Multispectral Instrument Data Research Articles

Assessment of Atmospheric Correction Algorithms for Correcting Sunglint Effects in Sentinel-2 MSI Imagery: A Case Study in Clean Lakes

The Sentinel-2 Multi-Spectral Instrument (MSI) is characterized by short revisit times (5 days), red-edge spectral bands (665 nm and 705 nm), and a high spatial resolution (10 m), making it highly suitable for monitoring water quality in both inland and coastal waters. Unlike SeaWiFS, which can adjust its viewing angles to minimize sunglint, the Sentinel-2 MSI operates with fixed near-nadir angles, which makes it more susceptible to sunglint. Additionally, the complex optical properties of water pose challenges in accurately determining its water-leaving reflectance. Therefore, we compared the effectiveness of six atmospheric correction (AC) algorithms (POLYMER, MUMM, DSF, C2RCC, BP, and GRS) in correcting sunglint using two typical lakes in Xinjiang, China, as examples. The results indicated that POLYMER achieved the highest overall evaluation score (1.61), followed by MUMM (1.21), while BP exhibited the lowest performance (0.62). Specifically, POLYMER showed robust performance at the 665 nm band with RMSE = 0.0012 sr−1, R2 = 0.74, and MAPE = 30.68%, as well as at the 705 nm band with RMSE = 0.0014 sr−1, R2 = 0.42, and MAPE = 38.44%. At the 443, 490, and 560 nm bands, MUMM showed better performance (RMSE ≤ 0.0026 sr−1, R2 ≥ 0.86, MAPE ≤ 28.20%). In terms of band ratios, POLYMER exhibited the highest accuracy (RMSE ≤ 0.093 and MAPE ≤ 22.2%), particularly for the ratio Rrs(490)/Rrs(560) (R2 = 0.71). In general, POLYMER is the best choice for the sunglint correction of Xinjiang’s clean lakes. This study assessed the capability of different AC algorithms for sunglint correction and enhanced the monitoring capability of MSI data in clean waters.

Estimation of Rice Plant Coverage Using Sentinel-2 Based on UAV-Observed Data

Vegetation coverage is a crucial parameter in agriculture, as it offers essential insight into crop growth and health conditions. The spatial resolution of spaceborne sensors is limited, hindering the precise measurement of vegetation coverage. Consequently, fine-resolution ground observation data are indispensable for establishing correlations between remotely sensed reflectance and plant coverage. We estimated rice plant coverage per pixel using time-series Sentinel-2 Multispectral Instrument (MSI) data, enabling the monitoring of rice growth conditions over a wide area. Coverage was calculated using unmanned aerial vehicle (UAV) data with a spatial resolution of 3 cm with the spectral unmixing method. Coverage maps were generated every 2–3 weeks throughout the rice-growing season. Subsequently, crop growth was estimated at 10 m resolution through multiple linear regression utilizing Sentinel-2 MSI reflectance data and coverage maps. In this process, a geometric registration of MSI and UAV data was conducted to improve their spatial agreement. The coefficients of determination (R2) of the multiple linear regression models were 0.92 and 0.94 for the Level-1C and Level-2A products of Sentinel-2 MSI, respectively. The root mean square errors of estimated rice plant coverage were 10.77% and 9.34%, respectively. This study highlights the promise of satellite time-series models for accurate estimation of rice plant coverage.

Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, Ethiopia

Effective urban planning and management rely on accurate land cover mapping, which can be achieved through the combination of remote sensing data and machine learning algorithms. This study aimed to explore and demonstrate the potential benefits of integrating Sentinel-1 SAR and Sentinel-2 MSI satellite imagery for urban land cover classification in Gondar city, Ethiopia. Synthetic Aperture Radar (SAR) data from Sentinel-1A and Multispectral Instrument (MSI) data from Sentinel-2B for the year 2023 were utilized for this research work. Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms were utilized for the classification process. Google Earth Engine (GEE) was used for the processing, classification, and validation of the remote sensing data. The findings of the research provided valuable insights into the performance evaluation of the Support Vector Machine (SVM) and Random Forest (RF) algorithms for image classification using different datasets, namely Sentinel 2B Multispectral Instrument (MSI) and Sentinel 1A Synthetic Aperture Radar (SAR) data. When applied to the Sentinel 2B MSI dataset, both SVM and RF achieved an overall accuracy (OA) of 0.69, with a moderate level of agreement indicated by the Kappa score of 0.357. For the Sentinel 1A SAR data, SVM maintained the same OA of 0.69 but showed an improved Kappa score of 0.67, indicating its suitability for SAR image classification. In contrast, RF achieved a slightly lower OA of 0.66 with Sentinel 1A SAR data. However, when the datasets of Sentinel 2B MSI and Sentinel 1A SAR were combined, SVM achieved an impressive OA of 0.91 with a high Kappa score of 0.80, while RF achieved an OA of 0.81 with a Kappa score of 0.809. These findings highlight the potential of fusing satellite data from multiple sources to enhance the accuracy and effectiveness of image classification algorithms, making them valuable tools for various applications, including land use mapping and environmental monitoring.

GF-1 WFV Surface Reflectance Quality Evaluation in Countries along “the Belt and Road”

The GaoFen-1 wide field of view (GF-1 WFV) has produced level 1 digital number data globally; however, most applications have focused on China, and data quality outside China has not been validated. This study presents a preliminary assessment of the 2020 GF-1 WFV surface reflectance data for Nepal, Azerbaijan, Kenya, and Sri Lanka along “the Belt and Road” route using Sentinel-2 Multi-Spectral Instrument (MSI), Landsat-8 Operational Land Image (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A method for obtaining the GF-1 WFV surface reflectance data was also proposed, with steps including atmospheric correction, cross-radiation calibration, and bidirectional reflectance distribution function correction. The results showed that WFV surface reflectance data was not significantly different from MSI, OLI, and MODIS surface reflectance data. In the visible and near-infrared bands, for most landcover types, the bias was less than 0.02, and the precision and root mean square error were less than 0.04. When the landcover types were forest and water, the MSI, OLI, and MODIS surface reflectance data were higher than that of WFV in the near-infrared band. The results of this study provide a basis for assessing the global application potential of GF-1 WFV.

Semi-Supervised Urban Change Detection Using Multi-Modal Sentinel-1 SAR and Sentinel-2 MSI Data

Urbanization is progressing at an unprecedented rate in many places around the world. The Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions, combined with deep learning, offer new opportunities to accurately monitor urbanization at a global scale. Although the joint use of SAR and optical data has recently been investigated for urban change detection, existing data fusion methods rely heavily on the availability of sufficient training labels. Meanwhile, change detection methods addressing label scarcity are typically designed for single-sensor optical data. To overcome these limitations, we propose a semi-supervised urban change detection method that exploits unlabeled Sentinel-1 SAR and Sentinel-2 MSI data. Using bitemporal SAR and optical image pairs as inputs, the proposed multi-modal Siamese network predicts urban changes and performs built-up area segmentation for both timestamps. Additionally, we introduce a consistency loss, which penalizes inconsistent built-up area segmentation across sensor modalities on unlabeled data, leading to more robust features. To demonstrate the effectiveness of the proposed method, the SpaceNet 7 dataset, comprising multi-temporal building annotations from rapidly urbanizing areas across the globe, was enriched with Sentinel-1 SAR and Sentinel-2 MSI data. Subsequently, network performance was analyzed under label-scarce conditions by training the network on different fractions of the labeled training set. The proposed method achieved an F1 score of 0.555 when using all available training labels, and produced reasonable change detection results (F1 score of 0.491) even with as little as 10% of the labeled training data. In contrast, multi-modal supervised methods and semi-supervised methods using optical data failed to exceed an F1 score of 0.402 under this condition. Code and data are made publicly available.

