Search Results (488)

The FLEX-S3 mission by ESA will deliver key Level 2 products such as sun-induced chlorophyll fluorescence (SIF) and vegetation-reflected radiance. To validate these, the SpaFLEX project, funded by the Spanish Ministry of Science and Innovation, is developing a robust calibration and validation strategy in Spain. This includes test site setup, instrument characterization, and sampling protocols. A field campaign was conducted in two Holm Oak forests in Teruel, analyzing Sentinel-2 spatial heterogeneity and collecting ground, UAV, and airborne data. The results support scaling procedures to match the 300 m pixel resolution of FLEX-S3, ensuring product accuracy and compliance with ESA standards. Full article

This study investigates the potential of spaceborne imaging spectroscopy to support the analysis of the status of two major Estonian lakes, i.e., Lake Peipsi and Lake Võrtsjärv, using data from the PRISMA and EnMAP missions. The study encompasses nine specific applications across 12 satellite scenes, including the validation of remote sensing reflectance (Rrs), optical water type classification, estimation of phycocyanin concentration, detection of macrophytes, and characterization of reflectance for lake ice/snow coverage. Rrs validation, which was performed using in situ measurements and Sentinel-2 and Sentinel-3 as references, showed a level of agreement with Spectral Angle < 16°. Hyperspectral imagery successfully captured fine-scale spatial and spectral features not detectable by multispectral sensors, in particular it was possible to identify cyanobacterial pigments and optical variations driven by seasonal and meteorological dynamics. Through the combined use of in situ observations, the study can serve as a starting point for the use of hyperspectral data in northern freshwater systems, offering new insights into ecological processes. Given the increasing global concern over freshwater ecosystem health, this work provides a transferable framework for leveraging new-generation hyperspectral missions to enhance water quality monitoring on a global scale. Full article

Using interferometric synthetic aperture radar (InSAR) to observe slow ground deformation can be challenging due to many sources of error, with tropospheric phase delay and unwrapping errors being the most significant. While analytical methods, weather models, and data exist to mitigate tropospheric error, most of these techniques are unsuitable for all InSAR applications (e.g., complex tropospheric mixing in the tropics) or are deficient in spatial or temporal resolution. Likewise, there are methods for removing the unwrapping error, but they cannot resolve the true phase when there is a high prevalence (>40%) of unwrapping error in a set of interferograms. Applying tropospheric delay removal techniques is unnecessary for C-band Sentinel-1 InSAR time series studies, and the effect of unwrapping error can be minimized if the full dataset is utilized. We demonstrate that using interferograms with long temporal baselines (800 days to 1600 days) but very short perpendicular baselines (<5 m) (LTSPB) can lower the velocity detection threshold to 2 mm y⁻¹ to 3 mm y⁻¹ for long-term coherent permanent scatterers. The LTSPB interferograms can measure slow deformation rates because the expected differential phases are larger than those of small baselines and potentially exceed the typical noise amplitude while also reducing the sensitivity of the time series estimation to the noise sources. The method takes advantage of the Sentinel-1 mission length (2016 to present), which, for most regions, can yield up to 300 interferograms that meet the LTSPB baseline criteria. We demonstrate that low velocity detection can be achieved by comparing the expected LTSPB differential phase measurements to synthetic tests and tropospheric delay from the Global Navigation Satellite System. We then characterize the slow (~3 mm/y) ground deformation of the Socorro Magma Body, New Mexico, and the Tampa Bay Area using LTSPB InSAR analysis. The method we describe has implications for simplifying the InSAR time series processing chain and enhancing the velocity detection threshold. Full article

