Small island ecosystems are threatened by climate change in several ways. This includes the Galapagos archipelago in the Tropical Eastern Pacific, where limited freshwater makes the islands dependent on atmospheric supply through precipitation. However, precipitation distribution remains highly uncertain due to the lack of operational observation systems, and it is unclear how climate change will affect availability. Given its location, climate impacts are closely tied to changes in El Nino Southern Oscillation extremes during El Nino and La Nina years. Using a new measurement network incorporating a vertical rain radar profiler, we investigated seasonal rainfall changes (hot and cool) by analyzing El Nino/La Nina-like years, serving as surrogate for a locally warmer and locally cooler world. Our analysis demonstrates that in a locally warmer world, precipitation is increasing in both seasons. Rainfall characteristics and satellite-retrievals convective cloud frequency indicate more convective activity, intensifying heavy rainfall, especially during the hot season. In the cool season, drizzle is replaced by light rain throughout the vertical profile. In a locally cooler world, the hot season's typical midday rainfall maximum is replaced by oscillating, short-wavelength patterns and lower totals. Interestingly, the cool season in a La Nina-dominated world exhibits slightly higher rainfall than a neutral year, likely due to better condensation conditions of the advected moist air at lower air temperatures. Overall, results suggest improved total rainfall supply in a locally warmer world, but torrential rain could endanger the islands in the hot season. Furthermore, cool seasons shift from drizzle to light rain, though totals remain low overall.

Radiation fog poses challenges for the very short-term weather forecasting due to its complex atmospheric dynamics. Accurate and spatially available visibility predictions are crucial for sectors where visibility conditions directly impact safety and operational efficiency. Traditional numerical weather prediction models lack real-time forecasting capabilities, so this study investigates a machine-learning-based visibility forecast with XGBoost for a station in Germany. Two data sources were used and compared: high-resolution station data and nationwide available Meteosat Second Generation (MSG) satellite data. The analysis investigates how the coarser spatial resolution of MSG data compares to finer station data in predicting fog formation and dissipation. Therefore, station-based predictors were substituted with MSG satellite data. Additionally, the study addresses data imbalances during training and evaluation by focusing on critical low-visibility conditions and specifically fog formation and dissipation. XGBoost significantly outperforms the three baseline models-pure visibility driven forecast, Persistence Model and Linear Regression. The mean absolute error (MAE) is less than 150 m in the low visibility range. For the predominantly MSG-variable-based model only 3 % of fog formations and 6 % of fog dissipations are completely missed. Furthermore, the MSG-model predicts 50 % of fog formations and 60 % of dissipations within the 30-min window of their actual occurrence. The model utilizing MSG data as substitutes for station-based predictors delivers comparable performance to the purely station-data-based forecast highlighting the potential of area-wide accessible MSG data. However, visibility measurements remain necessary for forecasting. Therefore, future research should develop satellite-derived products to replace visibility, enabling fully spatial forecasts.

Tree-ring width (TRW) is key for the identification of forest growth responses to climate, but field sampling inherently limits spatial and temporal coverage. Satellite-derived vegetation indices (VIs) can bridge this gap, yet most canopy-growth studies continue to rely on Normalized Difference Vegetation Index (NDVI), a greenness metric long known to saturate in high-biomass canopies and to decouple from stem growth under stress. Unlike previous studies relying on single vegetation indices and static seasonal metrics, we implemented an approach that integrates multiple vegetation indices across several accumulated temporal windows to capture intra-annual tree growth dynamics and stress responses. Rolling-sum correlations for the growth-year and previous-year linked canopy signals from greenness-, structural-, wetness- and pigment-related indices to annual TRW. The analysis used a 23-year (2000–2022) 8-day Landsat seamless data cube (SDC) at 30 m resolution covering 24 different forest sites in western and central Germany. Fifteen trees were investigated at each site, from mixed and pure stands consisting of the tree species Fagus sylvatica, Quercus petraea, Quercus robur, Pinus sylvestris, Pseudotsuga menziesii, and Larix decidua. Greenness indices, including the NDVI, consistently underperformed. Structure, including Near Infrared of Vegetation Index (NIRv), and wetness indices produced higher and more temporally targeted correlations, especially in pure stands and for beech during summer. Coniferous and mixed plots showed weaker, broader signals, while pigment-ratio indices proved comparatively most informative in mixed stands. High-resolution temporal analysis of the 8-day SDC Landsat time series revealed species-specific peak correlation windows, enabling the identification of critical periods driving radial tree growth. The results of this study by moving beyond greenness-only approaches and single-value, growing-season analyses reveals much better performance compared to studies averaging to growing-season means that blur these peaks and consequently report weaker or no correlations between tree growth and the satellite signal.

