Sentinel-2 Images Research Articles

MSMTRIU-Net: Deep Learning-Based Method for Identifying Rice Cultivation Areas Using Multi-Source and Multi-Temporal Remote Sensing Images

Identifying rice cultivation areas in a timely and accurate manner holds great significance in comprehending the overall distribution pattern of rice and formulating agricultural policies. The remote sensing observation technique provides a convenient means to monitor the distribution of rice cultivation areas on a large scale. Single-source or single-temporal remote sensing images are often used in many studies, which makes the information of rice in different types of images and different growth stages hard to be utilized, leading to unsatisfactory identification results. This paper presents a rice cultivation area identification method based on a deep learning model using multi-source and multi-temporal remote sensing images. Specifically, a U-Net based model is employed to identify the rice planting areas using both the Landsat-8 optical dataset and Sentinel-1 Polarimetric Synthetic Aperture Radar (PolSAR) dataset; to take full into account of the spectral reflectance traits and polarimetric scattering traits of rice in different periods, multiple image features from multi-temporal Landsat-8 and Sentinel-1 images are fed into the network to train the model. The experimental results on China’s Sanjiang Plain demonstrate the high classification precisions of the proposed Multi-Source and Multi-Temporal Rice Identification U-Net (MSMTRIU-NET) and that inputting more information from multi-source and multi-temporal images into the network can indeed improve the classification performance; further, the classification map exhibits greater continuity, and the demarcations between rice cultivation regions and surrounding environments reflect reality more accurately.

Vegetative Index Intercalibration Between PlanetScope and Sentinel-2 Through a SkySat Classification in the Context of “Riserva San Massimo” Rice Farm in Northern Italy

Rice farming in Italy accounts for about 50% of the EU’s rice area and production. Precision agriculture has entered the scene to enhance sustainability, cut pollution, and ensure food security. Various studies have used remote sensing tools like satellites and drones for multispectral imaging. While Sentinel-2 is highly regarded for precision agriculture, it falls short for specific applications, like at the “Riserva San Massimo” (Gropello Cairoli, Lombardia, Northern Italy) rice farm, where irregularly shaped crops need higher resolution and frequent revisits to deal with cloud cover. A prior study that compared Sentinel-2 and the higher-resolution PlanetScope constellation for vegetative indices found a seasonal miscalibration in the Normalized Difference Vegetation Index (NDVI) and in the Normalized Difference Red Edge Index (NDRE). Dr. Agr. G.N. Rognoni, a seasoned agronomist working with this farm, stresses the importance of studying the radiometric intercalibration between the PlanetScope and Sentinel-2 vegetative indices to leverage the knowledge gained from Sentinel-2 for him to apply variable rate application (VRA). A high-resolution SkySat image, taken almost simultaneously with a pair of Sentinel-2 and PlanetScope images, offered a chance to examine if the irregular distribution of vegetation and barren land within rice fields might be a factor in the observed miscalibration. Using an unsupervised pixel-based image classification technique on SkySat imagery, it is feasible to split rice into two subclasses and intercalibrate them separately. The results indicated that combining histograms and agronomists’ expertise could confirm SkySat classification. Moreover, the uneven spatial distribution of rice does not affect the seasonal miscalibration object of past studies, which can be adjusted using the methods described here, even with images taken four days apart: the first method emphasizes accuracy using linear regression, histogram shifting, and histogram matching; whereas the second method is faster and utilizes only histogram matching.

Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network

The detection of complex formations, initially suspected to be oil spills, is investigated using atmospherically corrected multispectral satellite images and deep learning techniques. Several formations have been detected in an inland lake in Northern Greece. Four atmospheric corrections (ACOLITE, iCOR, Polymer, and C2RCC) that are specifically designed for water applications are examined and implemented on Sentinel-2 multispectral satellite images to eliminate the influence of the atmosphere. Out of the four algorithms, iCOR and ACOLITE are able to depict the formations sufficiently; however, the latter is chosen for further processing due to fewer uncertainties in the depiction of these formations as anomalies across the multispectral range. Furthermore, a number of formations are annotated at the pixel level for the 10 m bands (red, green, blue, and NIR), and a deep neural network (DNN) is trained and validated. Our results show that the four-band configuration provides the best model for the detection of these complex formations. Despite not being necessarily related to oil spills, studying these formations is crucial for environmental monitoring, pollution detection, and the advancement of remote sensing techniques.

