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Sun-induced chlorophyll fluorescence (SIF) has proven to be advantageous in estimating gross primary production, despite the lack of a stable relationship. Satellite-based SIF measurements at Level 2 offer comprehensive global coverage and are available in near real time. However, these measurements are often limited by spatial and temporal sparsity, as well as discontinuities. These limitations primarily arise from incomplete satellite trajectories. Additionally, variability in cloud cover and periodic issues specific to the instruments can compromise data quality. Two families of methods have been developed to address data discontinuity: (1) machine learning-based gap-filling techniques and (2) geostatistical techniques (various forms of kriging). The former techniques utilize the relationships between ancillary data and SIF, while the latter usually rely on the available SIF data recordings and their covariance structure to provide estimates at unsampled locations. In this study, we create a synthetic approach for SIF gap filling by hybridizing the two approaches under the umbrella of kriging with external drift. We performed leave-one-out cross-validation of the OCO-2 SIF retrieval aggregates for the entire year of 2019, comparing three methods: ordinary kriging, ML-based estimation using ancillary data, and kriging with external drift. The Mean Absolute Error (MAE) for ML, ordinary kriging, and the hybrid approach was found to be 0.1399, 0.1318, and 0.1183 mW m2 sr−1 nm−1, respectively. We demonstrate that the performance of the hybrid approach exceeds both parent techniques due to the incorporation of information from multiple resources. This use of multiple datasets enriches the hybrid model, making it more robust and accurate in handling the spatio-temporal variability and discontinuity of SIF data. The developed framework is portable and can be applied to SIF retrievals at various resolutions and from various sources (satellites), as well as extended to other satellite-measured variables.

Optical satellite imaging for surface water mapping often encounters significant challenges owing to persistent spatial data gaps caused by clouds, shadows, and sensor errors. This study presents a novel Stepwise Gap-Filling (SGF) method, designed to enhance the monthly surface water mapping and monitoring. The SGF method leverages temporal similarities and spatial correlations to reconstruct gap pixels originally classified as invalid observations. We validated this approach against historical high-resolution Google Earth images from 2887 sample points in the Siling Co Basin of the Tibetan Plateau. The results demonstrated substantial improvements in mapping accuracy, achieving an overall accuracy of 98.93%, a producer’ accuracy of 98.59%, and a user’ accuracy of 99.11%, markedly reducing the uncertainties in the original dataset. Importantly, the SGF method offers detailed insights into monthly surface water dynamics, which are closely aligned with annual trends. This study highlights the effectiveness of the SGF method for filling data gaps and its potential for widespread application in the monitoring and management of global water resources.

Sea surface salinity (SSS) provides crucial information about ocean environments, influencing global hydrological cycles, thermohaline circulation, and climate change. Although L-band passive microwave radiometers have provided satellite-based SSS data, there are gaps due to the limited daily coverage of the sensors. This study proposes a U-Net-based spatial gap-filling model for global SSS using Soil Moisture Active Passive (SMAP) satellite data. The proposed model utilizes SSS swath data from the target and past days to generate daily global SSS maps with full coverage, incorporating only past temporal information. Additionally, bias-corrected data using gradient-boosted regression trees (GBRT) are employed to reduce inherent errors in the SMAP SSS data. We designed 24 schemes using data from the past 3, 5, and 7 days for both GBRT-corrected and original SMAP SSS data, with one to four times oversampling for low-salinity water, where the number of samples is significantly small. Validation results using masked-out pixels indicate that the gap-filling models with GBRT-corrected SSS data from the past 3 and 5 days and four times oversampling yielded the best performance, with root mean square errors (RMSEs) of 0.388 and 0.413 psu, respectively. Compared with the in situ Argo data for 2020, the RMSEs were 0.237 and 0.241 psu for the two models, respectively, significantly outperforming the SMAP Level 3 8-day SSS, which requires future data (RMSE of 0.456 psu). Notably, these models successfully filled gaps over coastal areas where low SSS (