The use of Google Earth Engine (GEE), a cloud-based computing platform, in spatio-temporal evaluation studies has increased rapidly in natural sciences such as forestry. In this study, Sentinel-2 satellite imagery and Shuttle Radar Topography Mission (SRTM) elevation data and image classification algorithms based on two machine learning techniques were examined. Random Forest (RF) and Classification and Regression Trees (CART) were used to classify land use and land cover (LULC) in western Oregon (USA). To classify the LULC from the spectral bands of satellite images, a composition consisting of vegetation difference indices NDVI, NDWI, EVI, and BSI, and a digital elevation model (DEM) were used. The study area was selected due to a diversity of land cover types including research forest, botanical gardens, recreation area, and agricultural lands covered with diverse plant species. Five land classes (forest, agriculture, soil, water, and settlement) were delineated for LULC classification testing. Different spatial points (totaling 75, 150, 300, and 2500) were used as training and test data. The most successful model performance was RF, with an accuracy of 98% and a kappa value of 0.97, while the accuracy and kappa values for CART were 95% and 0.94, respectively. The accuracy of the generated LULC maps was evaluated using 500 independent reference points, in addition to the training and testing datasets. Based on this assessment, the RF classifier that included elevation data achieved an overall accuracy of 92% and a kappa coefficient of 0.90. The combination of vegetation difference indices with elevation data was successful in determining the areas where clear-cutting occurred in the forest. Our results present a promising technique for the detection of forests and forest openings, which was helpful in identifying clear-cut sites. In addition, the GEE and RF classifier can help identify and map storm damage, wind damage, insect defoliation, fire, and management activities in forest areas. Full article

Semi-Mediterranean (SM) and semi-arid (SA) regions, exemplified by the Kurdo-Zagrosian forests in western Iran and northern Iraq, have experienced frequent wildfires in recent years. This study proposes a modified Non-Negative Matrix Factorization (NMF) method for detecting fire-prone areas using satellite-derived data in SM and SA forests. The performance of the proposed method was then compared with three other already proposed NMF methods: principal component analysis (PCA), K-means, and IsoData. NMF is a factorization method renowned for performing dimensionality reduction and feature extraction. It imposes non-negativity constraints on factor matrices, enhancing interpretability and suitability for analyzing real-world datasets. Sentinel-2 imagery, the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Zagros Grass Index (ZGI) from 2020 were employed as inputs and validated against a post-2020 burned area derived from the Normalized Burned Ratio (NBR) index. The results demonstrate NMF’s effectiveness in identifying fire-prone areas across large geographic extents typical of SM and SA regions. The results also revealed that when the elevation was included, NMF_L1/2-Sparsity offered the best outcome among the used NMF methods. In contrast, the proposed NMF method provided the best results when only Sentinel-2 bands and ZGI were used. Full article

Tree species classification provides invaluable information across various sectors, from forest management to conservation. This task is most commonly performed using remote sensing; however, this method is prone to classification errors, which modern computational approaches aim to minimize. Convolutional neural networks (CNNs) used to model tabular data have recently gained popularity as a highly efficient classification tool. In the present study, a variation of this method is used to classify satellite multispectral data from the Sentinel-2 mission to distinguish between 18 common Polish tree species. The novel model is trained and tested on data from species-homogeneous forest stands. The data form a multi-seasonal time series and cover five years of observations. The model achieved an overall accuracy of 80% and Cohen Kappa of 0.80 of the raw output and increased to 93% with post-processing procedures. Considering the large number of species classified, this is a promising and encouraging result. The presented results indicate the importance of early vegetation season reflectance data in model training. The spectral bands representing the infrared, red-edge and green wavelengths had the greatest impact on the model. Full article

Accurately estimating the forest canopy height is essential for quantifying forest biomass and carbon storage. Recently, the ICESat-2 and GEDI spaceborne LiDAR missions have significantly advanced global canopy height mapping. However, due to inherent sensor limitations, their footprint-level estimates often show systematic bias. Tall forests tend to be underestimated, while short forests are often overestimated. To address this issue, we used coincident G-LiHT airborne LiDAR measurements to correct footprint-level canopy heights from both ICESat-2 and GEDI, aiming to improve the canopy height retrieval accuracy across Puerto Rico’s tropical forests. The bias-corrected LiDAR dataset was then combined with multi-source predictors derived from Sentinel-1/2 and the 3DEP DEM. Using these inputs, we trained a canopy height inversion model based on the AutoGluon stacking ensemble method. Accuracy assessments show that, compared to models trained on uncorrected single-source LiDAR data, the new model built on the bias-corrected ICESat-2–GEDI fusion outperformed in both overall accuracy and consistency across canopy height gradients. The final model achieved a correlation coefficient (R) of 0.80, with a root mean square error (RMSE) of 3.72 m and a relative RMSE of 0.22. The proposed approach offers a robust and transferable approach for high-resolution canopy structure mapping and provides valuable support for carbon accounting and tropical forest management. Full article

Accurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21 September 2019) in southeastern Texas. Dual-polarization Sentinel-1 SAR data (VH and VV) were processed by computing the VH/VV backscatter ratio, and the resulting ratio image was classified using a supervised Random Forest classifier to delineate water and land. All Sentinel-1 images underwent radiometric calibration, speckle noise filtering, and terrain correction to ensure precision in flood delineation. The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. The spatial distribution of flooding was strongly influenced by topography and land cover. Analysis of Shuttle Radar Topography Mission (SRTM) digital elevation data revealed that low-lying, flat terrain was most vulnerable to inundation; correspondingly, the land cover types most affected were hay/pasture, cultivated land, and emergent wetlands. Additionally, urban areas with low-intensity development experienced extensive flooding, attributed to impervious surfaces exacerbating runoff. A strong, statistically significant correlation (R² = 0.87, p < 0.01) was observed between precipitation and flood extent, indicating that heavier rainfall led to greater inundation; accordingly, the areas with the highest rainfall totals (e.g., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. Harnessing globally available Sentinel-1 data with near-real-time processing and a robust classifier, this approach provides a scalable solution for rapid flood monitoring. These findings underscore the potential of SAR-based flood mapping under adverse weather conditions, thereby contributing to improved disaster preparedness and resilience in flood-prone regions. Full article

Mangrove forests are vital ecosystems with the highest global carbon absorption capacity, playing a crucial role in climate change mitigation. Therefore, their conservation and management are essential. However, as mangroves are primarily found in tropical regions, frequent cloud cover and limited accessibility pose significant challenges to effective monitoring using optical satellite imagery. In addition, many developing countries with extensive mangrove coverage face challenges in conducting precise monitoring due to limited technological infrastructure. To overcome these limitations, this study integrated open-access synthetic aperture radar (SAR) data with optical imagery to enhance the classification accuracy of mangrove forests in the Bali Denpasar–Badung region. The Sentinel-1 and Sentinel-2 datasets were used, and the U-Net deep learning model was employed for training and classification. A digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) was applied to exclude areas higher than 10 m above sea level, thereby improving the classification accuracy. Additionally, a time-series analysis was performed to assess changes in the mangrove distribution over the past decade, revealing a consistent increase in mangrove extent in the study area. The classification performance was evaluated using a confusion matrix, demonstrating that the combined SAR-optical model outperformed single-source models across all key metrics including precision, accuracy, recall, and F1-score. The findings highlight the effectiveness of integrating SAR and optical data for capturing the complex ecological and geographical characteristics of mangrove forests. Notably, SAR imagery, which is resistant to cloud cover, shows considerable potential for independent application in tropical mangrove monitoring, warranting further research to explore its capabilities in greater depth. Full article