Lake Cyanobacterial Bloom Color Recognition and Spatiotemporal Monitoring with Google Earth Engine and the Forel-Ule Index

Cyanobacterial blooms represent a significant environmental problem, threatening aquatic ecosystems worldwide. Caused by the eutrophication of water bodies and global climate change, these blooms have altered freshwater ecosystems worldwide during recent decades. Although cyanobacterial blooms are typically caused by blue-green cyanobacteria, which derive their color from the phycocyanin pigment, other pigmented cyanobacterial blooms have been frequently observed in water bodies. These blooms pose a serious environmental threat to inland waters, endangering global public health and aquatic ecosystems. Therefore, comprehending the mechanism of color variation in cyanobacterial blooms is crucial for revealing the outbreak mechanism and implementing effective prevention and control measures. This study developed a human–machine interactive workflow for extracting cyanobacterial blooms and recognizing their colors based on the Forel-Ule index and Sentinel-2 MultiSpectral Instrument data. Using this workflow, the authors conducted spatiotemporal analysis and statistical analysis of bloom color for cyanobacterial blooms in four typical eutrophic lakes from 2019 to 2022. The findings indicated a declining trend in cyanobacterial blooms across the four studied lakes over the years, among which Hulun Lake experienced an annual increase in cyanobacterial blooms and emerged as the lake with the most severe outbreak of such blooms in 2022. The yellowing status of cyanobacterial blooms varied among the four lakes, with Taihu Lake and Dianchi Lake exhibiting a relatively high proportion of green-yellow and yellow cyanobacterial blooms, followed by Chaohu Lake, whereas Hulun Lake had the lowest occurrence. The workflow developed in this study was implemented in Google Earth Engine and provided an automated, integrated, and rapid monitoring solution for the long-term monitoring and color recognition of cyanobacterial blooms.

A spatiotemporal monitoring model of TSM and TDS in arid region lakes utilizing Sentinel-2 imagery

The assessment of lake water quality is crucial for protecting and managing natural resources. Total suspended matter (TSM) and total dissolved solids (TDS) are significant parameters in evaluating lake water quality, and their concentration levels provide useful information about the health of the ecosystem. However, measuring TSM and TDS concentrations in remote arid areas can be challenging, necessitating the development of a simple, efficient, and cost-effective monitoring system. In this study, the aim was to retrieve TDS and TSM concentrations in the arid region using an empirical and physics-based method simultaneously. Measured data and remote sensing images for Chah-Nimeh Reservoirs (CNRs), Iran, were used to establish the TSM and TDS concentration model. Twenty-seven Sentinel-2 multispectral instrument (MSI) data acquired from 2018 to 2021 were employed to determine the spatiotemporal distribution of TDS and TSM concentrations. The results indicate that Sentinel-2 MSI data is a productive tool for retrieving TSM concentration using the Case-2 Regional/Coastal Color (C2RCC) processor, with a high level of performance in the dry ecosystem of CNRs (R2 = 0.92). Furthermore, the Bayesian regularization artificial neural network (BRANN) algorithm was used to evaluate TDS concentration, and the results demonstrated that the BRANN algorithm outperforms other models, with a high level of performance (R2 = 0.89). It is noteworthy that evaluating TDS concentration is generally more challenging than TSM. The study provides a valuable model for monitoring TSM and TDS concentrations in remote arid areas using Sentinel-2 MSI data. The proposed approach can facilitate effective spatiotemporal monitoring of TSM and TDS concentrations and aid in the evaluation of lake water quality, specifically in regions where in-situ measurements are not feasible. The findings have significant implications for the management and conservation of natural resources in arid and remote regions.

Hydrological regime of Sahelian small waterbodies from combined Sentinel-2 MSI and Sentinel-3 Synthetic Aperture Radar Altimeter data

Abstract. In the Sahelian semi-arid region, water resources, especially small waterbodies such as ponds, small lakes, and reservoirs in rural areas are of vital importance. However, because of their high number and the scarce in situ monitoring networks, these resources and their spatiotemporal variability are not well known at the regional scale. This study investigates the hydrological regime of 37 small waterbodies, located in Mali, Niger, and Burkina Faso in central Sahel. We propose a method based on remote sensing data only, which consists of combining water height data from Sentinel-3 Synthetic Aperture Radar Altimeter (SRAL) with water area data obtained with the Sentinel-2 MultiSpectral Instrument (MSI) to create a dense water height time series. Water height variations are then compared to the evaporation estimated by the Penman–Monteith method, using ERA5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF) to infer water regimes during the dry season. Three main regimes stand out, namely a net water loss, mainly resulting from anthropogenic withdrawals, a net water supply occurring after the end of the rainy season through river network or water table exchange, and a balanced behaviour, where water losses during the dry season closely correspond to evaporation rates. Spatial patterns have been identified; in central Burkina Faso, most of the reservoirs show a net dry season water loss, which is explained by frequent irrigation, while reservoirs in northern Burkina Faso, generally show little water loss, indicating that water withdrawal is not significant in this area. Lakes located in the Inner Niger Delta in Mali and connected to the Niger River network generally show an important water supply, particularly at the beginning of the dry season. Lakes in Niger tend to show a weak signal toward water inflow that could be explained by exchange processes with the groundwater. These results show that satellite data are effective at estimating hydrological regimes and the anthropogenic impact on water resources at the large scale, including resources found in small waterbodies.