Monitoring vegetation dynamics and optical proxies of photosynthetic activity is essential for understanding ecosystem functioning and forest responses to climate change. Optical remote sensing indices such as Normalized Difference Vegetation Index (NDVI) and Near Infrared of Vegetation Index (NIRv) are widely used as proxies for vegetation health and photosynthetic capacity, but their application is limited by cloud-induced temporal gaps. While SAR data offer weather-independent observations, NIRv reconstruction from SAR has rarely been investigated, particularly in forest ecosystems. In this study, we introduce two novel SAR-based indices, the Radar Volume Vegetation Index (RVVI) and the Radar Structure Vegetation Index (RSVI), and assess their relationship with NIRv as an established optical proxy for photosynthetic capacity. Using Sentinel-1 SAR and Sentinel-2 optical data, we analyzed species-specific relationships between a collinearity-reduced set of seven SAR vegetation indices and NIRv, and subsequently evaluated the predictive performance of the complete set of 15 SAR vegetation indices using a Foreward Feature Selection based Random Forest model. RVVI and RSVI showed low collinearity with existing SAR indices and the strongest correlations with NIRv, reaching up to |r| = 0.65 for deciduous species, while correlations were lower for conifers (|r| = 0.26). The cross-species Random Forest model achieved high predictive accuracy with an overall R2 of 0.85 and MAE of 0.03. However, performance differed between forest types, with higher predictive accuracy for deciduous species (R2 = 0.86 – 0.88) than for conifers (R2 = 0.77 – 0.79). RVVI and RSVI were identified as the most important SAR predictors. These findings demonstrate that the proposed indices provide complementary structural information that can improve the reconstruction of NIRv-like variability and show the potential to support more temporally continuous, cloud-independent monitoring of this optical proxy across forest ecosystems. However, the lower performance observed for coniferous species highlights the need for further refinement to fully capture species-specific NIRv variability.

IntroductionHydrological hazards, such as floods and landslides are frequently driven by extreme rainfall events (ERE). Thus, understanding the spatio-temporal patterns and intra-event behavior of these events is important for identifying vulnerable regions, improving early warning systems, and enhancing water management.MethodsThis study aimed to analyze the spatio-temporal intra-event characteristics of ERE, using high-resolution (5min) weather radar data focusing on their internal structure and spatial distribution. The study was conducted in the headwaters of the Paute basin (2,200–4,400 m a.s.l.) in southern Ecuador. Based on three ERE classes, four intra-event rainfall features were analyzed: area, maximum rainfall, cohesion, and the locations of rainfall hotspots.ResultsThese features revealed different rainfall patterns for the three distinct rainfall classes. Class 1 is characterized by the highest rainfall peaks, concentrated between 12:00 and 19:00 (afternoon). Class 3 shows the lowest rainfall peaks. Class 2 shows the least cohesive rainfall core and a mixed behavior in features. Regarding the locations of rainfall hotspots, classes 1 and 2 show hotspots located at the catchment outlets and at the urban (City of Cuenca) areas of the sub-catchments (around 2,500 m a.s.l), while those in class 3 are found at headwaters (above 3,500 m a.s.l).DiscussionIdentifying these rainfall characteristics and hotspot location provides a better understanding of extreme rainfall behavior in the tropical Andes, which enhances knowledge of hydrological processes, and improves flood forecasting.

Wet snow is an important component of the cryosphere. It appears darker in the visible compared to the fresh dry snow. This indicates the increased levels of light absorption in snow, which leads to the acceleration of snowmelt further increased by the exposure of light absorbing impurities, in turn having important climatic effects. In this article, a simple analytical model for the wet snow spectral reflectance is introduced and applied to remote sensing of snow from ground and space. The accuracy of the model is studied using experimental measurements of the light reflectance from wet snow.