Study area: The Manas River Basin (84°01′−86°32′E, 42°27′−45°21′N) on the northern slopes of the Tianshan Mountains and the Kaidu River Basin (82°18′−85°57′E, 40°54′−44°30′N) on the southern slopes. Study focus: Traditional gap filling models struggle to accurately simulate daily streamflow in data-scarce regions. The underutilize of multi-source data leads to insufficient simulation ability for extremely high values of streamflow. Currently, a quantitative relationship between daily streamflow and hydro-meteorological factors has not been systematically established, despite the potential for improving accuracy of hydrological models through such relationships. To address this, a Denoising Diffusion Probabilistic Model were employed to investigate the driving factors of daily streamflow data. This study tested and compared the gap filling efficiency of mainstream models in the Tianshan Mountains region and provided a quantitative gap filling model for data-scarce areas. New hydrological insights for the region: This study has revealed a strong correlation between the missing rate and the efficiency of gap filling models. It is revealed that the DDPM model accurately simulates streamflow in the climatic conditions of the region and outperformed other models in terms of all missing rates, which holds significant implications for gaining a deeper understanding of the water balance process and enhancing hydrological and meteorological simulations in data-scarce regions.

Satellite-derived Chlorophyll-a concentration (Chla) time series products are essential for large-scale marine environmental monitoring. However, the plenty of missing pixels in current satellite Chla products severely hinder their applications for marine research, due to cloud contamination, solar glint, and unfavorable observation conditions. This study proposed a Chla time series gap-filling method for MODIS 8-day composite Chla product by integrating spatiotemporal information (STGF). This method employed spatially neighboring pixels with similar temporal variation to fill the missing values in time series, without involving training or auxiliary data. The performance of the STGF is assessed quantitatively and qualitatively. The correlation coefficients (CC) between gap-filled data and actual observations for years of 2004, 2010, 2016, and 2022 across the entire study area are all greater than 0.97. The mean absolute percentage error (MAPE) and root mean square error (RMSE) are less than 16.1 % and 0.233 mg/m3, respectively. The proposed STGF outperformed linear interpolation and the DINEOF algorithm from both spatial and temporal perspectives, suggesting the effectiveness of STGF in handling continuous data gaps and capturing detailed Chla variation patterns, especially in regions with significant variability. The findings suggest that the proposed STGF method offers a viable alternative for filling missing values in Chla time series data. This supports the demand for long-term, large-scale, and high-coverage ocean color remote sensing data in marine environmental studies.

The spatial and temporal resolutions of contemporary microwave-based sea surface salinity (SSS) measurements are insufficient. Thus, we developed a gap-free gridded daily SSS product with higher spatial and temporal resolutions, which can provide information on short-term variability in the East China Sea (ECS), such as the front changes by Changjiang diluted water (CDW). Specifically, we conducted gap-filling for daily SSS products based on the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 1 km (0.01°), using a machine learning approach during the summer seasons from 2015 to 2019. The comparison of the Soil Moisture Active Passive (SMAP), Copernicus Marine Environment Monitoring Service (CMEMS), and Hybrid Coordinate Ocean Model (HYCOM) SSS products with the GOCI-derived SSS over the entire SSS range showed that the SMAP SSS was highly consistent, whereas the HYCOM SSS was the least consistent. In the < 31 psu range, the SMAP SSS was still the most consistent with the GOCI-derived SSS (R2=0.46; root mean squared error: RMSE = 2.41 psu); in the > 31 psu range, the CMEMS and HYCOM SSS products showed similar levels of agreement with that of the SMAP SSS. We trained and tested three machine learning models – the fine trees, boosted trees, and bagged trees models – using the daily GOCI-derived SSS as output, including the three SSS products, environmental variables, and geographical data. We combined the three SSS products to construct input datasets for machine learning. Using the test dataset, the bagged trees model showed the best results (mean R2=0.98 and RMSE = 1.31 psu), and the models that used the SMAP SSS as input had the highest level. For the dataset in the > 31 psu range, all the models exhibited similarly reasonable performances (RMSE = 1.25–1.35 psu). The comparison with in situ SSS data, time series analysis, and the spatial SSS distribution derived from models showed that all the models had proper CDW distributions with reasonable RMSE levels (0.91–1.56 psu). In addition, the CDW front derived from the model gap-free daily SSS product clearly demonstrated the daily oceanic mechanism during the summer season in the ECS at a detailed spatial scale. Notably, the CDW front in the zonal direction, as captured by the Ieodo Ocean Research Station (I-ORS), moved approximately 3.04 km d−1 in 2016, which is very fast compared with the cases in other years. Our model yielded a gap-free gridded daily SSS product with reasonable accuracy and enabled the successful recognition of daily SSS fronts at the 1 km level, which was previously not possible with ocean color data. Such successful application of machine learning models can further provide useful information on the long-term variation of daily SSS in the ECS. The gridded gap-free SSS dataset at 0.01°×0.01° spatial resolution is freely available at https://doi.org/10.22808/DATA-2023-2 (Shin et al., 2023).