Mapping the forage nitrogen, phosphorus, and potassium contents of alpine grasslands by integrating Sentinel-2 and Tiangong-2 data

Nitrogen (N), phosphorus (P), and potassium (K) contents are crucial quality indicators for forage in alpine natural grasslands and are closely related to plant growth and reproduction. One of the greatest challenges for the sustainable utilization of grassland resources and the development of high-quality animal husbandry is to efficiently and accurately obtain information about the distribution and dynamic changes in N, P, and K contents in alpine grasslands. A new generation of multispectral sensors, the Sentinel-2 multispectral instrument (MSI) and Tiangong-2 moderate-resolution wide-wavelength imager (MWI), is equipped with several spectral bands suitable for specific applications, showing great potential for mapping forage nutrients at the regional scale. This study aims to achieve high-accuracy spatial mapping of the N, P, and K contents in alpine grasslands at the regional scale on the eastern Qinghai-Tibet Plateau. The Sentinel-2 MSI and Tiangong-2 MWI data, coupled with multiple feature selection algorithms and machine learning models, are applied to develop forage N, P, and K estimation models from data collected at 92 sample sites ranging from the vigorous growth stage to the senescent stage. The results show that the spectral bands of both the Sentinel-2 MSI and Tiangong-2 MWI have an excellent performance in estimating the forage N, P, and K contents (the R2 values are 0.68–0.76, 0.54–0.73, and 0.74–0.82 for forage N, P, and K estimations, respectively). Moreover, the model integrating the spectral bands of these two sensors explains 78%, 74%, and 84% of the variations in the forage N, P, and K contents, respectively. These results indicate that the estimation ability of forage nutrients can be further improved by integrating Tiangong-2 MWI and Sentinel-2 MSI data. In conclusion, integration of the spectral bands of multiple sensors is a promising approach to map the forage N, P, and K contents in alpine grasslands with high accuracy at the regional scale. This study offers valuable information for growth monitoring and real-time determination of forage quality in alpine grasslands.

Sentinel-2 MSI Observations of Water Clarity in Inland Waters across Hainan Island and Implications for SDG 6.3.2 Evaluation

Freshwater on islands represents a precious resource and highly vulnerable ecosystem. For monitoring freshwater, satellite remote sensing is efficient and has large-scale application. This study proposed a modified model of the quasi-analytical algorithm (ZSD-QAAv6m) to retrieve the water clarity of inland waters (>1 km2) across Hainan Island, China using Sentinel-2 multispectral instrument data. By adjusting the threshold of Rrs(665), the proposed model could accurately estimate water clarity with diverse optical properties on the island and avoid underestimation in moderately clear waters. Based upon this, the first spatiotemporal analysis of recent water clarity in Hainan Island was conducted. The results show that lake water clarity in the central region was generally higher (with average value of 1.4 m) than that of coastal regions (with average value of 1.2 m). Seasonally, the water clarity during the wet season was usually lower than that in the dry season, with average values of 1.1 m and 1.3 m across the island respectively. From 2017 to 2021, the proportion of water bodies with water clarity > 0.5 m increased from 60% to 100%. The overall spatial pattern of water clarity was correlated to the regional vegetation cover in Hainan Island, with higher clarity associated with higher vegetation cover in the central regions. The seasonal variation of water clarity may be attributed to heavy rainfall and runoff during the wet season; while the distinct annual variation may be benefited from the strengthened surface water protections in Hainan Province in recent years. This study provides a practical approach for evaluating the SDG 6.3.2 indicator in Hainan Island using remote sensed water clarity as a comprehensive water quality indicator and the findings could facilitate the island’s water resource management and conservation.

