Sea Polynya.

[0040] The pattern of melt onset shows a latitudinal gradient, with melting occurring earlier in the marginal ice zones and delayed inward toward the continent. Key regions such as the Weddell Sea, the Bellingshausen Sea, and the Western Pacific show sparse melting, while certain sectors are unaffected or excluded from the interface. Validation with ECMWF atmospheric data resampled to a 25×25 km polar stereographic grid confirms the accuracy.

[0041] For certain analyzed pixels, the onset of melting is associated with rising air temperatures, increasing heat fluxes, and decreasing brightness temperature differences (Tb19H - Tb37H). In one region of the Weddell Sea, melting began on December 8, 2010, with the net heat flux reaching a peak of ~180 W / m 2and the air temperature approached 0 °C. A seasonal sea ice region in the Indian Ocean shows the onset of melting on 18 December 2010, with a backscatter decrease of ~17 dB and a net heat flux of over 200 W / m 2 In another region of the Ross Sea, melting began on December 13, 2010, with a significant decrease in brightness temperature and confirmation from MODIS images showing deteriorating ice conditions. In a region of the Amundsen Sea, where melting began on December 18, 2010, a two-week trend of increasing heat fluxes and air temperatures is observed, with ice-free conditions beginning in mid-February 2011.

[0042] A consistent inverse correlation is observed between backscatter values ​​and atmospheric parameters such as air temperature and heat flux, albeit weak (R = -0.26). This study does not consider ocean heat flux, which is considered another factor contributing to subsurface sea ice melting.

[0043] In one embodiment, the spatial and temporal distribution of melt onset dates in Antarctica shows significant regional variation. Most regions experience an early melt onset around the second week of October with a standard deviation of 13 days, based on observations from six seasonal sea ice locations. Some areas, such as the northwestern part of the Weddell Sea (NWWS), the Bellingshausen Sea, the Somov Sea, the D'Urville Sea, and the Kosmonaut and Riiser-Larsen Seas, have later melt onset dates. The NWWS typically sees melt onset in mid-November, while the Bellingshausen Sea and the Somov Sea have average onset dates around late November. East Antarctic coastal regions, including the Kosmonaut and Riiser-Larsen Seas, have even later onset dates, with some areas beginning to melt in early to mid-December, alongside longer periods of melt.

[0044] The Southern Ocean sectors exhibit notable latitudinal gradients in melt onset. In the Indian Ocean sector, for example, melting begins earlier at lower latitudes (December 11 for 55-60°S) than at higher latitudes (December 26 beyond 65°S). By December, over half of the pixels in this sector show signs of melting. In the Western Pacific sector, melting predominantly occurs until December, with over 63% of pixels showing melt signatures.

[0045] The Ross Sea and the Bellingshausen and Amundsen Seas exhibit minimal latitudinal variation, with melt observed in over half of all pixels by December. The Weddell Sea exhibits the greatest latitudinal variability, with melting occurring from late November at lower latitudes to late December at higher latitudes, and the highest proportion of melted pixels (~73%) by December.

[0046] Table 1 below shows that interannual trends in melt onset for these sectors are generally small and not statistically significant. Analysis of spatiotemporal variability reveals an earlier melt onset in areas near the Antarctic Peninsula, the northern Weddell Sea, the coast of the Bellingshausen and Amundsen Seas, and parts of the western Pacific. In contrast, regions such as the Cosmonaut Sea show delayed onset dates, with some areas exhibiting statistically significant trends influenced by factors such as ocean heat flow and atmospheric circulations: Industries Average MO (mean ±1 SD) trend Statistical significance Indian Ocean (20° E → 90 ° E) Dec 28±25 days 0.04 NO Western Pacific (90°E → 160°E) 21 Dec.±27 days -0.4 NO Ross Sea (160°0 → 130°W) Dec 26±25 days -0.7 NO Bellingshausen and Amundsen Seas (130°W → 60°W) Dec 23±23 days -0.37 NO Weddell Sea (60°W → 20°E) 15 Dec.±29 days 1.16 NO

[0047] The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Furthermore, the actions of any flowchart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and use of materials, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0048] Advantages, other benefits, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, benefits, solutions to problems, and any components that may cause an advantage or solution to occur or become more apparent are not to be construed as a critical, required, or essential feature or component of any or all of the claims. REFERENCES 100 A system for detecting the onset of surface melt on Antarctic sea ice. 102 scattered light meter 104 Passive microwave radiometer 106 data interpolation unit 108 processing unit 110 Atmospheric Data Unit 112 The beginning of melting 114 Motion tracking unit 116 controllers

Claims

[1] A system (100) for detecting the onset of surface melt on Antarctic sea ice, the system (100) comprising: a scatterometer (102) adapted to acquire data of the backscatter coefficient (σ0) at Ku-band frequency from dual-polarized scatterometers, including horizontal (HH) and vertical (VV) polarization, and configured to generate scatterometer image reconstruction (SIR) images for noise reduction; a passive microwave radiometer (104) connected to the scatterometer (102) and configured to detect sea ice concentration (SIC) and brightness temperatures (Tb19H, Tb37H) at different polarizations and frequencies for detecting surface melt; a data interpolation unit (106) connected to the scatterometer (102) and the passive microwave radiometer (104) and configured to fill data gaps in the scatterometer and radiometer data using multiple regression and median filtering; a processing unit (108) connected to the data interpolation unit (106) and configured to determine the onset of melting based on changes in the backscatter coefficient patterns during the summer melt season; an atmospheric data unit (110) configured to retrieve surface air temperature, surface latent and sensible heat flux, net shortwave and longwave radiation, and net heat flux data from reanalysis data sources; a melt onset verification unit (112) configured to confirm the melt onset by analyzing the atmospheric data and comparing the derived SIC values and brightness temperature variations with the pre-threshold values of -10 K for the presence of liquid water in the snowpack; a motion tracking unit (114) configured to determine sea ice motion by tracking features such as floes using successive satellite images and weekly sea ice motion vectors; and a controller (116) configured to process and synchronize inputs from the scatterometer (102), radiometer (104), and atmospheric data unit (110) to detect and confirm the onset of surface melting over various spatial and temporal scales. [2] The system (100) of claim 1, wherein the scatterometer (102) uses an egg-shaped SIR imaging technique that improves spatial resolution to approximately 8-10 km, thus improving image quality and analysis of temporal variations. [3] The system (100) of claim 1, wherein the passive microwave radiometer (104) measures sea ice concentration data using polarization differences at a frequency of 89 GHz, the data being provided in polar stereographic projection at resolutions of 3.125 km and 6.25 km. [4] The system (100) of claim 1, wherein the data interpolation unit (106) identifies missing data in scattered light images and uses linear interpolation to restore data continuity across the time series. [5] The system (100) of claim 1, wherein the melting onset verification unit (112) uses brightness temperature measurements (Tb19H and Tb37H) with a set threshold of -10 K and confirms melting onset when liquid water is detected in the snowpack. [6] The system (100) of claim 1, wherein the atmosphere data unit (110) processes heat flow components and net surface heat balance data at hourly intervals, applying a 7-day moving average to detect trends relevant to surface melting. [7] The system (100) of claim 1, wherein the motion tracking unit (114) calculates sea ice motion vectors with a resolution of 25 km and averages the daily movements over weekly periods to identify shifts in the floes and sea ice extent. [8] The system (100) of claim 1, wherein the scattermeter (102) is configured to operate with scattermeter data from Oceansat-2, SCATSAT-1, and Oceansat-3, thereby enabling enhanced monitoring and adaptability of the scattermeter data.

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