Drought is a devastating natural disaster, during which water shortage often manifests itself in the health of vegetation. Unfortunately, it is difficult to obtain high-resolution vegetation drought impact information that is spatially and temporally consistent. While remotely sensed products can provide part of this information, they often suffer from data gaps and limitations with respect to their spatial or temporal resolution. A persistent feature among remote-sensing products is the trade-off between the spatial resolution and revisit time: high temporal resolution is met with coarse spatial resolution and vice versa. Machine learning methods have been successfully applied in a wide range of remote-sensing and hydrological studies. However, global applications to resolve drought impacts on vegetation dynamics still need to be made available, as there is significant potential for such a product to aid with improved drought impact monitoring. To this end, this study predicted global vegetation dynamics based on the enhanced vegetation index (evi) and the popular Random forest (RF) regressor algorithm at 0.1°. We assessed the applicability of RF as a gap-filling and downscaling tool to generate global evi estimates that are spatially and temporally consistent. To do this, we trained an RF regressor with 0.1° evi data, using a host of features indicative of the water and energy balances experienced by vegetation, and evaluated the performance of this new product. Next, to test whether the RF is robust in terms of spatial resolution, we downscale the global evi: the model trained on 0.1° data is used to predict evi at a 0.01° resolution. The results show that the RF can capture global evi dynamics at both a 0.1° resolution (RMSE: 0.02–0.4) and at a finer 0.01° resolution (RMSE: 0.04–0.6). Overall errors were higher in the downscaled 0.01° product compared with the 0.1° product. Nevertheless, relative increases remained small, demonstrating that RF can be used to create downscaled and temporally consistent evi products. Additional error analysis revealed that errors vary spatiotemporally, with underrepresented land cover types and periods of extreme vegetation conditions having the highest errors. Finally, this model is used to produce global, spatially continuous evi products at both a 0.1 and 0.01° spatial resolution for 2003–2013 at an 8 d frequency.

High-frequency radars (HFRs) provide remote information on ocean surface velocity in extended coastal areas at high resolutions in space (O(km)) and time (O(h)). They directly produce radial velocities (in the radar antenna’s direction) combined to provide total vector velocities in areas covered by at least two radars. HFRs are a key element in ocean observing systems, with several important environmental applications. Here, we provide an assessment of the HFR-TirLig network in the NW Mediterranean Sea, including results from the gap-filling open-boundary modal analysis (OMA) using in situ velocity data from drifters. While the network consists of three radars, only two were active during the assessment experiment, so the test also includes an area where the radial velocities from only one radar system were available. The results, including several metrics, both Eulerian and Lagrangian, and configurations, show that the network performance is very satisfactory and compares well with the previous results in the literature in terms of both the radial and total combined vector velocities where the coverage is adequate, i.e., in the area sampled by two radars. Regarding the OMA results, not only do they perform equally well in the area sampled by the two radars but they also provide results in the area covered by one radar only. Even though obviously deteriorated with respect to the case of adequate coverage, the OMA results can still provide information regarding the velocity structure and speed as well as virtual trajectories, which can be of some use in practical applications. A general discussion on the implications of the results for the potential of remote sensing velocity estimation in terms of HFR network configurations and complementing gap-filling analysis is provided.