Multi-Temporal Sentinel-1 SAR and Sentinel-2 MSI Data for Flood Mapping and Damage Assessment in Mozambique

Floods are one of the most frequent natural disasters worldwide. Although the vulnerability varies from region to region, all countries are susceptible to flooding. Mozambique was hit by several cyclones in the last few decades, and in 2019, after cyclones Idai and Kenneth, the country became the first one in southern Africa to be hit by two cyclones in the same raining season. Aiming to provide the local authorities with tools to yield better responses before and after any disaster event, and to mitigate the impact and support in decision making for sustainable development, it is fundamental to continue investigating reliable methods for disaster management. In this paper, we propose a fully automated method for flood mapping in near real-time utilizing multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data acquired in the Beira municipality and Macomia district. The procedure exploits the processing capability of the Google Earth Engine (GEE) platform. We map flooded areas by finding the differences of images acquired before and after the flooding and then use Otsu’s thresholding method to automatically extract the flooded area from the difference image. To validate and compute the accuracy of the proposed technique, we compare our results with the Copernicus Emergency Management Service (Copernicus EMS) data available in the study areas. Furthermore, we investigated the use of a Sentinel-2 multi-spectral instrument (MSI) to produce a land cover (LC) map of the study area and estimate the percentage of flooded areas in each LC class. The results show that the combination of Sentinel-1 SAR and Sentinel-2 MSI data is reliable for near real-time flood mapping and damage assessment. We automatically mapped flooded areas with an overall accuracy of about 87–88% and kappa of 0.73–0.75 by directly comparing our prediction and Copernicus EMS maps. The LC classification is validated by randomly collecting over 600 points for each LC, and the overall accuracy is 90–95% with a kappa of 0.80–0.94.

Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI Data

The present research aims at verifying whether there are significant differences between Land Use/Land Cover (LULC) classifications performed using Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data—abbreviated as L8 and S2. To comprehend the degree of accuracy between these classifications, both L8 and S2 scenes covering the study area located in the Basilicata region (Italy) and acquired within a couple of days in August 2017 were considered. Both images were geometrically and atmospherically corrected and then resampled at 30 m. To identify the ground truth for training and validation, a LULC map and a forest map realized by the Basilicata region were used as references. Then, each point was verified through photo-interpretation using the orthophoto AGEA 2017 (spatial resolution of 20 cm) as a ground truth image and, only in doubtful cases, a direct GPS field survey. MLC and SVM supervised classifications were applied to both types of images and an error matrix was computed using the same reference points (ground truth) to evaluate the classification accuracy of different LULC classes. The contribution of S2′s red-edge bands in improving classifications was also verified. Definitively, ML classifications show better performance than SVM, and Landsat data provide higher accuracy than Sentinel-2.

Performances of Atmospheric Correction Processors for Sentinel-2 MSI Imagery Over Typical Lakes Across China

The launched MultiSpectral Instrument (MSI) equipped on Sentinel-2 satellites offers a powerful tool for observing the biogeochemical parameters of inland waters on a large scale. The proper use of atmospheric correction processors is essential for acquiring accurate satellite remote-sensing reflectance and downstream products. Therefore, we compared the performances of typical atmospheric correction processors, such as Sen2Cor, C2RCC-nets, C2RCC-C2X, Acolite, iCOR, Polymer, SeaDas/l2gen, and 6S processors, for MSI imagery over lake groups ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> = 296) across China collected from 2016 to 2020. Linear fitting between corrected reflectance and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> spectral measurements was used to assess performance; for the single lake, we additionally evaluated the performance of atmospheric correction processors in typical Chagan Lake in 2021. For large-scale lake groups with different water quality backgrounds, the SeaDas/l2gen and C2RCC processors performed best for all band match-ups, and the C2RCC processor had the smallest errors. The SeaDas/l2gen processor works well for the signal bands (490, 560, 665, 704, and 740 nm), followed by the signal bands (560, 665, and 704 nm) of the C2RCC processor. For large-scale observations, this study revealed that Sentinel-2 satellite optical MSI imagery related to the C2RCC processor can be used to monitor aquatic systems with high-frequency investigations. For the signal band, the SeaDas/l2gen processor was used to select potential match-ups for the availability of MSI data related to the empirical models of processors. Our results may help satellite users select appropriate atmospheric correction processors for large-scale lake observations.