In the past three decades, the city of Addis Ababa, a capital city of Africa, has grown significantly in population, facilities, and infrastructure. The area involved in the recent urbanization is prone to slow natural subsidence phenomena that can be accelerated due to anthropogenic factors such as groundwater overexploitation and loading of unconsolidated soils. The main aim of this study is to identify and monitor the areas most affected by subsidence in a context, such as that of many areas of emerging countries, characterized by the lack of geological and technical data. In these contexts, advanced remote sensing techniques can support the assessment of spatial and temporal patterns of ground instability phenomena, providing critical information on potential conditioning and triggering factors. In the case of subsidence, these factors may have a natural or anthropogenic origin or result from a combination of both. The increasing availability of SAR data acquired by the Sentinel-1 mission around the world and the refinement of processing techniques that have taken place in recent years allow one to identify and monitor the critical conditions deriving from the impressive recent expansion of megacities such as Addis Ababa. In this work, the Sentinel-1 SAR images from Oct 2014 to Jan 2021 were processed through the PS-InSAR technique, which allows us to estimate the deformations of the Earth’s surface with high precision, especially in urbanized areas. The obtained deformation velocity maps and displacement time series have been validated using accurate second-order geodetic control points and compared with the recent urbanization of the territory. The results demonstrate the presence of areas affected by a vertical rate of displacement of up to 21 mm/year and a maximum displacement of about 13.50 cm. These areas correspond to sectors that are most predisposed to subsidence phenomena due to the presence of recent alluvial deposits and have suffered greater anthropic pressure through the construction of new buildings and the exploitation of groundwater. Satellite interferometry techniques are confirmed to be a reliable tool for monitoring potentially dangerous geological processes, and in the case examined in this work, they represent the only way to verify the urbanized areas exposed to the risk of damage with great effectiveness and low cost, providing local authorities with crucial information on the priorities of intervention. Full article

Unmanned aerial vehicle (UAV) remote sensing has emerged as a powerful tool for precision agriculture, offering high-resolution crop monitoring capabilities. However, multi-flight UAV missions introduce radiometric inconsistencies that hinder the accuracy of vegetation indices and physiological trait estimation. This study investigates the efficacy of relative radiometric correction in enhancing canopy chlorophyll content (CCC) estimation for winter wheat. Dual UAV sensor configurations captured multi-flight imagery across three experimental sites and key wheat phenological stages (the green-up, heading, and grain filling stages). Sentinel-2 data served as an external radiometric reference. The results indicate that relative radiometric correction significantly improved spectral consistency, reducing RMSE values (in spectral bands by >86% and in vegetation indices by 38–96%) and enhancing correlations with Sentinel-2 reflectance. The predictive accuracy of CCC models improved after the relative radiometric correction, with validation errors decreasing by 17.1–45.6% across different growth stages and with full-season integration yielding a 44.3% reduction. These findings confirm the critical role of relative radiometric correction in optimizing multi-flight UAV-based chlorophyll estimation, reinforcing its applicability for dynamic agricultural monitoring. Full article

Tropical forests provide essential ecosystem services, playing a critical role in climate regulation, biodiversity conservation, and regional hydrological cycles while also supporting livelihoods. However, they are increasingly threatened by deforestation and land-use change. Accurate land cover (LC) mapping is vital to monitor these changes, but mapping tropical forests is challenging due to complex spatial patterns, spectral similarities, and frequent cloud cover. This study aims to improve LC classification accuracy in such a heterogeneous tropical forest region in Southeast Asia, namely Kulen, Cambodia, which is characterized by natural forests, regrowth forests, and agricultural lands including cashew plantations and croplands, using Sentinel-2 imagery, recursive feature elimination (RFE), and Random Forest. We generated 65 variables of spectral bands, indices, bi-seasonal differences, and topographic data from Sentinel-2 Level-2A and Shuttle Radar Topography Mission datasets. These variables were extracted from 1000 random points per 12 LC classes from reference polygons based on observed GPS points, Uncrewed Aerial Vehicle imagery, and high-resolution satellite data. The random forest models were optimized through correlation-based filtering and recursive feature elimination with hyperparameter tuning to improve classification accuracy, validated via confusion matrices and comparisons with global and national-scale products. Our results highlight the significant role of topographic variables such as elevation and slope, along with red-edge spectral bands and spectral indices related to tillage, leaf water content, greenness, chlorophyll, and tasseled cap transformation for tropical land cover mapping. The integration of bi-seasonal datasets improved classification accuracy, particularly for challenging classes like semi-evergreen and deciduous forests. Furthermore, correlation-based filtering and recursive feature elimination reduced the variable set from 65 to 19, improving model efficiency without sacrificing accuracy. Combining these variable selection methods with hyperparameter tuning optimized the classification, providing a more reliable LC product that outperforms existing LC products and proves valuable for deforestation monitoring, forest management, biodiversity conservation, and land use studies. Full article