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).

Abstract The fracture of a bronze object is usually accompanied by localized deformation. The deformed part often tends to spring back at an unpredictable moment after its shape is corrected. The reconstruction of the area adjacent to the damage should be sufficiently resistant to the stress derived from springback so that the whole structure can remain stable. However, the choice of gap-filling materials for bronze objects is typically based on personal experience, and there are few available studies on their pertinent properties. The present study focused on a typical situation of deformation, took the flexural modulus as a crucial parameter and explored the development of a protocol for selecting a gap-filling material for bronze restoration in terms of its mechanical property. The flexural modulus was measured for the main classes of materials in use, and the factors related to preparation that influenced performance were studied. Among the various materials tested, carbon fiber reinforced (CFR) materials exhibited an advantageous value of the modulus and notable operational convenience. Further tests were conducted to assess their chemical safety for application in a museum context, as well as their surface workability. After obtaining overall encouraging results, composites reinforced with CF cloth and chopped CF were applied to the reconstruction of different parts of an archaeological bronze vessel. Indications for future improvement in the experimental setup and some practical notes regarding the use of CFR materials were also provided in this paper.

The dynamic development of the entertainment market entails the need to develop new methods enabling the application of current scientific achievements. Motion capture is one of the cutting-edge technologies that plays a key role in movement and trajectory computer mapping. The use of optical systems allows one to obtain highly precise motion data that is often applied in computer animations. This study aimed to define the research methodology proposed to analyze the movement of remotely controlled cars utilizing developed gap filling algorithm, a part of post-processing, for creating realistic vehicle animation. On a specially prepared model, six various types of movements were recorded, such as: driving straight line forward, driving straight line backwards, driving on a curve to the left, driving on a curve to the right and driving around a roundabout on both sides. These movements were recorded using a VICON passive motion capture system. As a result, three-dimensional models of vehicles were created that were further post-processed, mainly by filling in the gaps in the trajectories. The case study highlighted problems such as missing points at the beginning and end of the recordings. Therefore, algorithm was developed to solve the above-mentioned problem and allowed for obtaining an accurate movement trajectory throughout the entire route. Realistic animations were created from the prepared data. The preliminary studies allowed one for the verification of the research method and implemented algorithm for obtaining animations reflecting accurate movements.

ABSTRACTNighttime light (NTL) remote sensing data plays a crucial role in comprehending changes in human activities. The availability of the daily lunar BRDF-corrected Black Marble NTL product (VNP46A2) enables the use of NTL data to detect and assess the impact of short-term emergencies. However, daily NTL data often experience missing values due to cloud cover and low-quality signals. To address this issue, many studies utilize monthly or annual time-composite NTL products, which restrict the timeliness and potential application scenarios of NTL data usage. Therefore, it is necessary to generate the gap-filled daily NTL product. This study presented a novel NTL gap-filling method comprising rough reconstruction based on spatiotemporal weighting and refined gap-filling using a Bidirectional Long Short-Term Memory (Bi-LSTM) model. We evaluate the accuracy of the proposed method using the “remove-reconstruct-compare” approach, which randomly removes some original data from the complete image, fills the gaps with the proposed gap-filling method, and compares the reconstructed NTL data with the original observations in Beijing, Shanghai, Xi’an and New York. The results reveal that when the rate of missing values in Beijing is 40% and 50%, the proposed gap-filling method achieves accuracy with mean coefficient of determination (R2) values of 0.834 and 0.841, accompanied by corresponding root mean square (RMSE) values of 7.793 and 7.171, respectively. Furthermore, the gap-filling accuracy was evaluated quantitatively, and our proposed gap-filling method performed better than the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Our proposed gap-filling method had R2 values of 0.685, 0.781, 0.720 and 0.642, which were higher than those for STARFM (0.430, 0.662, 0.221 and 0.345). The RMSE values of our gap-filling method were 9.628, 12.083, 10.963 and 19.882 for the four sites, while those of STARFM were 12.953, 14.872, 18.280 and 26.990, respectively. The temporal and spatial analysis results demonstrate that this model is robust, capturing city boundaries and NTL high-brightness hotspots with high accuracy and stability. The gap-filling model proposed in this study provides a new technique for expanding the potential applications and reliability of NASA’s daily Black Marble product (VNP46A2) in remote sensing.