Evaluation of remote sensing and modeled chlorophyll-a products of the Baltic Sea

The Baltic Sea is an optically very complex study object for watercolor remote sensing because of the high quantity of colored dissolved organic matter, two optically distinct phytoplankton seasons, high variability in concentrations of optically active substances, and low sun angles. Despite this, there are numerous remote sensing and modeled chlorophyll-a (Chl-a) products publicly available for the Baltic Sea. Sixteen openly accessible Chl-a products were tested with 267 in situ Chl-a measurements that were carried out in Estonian marine waters during 2016 to 2021. All modeled products and about half of the remote sensing products failed to produce reliable results. The best-performing remote sensing Chl-a product was Case2/Regional CoastColour produced from Sentinel-3 ocean and land color imager (OLCI) reflectance with R2 = 0.55, root mean squared error ( RMSE ) = 4.5 mg m − 3, mean absolute percentage error (MAPE) = 74%. In addition, eight different band ratio algorithms were applied on Sentinel-3 OLCI and Sentinel-2 multispectral instrument data. The best remote sensing band ratio algorithm was derived from top-of-atmosphere reflectance of Sentinel-3 data using 665, 709, and 754 nm bands (R2 = 0.67, RMSE = 3.9 mg m − 3, and MAPE = 63%). Our results show good suitability of Sentinel-3 for Chl-a retrieval. However, the high uncertainties suggest for the further product development and validation needs.

Physical-Based Spatial-Spectral Deep Fusion Network for Chlorophyll-a Estimation Using MODIS and Sentinel-2 MSI Data

Satellite-derived Chlorophyll-a (Chl-a) is an important environmental evaluation indicator for monitoring water environments. However, the available satellite images either have a coarse spatial or low spectral resolution, which restricts the applicability of Chl-a retrieval in coastal water (e.g., less than 1 km from the shoreline) for large- and medium-sized lakes/oceans. Considering Lake Chaohu as the study area, this paper proposes a physical-based spatial-spectral deep fusion network (PSSDFN) for Chl-a retrieval using Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 Multispectral Instrument (MSI) reflectance data. The PSSDFN combines residual connectivity and attention mechanisms to extract effective features, and introduces physical constraints, including spectral response functions and the physical degradation model, to reconcile spatial and spectral information. The fused and MSI data were used as input variables for collaborative retrieval, while only the MSI data were used as input variables for MSI retrieval. Combined with the Chl-a field data, a comparison between MSI and collaborative retrieval was conducted using four machine learning models. The results showed that collaborative retrieval can greatly improve the accuracy compared with MSI retrieval. This research illustrates that the PSSDFN can improve the estimated accuracy of Chl-a for coastal water (less than 1 km from the shoreline) in large- and medium-sized lakes/oceans.

Deep learning models to map an agricultural expansion area with MODIS and Sentinel-2 time series images

Mapping changing land use and land cover (LULC) is important for land management and environment analysis. We tried to build deep learning models to classify LULC over time at an agricultural expansion area in Matopiba region, Brazil with MCD43A4 V006 moderate resolution imaging spectroradiometer (MODIS), and Sentinel-2 multispectral instrument (MSI) time series data. We collected time series MODIS data and Sentinel-2 A/B MSI data from 2015 to 2020 and prepared small patches with containing blue, green, red, near-infrared, and shortwave-infrared-1 bands as features. Then the both datasets were used to build and train the convolutional neural network (CNN) model, the CNN gate recurrent unit (CNN-GRU) model, and the CNN long short-term memory (CNN-LSTM) model, respectively. We evaluated these three trained models with ground truth data, and the CNN-LSTM model (overall accuracy: 91.29% from MODIS data and 89.47% from Sentinel-2 data) was better than the CNN-GRU model (overall accuracy: 89.19% from MODIS data and 88.61% from Sentinel-2 data) and the CNN model (overall accuracy: 89.17% from MODIS data and 86.02% from Sentinel-2 data). Our results also showed that the accuracy from cropland and savanna classes were higher than grassland and forest classes in all three models. These two classes generated from the CNN-LSTM model performed better than the other two deep learning models. The results from these two datasets indicated that the methods were reliable for both coarse and medium spatial resolution satellite images and time series remote sensing images worked better than single image for classification problems when considering LULC change over time. The results also provided an alternative way to prepare input data from satellite images for deep learning models. Furthermore, the classification results of the whole agricultural expansion area were reasonable and it can be used as an additional dataset for further environmental analysis at a regional scale.