Abstract Plant-associated microbial communities exhibit pronounced specificity across biological and spatial scales. While the patterns and accompanied functions have been well documented across and within plant species, the functional importance of intra-individual variation remains underexplored. Particularly in trees that experience strong environmental gradients within single crowns, stratum -specific microbiota may significantly contribute to plant performance. We experimentally tested whether variation in microbiota within the crown of Quercus robur is related to host performance. In mesocosm experiments, we transferred microbial communities derived from sun and shade leaves to germ-reduced clonal individuals of the same species and applied UV radiation simulating conditions that matched or mismatched the origin of the microbial inoculum (environmental matching). Our results demonstrate that matching microbiota-environment combinations increased plant performance compared to mismatching combinations. We infer that pronounced intra-individual variation of leaf-associated microbial communities not only reflects environmental heterogeneity along canopy strata but is functionally relevant for the plant host.

Global evidence indicates that orography can exert a strong influence on rainfall, thereby giving rise to natural hazards such as flooding in mountain areas. Alongside orographic influences, urban areas can also act to intensify storms, but their impact on heavy rainfall in high elevations has not yet been investigated in detail. Here, we quantify the contribution of a high-altitude city to both rainfall intensification and its daytime and nighttime patterns, focusing on the city of Cuenca in the Ecuadorian Andes. Modeling several observed storms with and without the city suggests that the urban area can enhance downstream precipitation by over 20% over a pre-existing hotspot of orographically induced precipitation. This urban-induced rainfall intensification seems to exceed that observed for cities of comparable size outside mountainous regions. This may be explained by Cuenca’s valley setting and its high humidity compared to its surroundings, suggesting that high-altitude cities with similar morphologies could amplify combined orographic-dynamic and urban-thermodynamic effects.

Monitoring vegetation dynamics and photosynthetic activity is essential for understanding ecosystem functioning and forest responses to climate change. Optical remote sensing indices such as Normalized Difference Vegetation Index (NDVI) and Near Infrared of Vegetation Index (NIRv) are widely used as proxies for vegetation health and photosynthetic capacity, but their application is limited by cloud-induced temporal gaps. While SAR data offer weather-independent observations, NIRv reconstruction from SAR has rarely been investigated, particularly in forest ecosystems. In this study, we introduce two novel SAR-based indices, the Radar Volume Vegetation Index (RVVI) and the Radar Structure Vegetation Index (RSVI), and assess their relationship with NIRv as an established proxy for photosynthetic capacity. Using Sentinel-1 SAR and Sentinel-2 optical data, we analyzed species-specific relationships between seven SAR indices and NIRv and evaluated their predictive performance with a random forest model.RVVI and RSVI showed low collinearity with existing SAR indices and the strongest correlations with NIRv, reaching up to |r| = 0.65 for deciduous species, while correlations were lower for conifers (|r| = 0.26). The cross-species random forest model achieved high predictive accuracy (R² = 0.85; MAE = 0.03), with RVVI and RSVI identified as the most important predictors. These findings demonstrate that the proposed indices contains sufficient structural information to reconstruct NIRv-like variability across forest ecosystems, enabling cloud-independent monitoring of vegetation functioning.

The Galapagos Islands, located in the eastern equatorial Pacific approximately 1000 km west of mainland Ecuador, are highly sensitive to the El Nino-Southern Oscillation. However, the mechanisms linking sea surface temperature (SST) variability to daily rainfall extremes remain poorly understood. Focusing on Santa Cruz Island, one of the main islands of the archipelago, we analyzed the response of daily rainfall to four El Nino events (1991-1992, 1997-1998, 2015-2016 and 2023-2024) and their relationship with SST spatial patterns. Our approach followed three steps: (1) Daily rainfall observations were classified using percentile thresholds; (2) SST spatial clusters were identified using Local Indicators of Spatial Association (LISA), which explicitly incorporates spatial autocorrelation to distinguish warm and cold SST spatial clusters; and (3) SST cluster metrics (mean temperature, spatial extent, and persistence) were extracted and related to rainfall intensification. Results show that El Nino can increase daily extreme rainfall (>P95) in frequency and in totals, with the strongest and most persistent signal during 1997-1998; in contrast, the 2015-2016 event, despite being classified as very strong by the Oceanic Nino Index (ONI), exhibited a limited and short-lived >P95 rainfall response in Santa Cruz. The link between SST clusters and extreme rainfall strengthened during El Nino (r from similar to 0.40 to 0.70). Correspondingly, SST clusters underwent significant spatial reorganization in their extent and persistence. Contrasts were most evident in the central-southern domain, where 1997-1998 showed strong warm incursion and persistent >= 28 degrees C coverage, while 2015-2016 remained more spatially constrained and less coherent. The area where clusters reached mean SST >= 28 degrees C became widespread in 1997-1998 (98.55%), whereas it remained more localized in 1991-1992 (30.28%), 2015-2016 (27.02%), and 2023-2024 (26.55%) and was absent in neutral years (0%). Persistent warm-cluster coverage increased from neutral conditions (38.53%) in 1991-1992 (47.49%), 1997-1998 (53.42%), and 2023-2024 (42.97%), but was lower in 2015-2016 (34.53%). Overall, these results provide a process-oriented link between SST cluster organization and event-to-event differences in Galapagos rainfall extremes, highlighting the value of local SST metrics beyond basin-scale ENSO indices.

