The Surface Water and Ocean Topography (SWOT) mission, launched in December 2022, aims to address the crucial environmental goal of water monitoring to support preparedness for extreme events and facilitate adaptation to climate change on global and local scales. This mission will provide a comprehensive inventory of worldwide water resources, lakes, reservoir storage, and river dynamics. In this work, we carried out a preliminary assessment of SWOT’s Lake product Level 2 version 1.1, also known as “L2_HR_LakeSP”. The analysis was performed across six diverse lakes on three continents, revealing an average median bias of 0.08 m with respect to the considered reference, after suitable outlier removal. An overall precision of 0.22 m was found, combined with an average correlation of 68% between SWOT and reference time series. Moreover, the accuracy varied in the considered six lakes, since biases up to some decimeters were found for some of them; they could be due to residual inconsistencies between the vertical reference frame of SWOT and that of the considered reference. In summary, the first analysis of the “L2_HR_LakeSP” product, Version 1.1, demonstrated the promising potential of SWOT for monitoring seasonal variations in water levels. Nevertheless, notable anomalies were found in the water masks, particularly in higher latitudes, suggesting potential difficulties in accurately delineating water bodies in those regions. Additionally, a discernible reduction in accuracy was observed towards the end of the monitoring period. These preliminary findings indicate some issues that should be addressed in future investigations about the quality and potential of SWOT’s lake products for advancing our understanding of global water dynamics.

Freshwater is one the most important renewable water resources of the planet but, due to climate change, surface freshwater available in the form of lakes, rivers, reservoirs, snow, and glaciers is becoming significantly threatened. As a result, surface water level monitoring is fundamental for understanding climatic changes and their impact on humans and biodiversity.This study evaluates the accuracy of the Global Ecosystem Dynamics Investigation (GEDI) LiDAR (Light Detection And Ranging) instrument for monitoring inland water levels. Four lakes in northern Italy were selected for comparison with gauge station measurements. To evaluate the accuracy of GEDI altimetric data, two steps of outlier removal are proposed. The first stage employs GEDI metadata to filter out footprints with very low accuracy. Then, a robust version of the standard 3σ test using a 3NMAD (Normalized Median Absolute Deviation) test is iteratively applied.After the outlier removal, which led to the elimination of between 80% to 87% of the data, the remaining footprints show an average standard deviation of 0.36 m, a mean NMAD of 0.38 m, and a Root Mean Square Error (RMSE) of 0.44 m, proving the promising potentialities of GEDI L2A altimetric data for inland water monitoring.

Inland surface water is the source of about 60% and a key component of the hydrological cycle. The monitoring of inland surface water is fundamental to understanding the effects of climate change on this key resource and preventing water stresses. Water levels traditionally measured by ground instruments like gauge stations are expensive and have high maintenance costs. Conversely, Earth Observation technologies can nowadays collect frequent and regular data with continuous monitoring of water reservoirs, reducing monitoring costs. With the availability of new data, the need for a capable computation tool is crucial. Google Earth Engine (GEE), a cloud-based computation platform capable of integrating a high variety of datasets with powerful analysis tools [1], has recently added the Global Ecosystem Dynamics Investigation (GEDI) [4] to its wide archive. The GEDI [2] instrument, hosted onboard the International Space Station, is a geodetic-class, light detection and ranging (LiDAR) system, having a 25 m spot (footprint) on the surface over which 3D structure is measured. The footprints are separated by 60 m along-track, with an across-track distance of about 600 m. The measurements are made over the Earth's surface nominally between the latitudes of 51.6° and -51.6°. GEDI was originally developed to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The available literature highlights that the quality of GEDI data is variable and impacted by several factors (e.g., latitude, orbit). Our preliminary analysis is focused on the accuracy assessment of the GEDI data, at first addressing the problem of outliers detection and removal, and secondly comparing the water levels measured by GEDI with reference ground truth; thus, we considered four lakes in Northern Italy for which level measurements from gauge stations are available.The proposed outlier detection consists of two steps for each GEDI passage over water surfaces.The first step is based on two flags implanted within GEDI bands. Specifically, the “quality_flag” indicates if the considered footprint has valid waveforms (1=valid, 0=invalid), due to anomalies in the energy, sensitivity, and amplitude of signals; the “degrade_flag” indicates the degraded state of pointing (saturation intensity of returned photons might reduce the accuracy of measurements) and/or positioning information (GPS data gap, GPS receiver clock drift).The second step relies on the robust version of the standard 3σ test, implemented considering the NMAD (Normalized Median Absolute Deviation): every GEDI measurement not within -/+3*NMAD from the median is removed as outlier.To assess the outlier detection procedure and to preliminarily evaluate the accuracy of the GEDI data, we compared the water levels inferred from the median of GEDI measurements after outlier removal with the contemporary water levels from hydrometric stations at four major lakes (Como, Garda, Iseo, Maggiore) in Northern Italy [3]. The comparison is ongoing over the period from GEDI activation until June 2022, for 3 years.References[1] Cardille, et al., 2022. Cloud-Based Remote Sensing with Google Earth Engine.[2] Dubayah, et al., 2021. GEDI L3 gridded land surface metrics, version 1[3] Enti Regolatori dei Grandi Laghi, 2022. Home Page - Laghi. www.laghi.net.[4] University of Maryland, 2022. GEDI ecosystem lidar