Chlorophyll-a concentrations in the Arabian Gulf waters of arid region: A case study from the northern coast of Qatar

Remote sensing of spectrally active Chlorophyll-a (Chl-a) in the gulf water of an arid region is important to understand the spatial and temporal variations of phytoplankton and biomass in the water. This study characterizes the spectral absorption of Chl-a and detects and maps the Chl-a of Al Arish–Al Ghariyah coastal region of northern Qatar using the data of Hyperion of EO-1, MultiSpectral Instrument (MSI) of Sentinel-2, and Operational Land Imager (OLI) of Landsat-8 satellites. The study of image spectra of Chl-a of coastal water of the Al Ghariyah region using the Hyperion image showed spectral band absorptions near 450 and 475 nm in the blue region and around 640, 730, 760, and 830 nm in the red region. The analysis of Hyperion data using image spectra by linear spectral unmixing (LSU) method showed the occurrence of Chl-a in very shallow and shallow water. The mapping of Chl-a of the Al Arish–Al​ Ghariyah coastal region was carried out using MSI and OLI data by NDCI, 2BDA, 3BDA, and FLH violet algorithms, which showed the concentrations and distributions of the Chl-a in the region. The performance of the algorithms was studied using WorldView-3 data, which provided the R2 values of 60% and the best suitability of the NDCI algorithm and MSI data to map the concentration of Chl-a. All the results were validated using field measurements, in-situ measurements, and laboratory analyses of field samples. The measurements of Chl-a showed the occurrence of poor occurrence of Chl-a and confirmed the imagery results. The remote sensing results with physical and chemical parameters of water samples allowed us to assess Chl-a concentrations in the region. This study evaluates the sensor’s capability and applications of satellite data and algorithms to map concentrations of Chl-a in the Gulf waters of the arid region.

Unsupervised domain adaptation for global urban extraction using Sentinel-1 SAR and Sentinel-2 MSI data

Accurate and up-to-date maps of built-up areas are crucial to support sustainable urban development. Earth Observation (EO) is a valuable data source to cover this demand. In particular, Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions offer new opportunities to map built-up areas on a global scale. Using Sentinel-2 images, recent urban mapping efforts achieved promising results by training Convolutional Neural Networks (CNNs) on available built-up data. However, these results strongly depend on the availability of local reference data for fully supervised training or assume that the application of CNNs to unseen areas (i.e. across-region generalization) produces satisfactory results. To alleviate these shortcomings, it is desirable to leverage Semi-Supervised Learning (SSL) algorithms that can take advantage of unlabeled data, especially because satellite data is plentiful. In this paper, we propose a novel Domain Adaptation (DA) approach using SSL that jointly exploits Sentinel-1 SAR and Sentinel-2 MSI to improve across-region generalization for built-up area mapping. Specifically, two identical sub-networks are incorporated into the proposed model to perform built-up area segmentation from SAR and optical images separately. Assuming that consistent built-up area segmentation should be obtained across data modality, we design an unsupervised loss for unlabeled data that penalizes inconsistent segmentation from the two sub-networks. Therefore, we propose to use complementary data modalities as real-world perturbations for consistency regularization. For the final prediction, the model takes both data modalities into account. Experiments conducted on a test set comprised of sixty representative sites across the world showed that the proposed DA approach achieves strong improvements (F1 score 0.694) over fully supervised learning from Sentinel-1 SAR data (F1 score 0.574), Sentinel-2 MSI data (F1 score 0.580) and their input-level fusion (F1 score 0.651). To demonstrate the effectiveness of DA, we also performed a comparison with two state-of-the-art products, namely GHS-BUILT-S2 and WSF 2019, on the test set. The comparison showed that our model is capable of producing built-up area maps with comparable or even better quality than the state-of-the-art global human settlement maps. Therefore, the multi-modal DA offers great potential to be adapted to produce easily updateable human settlements maps at a global scale.