The paper is aimed at the retrieval of total column water vapor (TCWV) over a melting glacier using high spatial resolution hyperspectral measurements performed by the German EnMAP (Environmental Mapping and Analysis Program) satellite. Such measurements are important for understanding the intra-pixel variability of water vapor satellite products with coarser spatial resolution especially in regions with drastic changes in the water vapor abundances. The retrieved TCWV is compared with that derived using other spaceborne optical measurements such as performed by Sentinel-5P. The close correspondence of both retrievals has been found with a smaller variance over derived EnMAP TCWV values.

Tropical rainforests represent major carbon reservoirs and play a critical role in regulating carbon dynamics and climate. Quantifying their carbon fluxes is essential for understanding ecosystem responses to climate variability and improving future climate change projections. In this study, we investigated interannual and seasonal variability in gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem productivity (NEP) in a montane tropical rainforest in Southern Ecuador using eddy-covariance measurements from 2019 to 2024. GPP showed rather low interannual variability with a gradual increase from approximately 7.79–8.92 gCm−2day−1 over the study period. Seasonal differences were generally weak, although a more pronounced contrast was observed in 2024 with higher GPP during the wet phase (10.26 gCm−2day−1) and compared to the dry phase (7.38 gCm−2day−1). The ecosystem consistently functioned as a net carbon sink, with maximum carbon uptake occurring in 2024 (NEP approximately −3.97 gCm−2day−1). Despite the general stability of GPP, variability in net carbon exchange was primarily driven by changes in Reco, indicating a stronger sensitivity of respiration to environmental fluctuations. Principal component analysis (PCA) revealed that carbon flux variability reflects a combination of radiation and moisture controls. Incoming solar radiation (Rg) showed the strongest association with GPP, whereas soil temperature (Ts) and soil moisture (SM) appeared to influence variations in Reco. Furthermore, we assessed the influence of ENSO phases on carbon fluxes and found reduced productivity during El Niño conditions, associated with elevated Ts and slight reduced SM and Rg. These results highlight the sensitivity of tropical montane forests to both local environmental drivers and large-scale climate variability. Overall, our findings demonstrate that while short-term photosynthetic processes remain generally stable, respiration processes were identified as important correlates of interannual variability in carbon balance, highlighting their relevance to tropical carbon dynamics.

Biodiversity is increasingly threatened by human land use and climate change, making predictive modeling crucial for conservation planning and the identification of priority conservation areas. Large-scale species distribution projections can be improved by integrating remotely sensed vegetation indices, as they reflect important vegetation and habitat characteristics. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) indicate vegetation productivity, while texture metrics derived from these indices reveal habitat structure. When combined with climatic variables, these indicators can significantly improve the accuracy of species distribution models (SDMs). In this study, we evaluated the performance of SDMs using vegetation indices, texture metrics, and climatic variables to predict the distribution of the Violet-throated Metaltail (Metallura baroni), a microendemic hummingbird restricted to the environmentally complex high-altitude regions of the southern Ecuadorian Andes. Using a backward-selection and cross-validation approach for predictor selection and the Maximum Entropy (MaxEnt) algorithm, we compared model performance using AUC values. Our results demonstrate that incorporating habitat structure indicators (NDVI- and NDWI-derived texture metrics) together with climatic variables significantly improves SDM performance, allowing better discrimination of shrub ecosystems where M. baroni occurs. This approach highlights the importance of integrating key aspects of habitat structure as indicators of resource availability, which directly influence the distribution of bird species in complex landscapes.