This study made triangulated use of eye-tracking and retrospective verbal reports (RVRs) to compare the banked gap-filling processes of two same-scorers and those of a top scorer and a bottom scorer. The two same-scorers differed in their cognitive effort on global and local processing, fluency of choice making, and strategy use when completing the task and half of the mutual correct responses. Contrary to findings from previous studies, the top scorer exerted much greater effort on global and local processing than the bottom scorer, in her pursuit of perfection, and she reported much less use of syntax analysis as a strategy. The findings of this study increase our understanding of individual differences in the cognitive processes of English-as-a-foreign-language (EFL) reading and test-taking and prove the value of in-depth, multi-faceted process research. Featuring the use of heatmaps, eye-tracking metrics, choice-making graphs, gaze plots, and RVRs, this study also responds to an emergent concern in language assessment about how the enormous process data can be handled effectively. Implications for testing and learning EFL reading are further developed.

Gravity recovery and climate experiment (GRACE) and GRACE Follow-On (GRACE-FO) are Earth’s gravity satellite missions with hydrological monitoring applications. However, caused by measuring instrumental problems, there are several temporal missing values in the dataset of the two missions where a long gap between the mission dataset also exists. Recent studies utilized different gap-filling methodologies to fill those data gaps. In this article, we employ a variety of singular spectrum analysis (SSA) algorithms as well as the least squares-harmonic estimation (LS-HE) approach for the data gap-filling. These methods are implemented on six hydrological basins, where the performance of the algorithms is validated for different artificial gap scenarios. Our results indicate that each hydrological basin has its special behaviour. LS-HE outperforms the other algorithms in half of the basins, whereas in the other half, SSA provides a better performance. This highlights the importance of different factors affecting the deterministic signals and stochastic characteristics of climatological time series. To fill the missing values of such time series, it is therefore required to investigate the time series behaviour on their time-invariant and time-varying characteristics before processing the series.

Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science.

Sun-induced chlorophyll fluorescence (SIF) has proven to be advantageous in estimating gross primary production, despite the lack of a stable relationship. Satellite-based SIF measurements at Level 2 offer comprehensive global coverage and are available in near real time. However, these measurements are often limited by spatial and temporal sparsity, as well as discontinuities. These limitations primarily arise from incomplete satellite trajectories. Additionally, variability in cloud cover and periodic issues specific to the instruments can compromise data quality. Two families of methods have been developed to address data discontinuity: (1) machine learning-based gap-filling techniques and (2) geostatistical techniques (various forms of kriging). The former techniques utilize the relationships between ancillary data and SIF, while the latter usually rely on the available SIF data recordings and their covariance structure to provide estimates at unsampled locations. In this study, we create a synthetic approach for SIF gap filling by hybridizing the two approaches under the umbrella of kriging with external drift. We performed leave-one-out cross-validation of the OCO-2 SIF retrieval aggregates for the entire year of 2019, comparing three methods: ordinary kriging, ML-based estimation using ancillary data, and kriging with external drift. The Mean Absolute Error (MAE) for ML, ordinary kriging, and the hybrid approach was found to be 0.1399, 0.1318, and 0.1183 mW m2 sr−1 nm−1, respectively. We demonstrate that the performance of the hybrid approach exceeds both parent techniques due to the incorporation of information from multiple resources. This use of multiple datasets enriches the hybrid model, making it more robust and accurate in handling the spatio-temporal variability and discontinuity of SIF data. The developed framework is portable and can be applied to SIF retrievals at various resolutions and from various sources (satellites), as well as extended to other satellite-measured variables.