Flooded area detection and mapping from Sentinel-1 imagery. Complementary approaches and comparative performance evaluation

ABSTRACT The current study assesses the performance of several machine learning (ML) and deep learning (DL) models for detecting and mapping floods using Sentinel-1 SAR imagery. Three distinct approaches were used: pixel classification, object-based image analysis and object instance segmentation. The ML models are Random Forest, and Support Vector Machine and the DL models are U-NET, DeepLabV3 and Mask RCNN. Five different case studies were selected to test the models’ scalability. These areas are in Romania (Prut River, at the border between Romania, the Republic of Moldova and Ukraine, Timiș River, and Râul Negru River), the United States of America (Missouri River) and Australia (Broughton Creek). For each flood, five Sentinel-1 images were used, four collected before the flood and one collected after the flood. The intensity images were stacked and scaled in the range of the intensity thresholds associated with water and non-water so that all the case studies have the same margins for intensity. Samples of water, vegetation, agricultural fields, and bare soil were collected only from the Prut River case study and used in the training process. Out of all models, the U-Net model returned the highest accuracy with a value for Intersect over Union of 0.763 for a tile size of 128x128 pixels.

Monitoring Salinity in Inner Mongolian Lakes Based on Sentinel-2 Images and Machine Learning

Salinity is an essential parameter for evaluating water quality and plays a crucial role in maintaining the stability of lake ecosystems, particularly in arid and semi-arid climates. Salinity responds to changes in climate and human activity, with significant impacts on water quality and ecosystem services. In this study, Sentinel-2A/B Multi-Spectral Instrument (MSI) images and quasi-synchronous field data were utilized to estimate lake salinity using machine learning approaches (i.e., XGB, CNN, DNN, and RFR). Atmospheric correction for MSI images was tested using six processors (ACOLITE, C2RCC, POLYMER, MUMM, iCOR, and Sen2Cor). The most accurate model and atmospheric correction method were found to be the extreme gradient boosting tree combined with the ACOLITE correction algorithm. These were used to develop a salinity model (N = 70, mean absolute percentage error = 9.95%) and applied to eight lakes in Inner Mongolia from 2016 to 2024. Seasonal and interannual variations were explored, along with an examination of potential drivers of salinity changes over time. Average salinities in the autumn and spring were higher than in the summer. The highest salinities were observed in the lake centers and tended to be consistent and homogeneous. Interannual trends in salinity were evident in several lakes, influenced by evaporation and precipitation. Climate factors were the primary drivers of interannual salinity trends in most lakes.

Estimation of Urban Tree Chlorophyll Content and Leaf Area Index Using Sentinel-2 Images and 3D Radiative Transfer Model Inversion

Urban trees play an important role in mitigating effects of climate change and provide essential ecosystem services. However, the urban environment can stress trees, requiring the use of effective monitoring methods to assess their health and functionality. The objective of this study, which focused on four deciduous tree species in Rennes, France, was to evaluate the ability of hybrid inversion models to estimate leaf chlorophyll content (LCC), leaf area index (LAI), and canopy chlorophyll content (CCC) of urban trees using eight Sentinel-2 (S2) images acquired in 2021. Simulations were performed using the 3D radiative transfer model DART, and the hybrid inversion models were developed using machine-learning regression algorithms (random forest (RF) and gaussian process regression). Model performance was assessed using in situ measurements, and relations between satellite data and in situ measurements were investigated using spatial allocation (SA) methods at the pixel and tree scales. The influence of including environment features (EFs) as model inputs was also assessed. The results indicated that random forest models that included EFs and used the pixel-scale SA method were the most accurate with R2 values of 0.33, 0.29, and 0.46 for LCC, LAI, and CCC, respectively, with notable variability among species.

Above ground biomass estimation in the upper Blue Nile basin forests, North-Western Ethiopia

Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management.

