Use of Surface Water and Ocean Topography (SWOT) observations to support Land Use/Land Cover (LULC) change products: the case of the pacific coast of Ecuador

Radar altimetry has been used to characterise land surfaces. However, the nadir configuration of the radar altimeter sensor and its coarse spatial resolution were limiting factor. The Surface Water and Ocean Topography (SWOT) mission overcomes these limitations through its Ka-band Radar Interferometer (KaRIn), a bistatic synthetic aperture radar (SAR) system, providing high spatial resolution and accurate surface height measurements. Initially used for hydrology and oceanography, this study explores an innovative use of SWOT to analyse changes in Land Use/Land Cover (LULC). To do this, three study areas located on the Pacific Coast of Ecuador were considered. The area in the south (A) is characteristic of cultivated areas, while the area in the center (B) presents a landscape mosaic and the area in the north (C) hosts tropical rainforests. For each study area, the SWOT backscatter coefficient (sig0) was analysed for the year 2024 from the raster product at 100m spatial resolution. We calculated the number of occurrences and the sig0 average from the raster product over each pixel. The spatial patterns obtained from these two variables enabled us to assign a LULC class (city, water, road, crop, or no forest, depending on the study area) to each pixel, using a Support Vector Machine (SVM). The assigned LULC classes depend on the partial spatial coverage of the SWOT data, which does not allow representing all the LULC classes. The classification results were compared with the LULC map provided by the Ministry of the Environment using a confusion matrix and obtained an accuracy greater than 0.87 and an F1 score greater than 0.89 for the three study areas. In the forest area (C), the SWOT observations were also compared to two change detection products: RAdar for Detecting Deforestation (RADD) alerts and detections by the Cumulative Sum (CuSum) method. 39% of the SWOT observations were in areas identified as forest by these products but classified as no forest or water in our SWOT classification. By detecting small streams (areas A, B and C), roads (area A), the boundaries of agricultural plots and the state of cultivated land (area A) as well as recent forms of deforestation (zone C), SWOT was found to be a complementary source of information for LULC change products. • Innovative application of SWOT for LULC. • SWOT provides complementary information to the change detection products. • SWOT can detect small rivers, roads, agricultural boundaries and deforestation.