Here, we study melting glacier surface albedo using satellite hyperspectral imagery from the Environmental Mapping and Analysis Program (EnMAP). The proposed broadband albedo (BBA) retrieval algorithm is based on radiative transfer theory and includes atmospheric and topography corrections. The comparison with ground and other satellite snow and ice albedo products is presented. Ways to improve current satellite snow and ice albedo retrieval algorithms are discussed. While the EnMAP-derived BBA is highly correlated with BBA retrieved from other spaceborne instrumentation and algorithms, the various modern snow and ice satellite BBA products can differ by more than 5–10% for snow and, especially, bare ice. Bare ice optical heterogeneity is high from variable roughness conditions and impurity content. Bare ice BBA uncertainties exceed requirements needed for highly accurate assessment of glacier climatic effects.

This study investigates the thermal characteristics of a highly biodiverse mountain ecosystem in southern Ecuador. The analysis involves temperature measurements conducted within the native mountain forest and at open sites across an altitudinal range from 1600 to 3200 meters. The primary methodological objective is to create a tool for regionalizing air temperature, enabling the generation of spatial datasets for average monthly mean, minimum, and maximum temperatures using observational data. These temperature maps, based on data spanning from 1999 to 2023, are essential for ecological projects operating in areas lacking climate station data. To develop the temperature maps, a combination of a straightforward detrending technique, a Digital Elevation Model, and a satellite-based land cover classification is employed. This classification also provides information on the relative forest cover per pixel. The specific focus of the study is to examine the thermal structure of both components of the ecosystem (pastures and natural vegetation), with special attention given to how the conversion of natural forest into pasture affects the ecosystem's temperature regulation service. The findings reveal a distinct thermal variation throughout the year, influenced in part by changes in synoptic weather patterns and the impact of land cover. Thermal amplitudes are notably low during the primary rainy season when cloudiness and air humidity are high. However, they become more pronounced in the relatively dry season, marked by differences in daily irradiance and outgoing nocturnal radiation between land cover units. Lower pasture areas, resulting from slash-and-burn practices on the natural forest, experience the most extreme thermal conditions, while the atmosphere within the mountain forest remains slightly cooler due to the regulating effects of dense vegetation. In summary, the study underscores that clearing the forest diminishes the ecosystem's thermal regulation function (regulating ecosystem services). This reduction in thermal regulation could pose challenges, especially in the context of anticipated global warming trends in the future.

Accurate estimation of functional vegetation traits is essential for understanding ecosystem dynamics and supporting environmental monitoring. This study investigates the potential of spaceborne hyperspectral data from the Environmental Mapping and Analysis Program (EnMAP) for mapping key forest vegetation traits such as leaf area index (LAI), leaf chlorophyll content (Cab), leaf mass per area (LMA), specific leaf area (SLA), and leaf water content (Cw) as well as the rarely addressed leaf anthocyanin content (Canth) using a hybrid approach combining radiative transfer calculations and machine learning. While machine learning-based studies on vegetation traits retrieval often have relied on intensive field data collection, the present studies explicitly focus on situations with limited field samples which is a often the case in ecosystem research. Here, we use a combination of the PROSAIL-D leaf and canopy radiative transfer models (RTMs) and Gaussian Process Regression (GPR) optimized with Active Learning (AL) sampling to improve the retrieval accuracy while minimizing reliance on extensive in situ data. Validation results demonstrated that the hybrid approach successfully retrieved LAI (R2 = 0.69) and Cab (R2 = 0.68) with relatively low errors (RMSE of 0.172 m2/m2 for LAI and 1.502 mu g/cm2 for Cab), while more complex traits, such as Canth, LMA, and Cw exhibited moderate retrieval performance only (R2 of 0.45, 0.51, and 0.31, respectively). The application of optimized hybrid GPR models to the study area enabled area-wide landscape forest trait mapping with minimal computational effort.

Understanding the partitioning of downward shortwave radiation into direct and diffuse components is essential for modeling ecosystem energy fluxes. Accurate partitioning functions are critical for land surface models (LSMs) coupled with climate models, yet these functions often depend on regional cloud and aerosol conditions. While data for developing semi-empirical partitioning functions are abundant in mid-latitudes, their performance in tropical regions, particularly in the high Andes, remains poorly understood due to scarce ground-based measurements. This study analyzed a unique dataset of shortwave radiation components from a tropical mountain rainforest (MRF) in southern Ecuador, developing and testing a locally adapted partitioning function using Random Forest Regression. The model achieved high accuracy in predicting the percentage of diffuse radiation (%Dif; R2=0.95, RMSE = 5.33, MAE = 3.74) and absolute diffuse radiation (R2=0.99, RMSE = 5.30, MAE = 14). When applied to simulate upward shortwave radiation, the model outperformed commonly used partitioning functions achieving the lowest RMSE (8.62) and MAE (5.82) while matching the highest R2 (0.97). These results underscore the importance of regionally adapted radiation partitioning functions for improving LSM performance, particularly in complex tropical environments. The adapted LSM will be further utilized for studies on heat fluxes and carbon sequestration.