TSAE-UNet: A Novel Network for Multi-Scene and Multi-Temporal Water Body Detection Based on Spatiotemporal Feature Extraction

The application of remote sensing technology in water body detection has become increasingly widespread, offering significant value for environmental monitoring, hydrological research, and disaster early warning. However, the existing methods face challenges in multi-scene and multi-temporal water body detection, including the diverse variations in water body shapes and sizes that complicate detection; the complexity of land cover types, which easily leads to false positives and missed detections; the high cost of acquiring high-resolution images, limiting long-term applications; and the lack of effective handling of multi-temporal data, making it difficult to capture the dynamic changes in water bodies. To address these challenges, this study proposes a novel network for multi-scene and multi-temporal water body detection based on spatiotemporal feature extraction, named TSAE-UNet. TSAE-UNet integrates convolutional neural networks (CNN), depthwise separable convolutions, ConvLSTM, and attention mechanisms, significantly improving the accuracy and robustness of water body detection by capturing multi-scale features and establishing long-term dependencies. The Otsu method was employed to quickly process Sentinel-1A and Sentinel-2 images, generating a high-quality training dataset. In the first experiment, five rectangular areas of approximately 37.5 km2 each were selected to validate the water body detection performance of the TSAE-UNet model across different scenes. The second experiment focused on Jining City, Shandong Province, China, analyzing the monthly water body changes from 2020 to 2022 and the quarterly changes in 2022. The experimental results demonstrate that TSAE-UNet excels in multi-scene and long-term water body detection, achieving a precision of 0.989, a recall of 0.983, an F1 score of 0.986, and an IoU of 0.974, significantly outperforming FCN, PSPNet, DeepLabV3+, ADCNN, and MECNet.

Ensemble Machine-Learning-Based Framework for Estimating Surface Soil Moisture Using Sentinel-1/2 Data: A Case Study of an Arid Oasis in China

Soil moisture (SM) is a critical parameter in Earth’s water cycle, significantly impacting hydrological, agricultural, and meteorological research fields. The challenge of estimating surface soil moisture from synthetic aperture radar (SAR) data is compounded by the influence of vegetation coverage. This study focuses on the Weigan River and Kuche River Delta Oasis in Xinjiang, employing high-resolution Sentinel-1 and Sentinel-2 images in conjunction with a modified Water Cloud Model (WCM) and the grayscale co-occurrence matrix (GLCM) for feature parameter extraction. A soil moisture inversion method based on stacked ensemble learning is proposed, which integrates random forest, CatBoost, and LightGBM. The findings underscore the feasibility of using multi-source remote sensing data for oasis moisture inversion in arid regions. However, soil moisture content estimates tend to be overestimated above 10% and underestimated below 5%. The CatBoost model achieved the highest accuracy (R2 = 0.827, RMSE = 0.014 g/g) using the top 16 feature parameter groups. Additionally, the R2 values for Stacking1 and Stacking2 models saw increases of 0.008 and 0.016, respectively. Thus, integrating multi-source remote sensing data with Stacking models offers valuable support and reference for large-scale estimation of surface soil moisture content in arid oasis areas.

Mapping Italian high-altitude ponds.

Permanent and temporary ponds are considered peculiar ecosystems which provide important ecosystem functions, services, supporting biodiversity on small and large scales. Pond's conservation status is globally critical. Moreover, their ecological functioning and conservation status is frequently overlooked, because of the habitat small size, their seasonal occurrence and their unique appearance. While a certain attention is given to Mediterranean Temporary Ponds and, in general, to low altitude ponds, the ecological importance of high-altitude ponds is critically unrecognized, especially in the Italian peninsula. The main aim of this research is to create the first georeferenced checklist of Italian high-altitude ponds. In order to achieve this goal, we integrated spectral, spatial characteristics, and morphological operations based on Sentinel-1 and Sentinel-2 image data using the Google Earth Engine (GEE). Overall, 2156 ponds were identified: 62% (n = 1343) in the Alps and 38% (n = 813) in the Apennines. The highest number of ponds was detected in Central Alps (n = 642), followed by Western Alps (n = 479), Central Apennines (n = 412), Eastern Alps (n = 222), Southern Apennines (n = 216) and Northern Apennines (n = 185). For what concerns the Alps, the average altitude was estimated in 2428 m a.s.l., while in the Apennines the average altitude was estimated in 784 m a.s.l. The total area covered from ponds has been estimated in 4.258.640 m2, with a mean of 1716 m2. Ponds were described as 20% temporary (n = 445) and 80% permanent (n = 1711). Considering the land use, 83% (n = 1797) of ponds were described as "natural" and 17% (n = 359) as "anthropized". Identification and georeferentiation of high-altitude ponds are primary actions to the application of management plans and this research could be considered the first step towards the safeguard of these threatened ecosystems.