This review discusses the evolution and integration of open-access remote sensing technology in shoreline detection and coastal erosion monitoring through the use of Geographic Information Systems (GIS), Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAVs), and Ground Truth Data (GTD). The Sentinel-2 and Landsat 8/9 missions are highlighted as the primary core datasets due to their open-access policy, worldwide coverage, and demonstrated applicability in long-term coastal monitoring. Landsat data have allowed the detection of multi-decadal trends in erosion since 1972, and Sentinel-2 has provided enhanced spatial and temporal resolutions since 2015. Through integration with GIS programs such as the Digital Shoreline Analysis System (DSAS), AI-based processes such as sophisticated models including WaterNet, U-Net, and Convolutional Neural Networks (CNNs) are highly accurate in shoreline segmentation. UAVs supply complementary high-resolution data for localized validation, and ground truthing based on GNSS increases the precision of the produced map results. The fusion of UAV imagery, satellite data, and machine learning aids a multi-resolution approach to real-time shoreline monitoring and early warnings. Despite the developments seen with these tools, issues relating to atmosphere such as cloud cover, data fusion, and model generalizability in different coastal environments continue to require resolutions to be addressed by future studies in terms of enhanced sensors and adaptive learning approaches with the rise of AI technology the recent years. Full article

As part of the Copernicus program of the European Union (EU), the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) are currently operating the Sentinel-3 mission that consists of a constellation of two unites A and B (S3A, S3B). Each unit carries on board an Ocean and Land Colour Instrument (OLCI) that is acquiring moderate-spatial-resolution optical imagery. This article provides a description of the Level-1B radiometry product validation activities of the constellation Sentinel-3A and Sentinel-3B after six years in orbit. Several vicarious calibration methods have been applied independently by three expert groups and the results are compared over different surface target types. All methods agree on the good radiometric performance of both instruments. Although OLCI-A shows brighter Top-of-Atmosphere (TOA) radiance than OLCI-B by about 1–2%, both sensors exhibit very good stability and good image quality. The results are analyzed and discussed to propose a set of vicarious gain coefficients that could be used to align OLCI-A with OLCI-B radiometry time-series. Finally, recommendations for future missions are suggested. Full article

There has been an uninterrupted suite of ocean-color missions with global coverage since 1997, a continuity now supported by programs ensuring the launch of a series of platforms such as the Sentinel-3 missions hosting the Ocean and Land Color Imager (OLCI). The products derived from these missions should be consistent and allow the analysis of long-term multi-mission data records, particularly for climate science. In metrological terms, this agreement is expressed by compatibility, by which data from different sources agree within their stated uncertainties. The current study investigates the compatibility of remote-sensing reflectance products $R_{RS}$ derived from standard atmospheric correction algorithms applied to Sentinel-3A and -3B (S-3A and S-3B, respectively) data. For the atmospheric correction $l2gen$, validation results obtained with field data from the ocean-color component of the Aerosol Robotic Network (AERONET-OC) and uncertainty estimates appear consistent between S-3A and S-3B as well as with other missions processed with the same algorithm. Estimates of the error correlation between S-3A and S-3B $R_{RS}$, required to evaluate their compatibility, are computed based on common matchups and indicate varying levels of correlation for the various bands and sites in the interval 0.33–0.60 between 412 and 665 nm considering matchups of all sites put together. On average, validation data associated with Camera 1 of OLCI show lower systematic differences with respect to field data. In direct comparisons between S-3A and S-3B, $R_{RS}$ data from S-3B appear lower than S-3A values, which is explained by the fact that a large share of these comparisons relies on S-3B data collected by Camera 1 and S-3A data collected by Cameras 3 to 5. These differences are translated into a rather low level of metrological compatibility between S-3A and S-3B $R_{RS}$ data when compared daily. These results suggest that the creation of OLCI climate data records is challenging, but they do not preclude the consistency of time (e.g., monthly) composites, which still needs to be evaluated. Full article