Abstract Structural distortions and imperfections are a crucial aspect of materials science, on the macroscopic scale providing strength, but also enhancing corrosion and reducing electrical and thermal conductivity. At the nanometre scale, multi-atom imperfections, such as atomic chains and crystalline domain walls have conversely been proposed as a route to topological superconductivity, whose most prominent characteristic is the emergence of Majorana Fermions that can be used for error-free quantum computing. Here, we shed more light on the nature of purported domain walls in Fe(Se,Te) that may host 1D dispersing Majorana modes. We show that the displacement shift of the atomic lattice at these line-defects results from sub-surface impurities that warp the topmost layer(s). Using the electric field between the tip and sample, we manage to reposition the sub-surface impurities, directly visualizing the displacement shift and the underlying defect-free lattice. These results, combined with observations of a completely different type of 1D defect where superconductivity remains fully gapped, highlight the topologically trivial nature of 1D defects in Fe(Se,Te).

Accurate climatic data, especially precipitation measurements, play a critical role in various studies concerning the water cycle, particularly in modeling flood and drought risks. Unfortunately, these datasets often suffer from temporary gaps that are randomly dispersed over time. This study aims to assess the effectiveness of three imputation methods: KNN, MICE, and missForest, in impute missing values in climate series. The evaluation is conducted in two distinct rainfall regimes: the Moulouya basin and the Sous Massa basin. The performance analysis considers the percentage of missing data across the entire dataset. The imputed datasets are used to estimate annual precipitation, which are then subjected to statistical tests to identify potential trends and detect changepoints. The analysis focuses on the precipitation series within the Souss Massa watershed, encompassing 27 rainfall stations. Results indicate that data imputation has a highly positive impact on the study of rainfall series trends and change point detection. The study found that studying trends without data imputation could lead to questionable conclusions. The most significant breakpoints detected in the analyzed rainfall series were in the years 1988, 1991, 1997, 2007, and 2010. The decrease in precipitation at stations showing a downward trend varies between -60 mm and -137 mm using the MICE method, and between -40 mm and 186 mm using the MissForest method.

Surface water is a vital component of the Earth’s water cycle and characterizing its dynamics is essential for understanding and managing our water resources. Satellite-based remote sensing has been used to monitor surface water dynamics, but cloud cover can obscure surface observations, particularly during flood events, hindering water identification. The fusion of optical and synthetic aperture radar (SAR) data leverages the advantages of both sensors to provide accurate surface water maps while increasing the temporal density of unobstructed observations for monitoring surface water spatial dynamics. This paper presents a method for generating dense time series of surface water observations using optical–SAR sensor fusion and gap filling. We applied this method to data from the Copernicus Sentinel-1 and Landsat 8 satellite data from 2019 over six regions spanning different ecological and climatological conditions. We validated the resulting surface water maps using an independent, hand-labeled dataset and found an overall accuracy of 0.9025, with an accuracy range of 0.8656–0.9212 between the different regions. The validation showed an overall false alarm ratio (FAR) of 0.0631, a probability of detection (POD) of 0.8394, and a critical success index (CSI) of 0.8073, indicating that the method generally performs well at identifying water areas. However, it slightly underpredicts water areas with more false negatives. We found that fusing optical and SAR data for surface water mapping increased, on average, the number of observations for the regions and months validated in 2019 from 11.46 for optical and 55.35 for SAR to 64.90 using both, a 466% and 17% increase, respectively. The results show that the method can effectively fill in gaps in optical data caused by cloud cover and produce a dense time series of surface water maps. The method has the potential to improve the monitoring of surface water dynamics and support sustainable water management.