Aim: Progress has been made in understanding the relationship between biodiversity and ecosystem functioning (BEF) in both experimental and real-world ecosystems. Yet, we have a limited understanding of the extent to which biodiversity affects ecosystem functioning in heterogeneous environments and whether variation in ecosystem functioning between communities is related to variation in species richness or turnover. Here, we quantify the relative contribution of variation in species richness and species turnover to variation in ecosystem functioning between communities (i.e., the diversity effect) along two tropical elevational gradients. Location: Andes (Ecuador) and Mt. Kilimanjaro (Tanzania).Taxa Studied Woody plants, springtails, soil arthropods, ants and frugivorous birds. Methods: We collected data on seven ecosystem functions, including biomass and process rates, across six ecosystem types along the two elevational gradients. We then combine the ecological Price equation with the concept of beta-diversity to quantify how the diversity effect is shaped by environmental heterogeneity within and across ecosystem types, and whether the effect of environmental heterogeneity is primarily mediated by variation in species richness or species turnover. Results: The diversity effect on ecosystem functioning increased consistently with environmental heterogeneity on both mountains. Species richness and turnover, on average, contributed similarly to the diversity effect on ecosystem functioning in both mountain regions, but effect sizes varied across functions. The increase in the diversity effect with environmental heterogeneity was primarily mediated by species richness, while species turnover played a secondary role in mediating the effects of environmental heterogeneity. Main Conclusions: Our study reveals that the diversity effect on ecosystem functioning increases with environmental heterogeneity and that species richness, rather than species turnover, primarily drives this relationship. The dominant role of species richness in mediating the effect of environmental heterogeneity indicates that BEF relationships along environmental gradients are strongly influenced by environmental filters that limit local species coexistence.

Like many small oceanic islands, the Galapagos archipelago, renowned for its unique geographic location and exceptional endemic biodiversity, faces significant challenges under climate change. In particular, the atmospheric water supply for the ecosystem and the local population is under threat, with clouds and rain playing an important role in ensuring freshwater availability under climate change. Better planning of adaptation measures would require climate data on clouds as a prerequisite for precipitation and rainfall at high spatio-temporal resolution, which are not available in this area. Operational products such as satellite derived cloud and precipitation products or reanalysis data are widely used to compensate for the lack of local data availability but are often poorly suited for regional applications. In the current study, we aim to generate high quality area-wide cloud information to distinguish ecoclimatic cloud zones that may require different adaptation measures to climate change. To address this issue, we have developed a new physical rule-based cloud mask retrieval specifically tailored for the Galapagos Archipelago, based on data from the third generation GOES-16 Advanced Baseline Imager (ABI) geostationary satellite. The new Galapagos Rainfall Retrieval (GRR) cloudmask was tested against independent observational data and compared to both the operational GOES-16 ACM (ABI Clear sky Mask) and the MODIS cloudmask benchmark cloud mask. Our test results confirm that the GRR-cloudmask (Probability of Detection POD = 0.94, Critical Success Index CSI = 0.92-0.93) clearly outperforms the operational ACM-cloudmask (POD = 0.56-0.68, CSI = 0.55-0.67). Area-wide tests against the MODIS cloud mask showed a CSI of 0.72 and a POD of 0.74 for the ACM, which is superior to the GOES-16 ACM-cloudmask. We produced cloud frequency maps for all months and day slots and analysed cloud frequency using ancillary meteorological data. In general, the cool season (Jun-Dec) / night shows much higher cloud frequencies than the warm season (Jan-May) / daytime. However, regional cloud patterns differ along a west-to-east and south-tonorth gradient, depending on complex interactions of forcing parameters such as exposure to the main circulation, sea surface temperature zones, altitude and land cover. A k-mean cluster analysis resulted in nine ecoclimatic cloud zones over land, which are much more differentiated than the widely used four-zone classification. The results will help to develop more site-specific climate change adaptation planning for the iconic Galapagos National Park.