Climate change has notably altered the elevation of mountain glaciers, particularly in alpine regions. Alpine glaciers play a pivotal role not only as indicators of climate change but also as crucial elements for human and wildlife well-being, regulating freshwater supply and providing vital habitats in Europe. Consequently, continuous monitoring of these glaciers offers valuable insights into their changing structure and surface dynamics [1]. While Unmanned Aerial Vehicles (UAV) offer the most precise method for tracking glacier surface changes, their practicality is often hindered by cost limitations and challenging in-situ measurements in extreme weather or remote areas. Therefore, remote sensing and satellite altimetry emerge as a feasible alternative in such scenarios. Numerous LiDAR and RADAR altimetry sensors, such as Jason-2 and 3, CryoSat, and ICESat-1 and 2, have been employed. However, the Global Ecosystem Dynamics Investigation (GEDI), a reliable source of altimetry data, has been overlooked due to its restricted latitude range of 51.6 and -51.6 [2]. GEDI has proven its efficacy in measuring forest and canopy top height, monitoring lakes and water resources and generating Digital Surface Models (DSM). Google Earth Engine (GEE), a cloud-based platform renowned for its ability to integrate diverse datasets and potent analytical tools, has recently incorporated GEDI into its extensive repository [3].Our initial analysis aims to assess the accuracy of GEDI data for glacier monitoring. Firstly, we focus on detecting and eliminating outliers. Secondly, we compare the glacier levels obtained from GEDI with reference ground truth. Thus, we've chosen the Rutor and Belvedere glaciers in Northern Italy, where we have access to reference-level measurements from UAV DEMs. The proposed outlier detection consists of two steps for each GEDI passage over the glacier surface.The first step relies on quality surface flags available within GEDI bands, In the subsequent phase, the outlier removal process was refined by employing the x-means algorithm, an unsupervised classifier available within GEE. This approach facilitated the identification and elimination of outliers within the GEDI data set, contributing to refining the dataset's accuracy for comparative analysis with the reference ground truth.After the above-mentioned outlier removals, we obtained a median difference of -0.27m and NMAD of 4.9 m for Rutor Glacier in 2021 from more than 500 footprints, whereas for Belvedere a median difference of -0.43 and NMAD of 3.7m were obtained. These underestimated values might be due to the nearly 2-month difference between the DEM and the GEDI acquisitions. [1] Belloni, V., et al. (2023). High-resolution high-accuracy orthophoto map and digital surface model of Forni Glacier tongue (Central Italian Alps) from UAV photogrammetry. Journal of Maps, 19(1), 2217508.[2] Hamoudzadeh, A., et al.: Gedi Data Within Google Earth Engine: Potentials And Analysis For Inland Surface Water Monitoring, EGU General Assembly 2023, Vienna, Austria, EGU23-15083 [3] Hamoudzadeh, A., et al. (2023). GEDI data within google earth engine: preliminary analysis of a resource for inland surface water monitoring. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

Glaciers are vital components of Alpine ecosystems and are increasingly threatened by climate change. Therefore monitoring glacier elevation change over time is an essential task and aids in modeling future freshwater availability. By leveraging remote sensing technologies with high revisit frequencies, we can gain a comprehensive understanding of glacier dynamics, including retreat rates, the influence of landslides, and overall glacier health.Unmanned Aerial Vehicles (UAVs) provide the most precise means of tracking glacier surface changes, however, their use is often constrained by high costs and the difficulty of conducting in-situ measurements in extreme weather or remote locations. In these cases, remote sensing and satellite altimetry offer a practical and viable alternative.In this study, we present a novel methodology utilizing Global Ecosystem Dynamics Investigation (GEDI) altimetry data. GEDI is a LiDAR (Light Detection and Ranging) sensor collecting altimetric data with a 25 m footprint size and 60 m along-track spacing from the International Space Station [1,2]. GEDI was active from early 2019 till 2023 when it was temporarily hibernated and has recently been reactivated.The proposed method relies exclusively on available GEDI bands and is fully implemented within Google Earth Engine (GEE). We have applied the methodology to three Alpine glaciers using nine GEDI acquisitions and evaluated its performance through comparisons with reference Digital Surface Models (DSMs) generated from aerial and drone photogrammetry and LiDAR data.After applying outlier detection techniques solely based on GEDI bands, GEDI-derived glacier profiles along the tracks provided valuable surface elevation information. The results showed a strong correlation (r = 0.99) with reference DSMs along with low dispersion and R2 of 0.99, based on an average of 135 GEDI footprints per glacier. Additionally, the analysis indicated that GEDI could capture seasonal variations in glacier surfaces, detecting the melt and gain in the snowpack.While GEDI lacks the capability to map an entire glacier extent as photogrammetric block imagery does, its higher acquisition rate, including coverage of smaller glaciers, offers a significant advantage. Integrating GEDI with traditional approaches thus enables more continuous and comprehensive glacier monitoring.References:[1] Hamoudzadeh, A., Ravanelli, R., and Crespi, M.: Glacier Monitoring Using GEDI Data in Google Earth Engine: Outlier Removal and Accuracy Assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10176, https://doi.org/10.5194/egusphere-egu24-10176, 2024.[2] Hamoudzadeh, A., Ravanelli, R., and Crespi, M.: GEDI DATA WITHIN GOOGLE EARTH ENGINE: PRELIMINARY ANALYSIS OF A RESOURCE FOR INLAND SURFACE WATER MONITORING, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 131–136, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-131-2023, 2023.