Monitoring urbanization and environmental impact in Kigali, Rwanda using Sentinel-2 MSI data and ecosystem service bundles

• U-Net achieved good performance in urban imperviousness density extraction from Sentinel-2 MSI data. • The hybrid approach using Random Forest and U-Net improved urban land cover classification accuracies. • The expansion of built-up areas resulted in high demand for provisioning and regulating ecosystem services. • The decrease in cropland and forest negatively affected ecosystem service supply. • The framework using Sentinel-2 MSI data and ES bundles is effective for urbanization monitoring and environmental impact. Rapid urbanization in developing countries often results in uncontrolled urban growth. In order to support sustainable urban development, reliable and up-to-date information on urban land cover changes and their environmental impact is needed. In this study, we aim at evaluating the potential of Sentinel-2 (S-2) Multispectral Instrument (MSI) data for urban land cover change monitoring and for analyzing resulting impacts on Ecosystem Services (ES) provision in Kigali, Rwanda. Land cover classification into eight distinct urban classes (84% overall accuracies, 0.8 Kappa) was performed on data from 2016 and 2021 using a hybrid approach combining Random Forest with a U-Net-based impervious surface segmentation that improved the delineation of urban areas. The bi-temporal land cover maps were then analyzed regarding landscape structure using Landscape Metrics (LM). Ecosystem service bundles were derived for both years and their changes were summarized. Service providing areas were further evaluated in terms of changes in spatial attributes and structure of patches. ES were aggregated into eight bundles and grouped into provisioning, regulating and supporting services. The bundles were further analyzed using a matrix spatially linking landscape units with service supply and demand budgets. The results illustrated that three urban development scenarios can be distinguished including infill through housing and infrastructures development in core urban areas, urban sprawl in fringe zones and the development of urban patches at distant locations intercepted by cropland. The results revealed that the changes in LM negatively affected ES supply mainly through a decrease in cropland and forest. The expansion of built-up areas resulted in a high demand for provisioning and regulating services, especially food and water provision, surface runoff mitigation and erosion control. This is the first study demonstrating that detailed monitoring of urbanization and resulting environmental impacts can be performed with open access S-2 MSI data in Sub-Saharan Africa. Moreover, the framework developed in this study has the potential to be transferred to other Sub-Saharan cities.

Oyster Aquaculture Site Selection Using High-Resolution Remote Sensing: A Case Study in the Gulf of Maine, United States

Aquaculture of the eastern oyster, Crassostrea virginica, is an expanding industry in the US, particularly in the Gulf of Maine. High resolution ocean color satellites launched in the last decade potentially provide aquaculture-relevant water-quality parameters at farm scales. However, these parameters, such as temperature, suspended particulate matter (SPM), and Chlorophyll a (Chl a), need to be derived by interested users. Water quality parameters are derived first by applying an atmospheric correction and then estimating the target parameter with a specific algorithm. Here, we use five atmospheric correction schemes and two algorithms to derive SPM and Chl a from the Sentinel 2A&amp;amp;B satellites’ multispectral instrument data. The best estimates of SPM and Chl a are determined by comparison with in situ observations from buoys. Together with SST from Landsat-8, we estimated an Oyster Suitability Index (OSI) along the transects in five estuaries in the Gulf of Maine as well as applied a novel particulate organic matter algorithm, a function of Chl a and SPM in low turbidity estuaries. We then apply the optimal approaches to derive water quality parameters to study five different estuaries in Maine and find that existing high-yield oyster aquaculture farms are found in areas with elevated OSI values. Additionally, we suggest new areas, currently under-exploited, where oyster aquaculture is likely to succeed, showcasing the utility of the approach.