Super-resolution cropland mapping with Sentinel-2 images based on a self-training learning network

ABSTRACT The Sentinel-2 image is widely used for cropland mapping, but the limitation of its 10 m spatial resolution may significantly impact the accuracy of result in areas characterized by severe cropland fragmentation. To address the issue, this letter proposes a super-resolution cropland mapping model for Sentinel-2 images. The proposed model improves the spatial resolution of Sentinel-2 images to 2.5 m through self-training learning with the Swin Transformer for Image Restoration (SwinIR) model without needing fine-resolution training samples. Then, the random forest classification is applied to map cropland from the super-solved 2.5 m resolution image. The proposed model was assessed in the Jianghan Plain, China, and the results show that the super-resolution images produce cropland maps with higher accuracy than the original Sentinel-2 images. Comparing cropland mapping results before and after super-resolution, the overall accuracy improves from 0.82 to 0.89, while the commission error and omission error decrease from 0.09 to 0.05 and 0.17 to 0.11, respectively. This method addresses the shortage of training samples collection and improves the accuracy of cropland mapping, contributing to more reliable agricultural remote sensing applications.

Land cover and burn severity dynamics of the Ogan Komering Ilir peatlands from 2015 to 2023 using sar and optical datasets

Land cover changes and wildfires have had an increasing impact on the Ogan Komering Ilir Peatland ecosystems in South Sumatra, Indonesia. This study aims to understand the peatland land cover and burn severity dynamics from 2015 to 2023. The random forest method was applied to classify land cover, while the differenced Normalized Burn Ratio (dNBR) was used for mapping fire severity. We combined various satellite data to classify land cover, consisting of Landsat-8, Sentinel-1, and Sentinel-2. Landsat-8 or Sentinel-2 images were also used for the dNBR calculation. We revealed complex climate, human, and restoration interactions in land cover and burn severity fluctuations over 273,799 hectares of the study area from 2015 to 2023. The 2015 El Niño-induced drought led to 21,754 fire hotspots and 2.01% of the area in high-severity burns. In 2016, it reduced tree cover by 10.18% and increased bare/sparse vegetation by 6.11%. The 2019 El Niño event led to 7,893 fire hotspots, lessening unburned areas and worsening burns. Due to the extreme effects of the 2015 drought, restoration efforts between 2016 and 2020 significantly decreased fire hotspots in 2016. Tree cover stabilized, reaching 48.46% by 2020, whereas unburned areas rose to 69.46% in 2018, showing good recovery and lower fire severity. In 2021-2023, fire hotspots were modest relative to El Niño years but increased in 2023. After 2020, tree cover decreased, but other land cover classes fluctuated. Therefore, continual monitoring and adaptive management are critical for reducing negative consequences and increasing ecosystem resilience.

Surface energy balance-based surface urban heat island decomposition at high resolution

Urban heat island (UHI) is among the most pronounced human impacts on Earth. To formulate locally adapted mitigation strategies, a comprehensive understanding of the influencing mechanisms of UHI at high resolution is imperative. Based on surface energy balance, we attributed surface UHI (SUHI) into five biophysical terms (surface radiation, anthropogenic heat, convection, evapotranspiration and heat storage term) using Sentinel-2 and Landsat-8 images in Beijing. The simulated SUHI intensity, derived by combining all five contribution terms, exhibited a good consistency but a higher spatial resolution, than SUHI intensity extracted from Landsat-8 land surface temperature product. SUHI intensity tended to decrease from the old city to outsides, attributed to the decrease of evapotranspiration, solar radiation and anthropogenic heat term. The convection and heat storage term play a positive role in reducing SUHI. Among urban morphological blocks, low-rise and high-density blocks had the strongest SUHI, with the evapotranspiration term contributing the most. The results highlighted the capacity of the urban surface to evaporate water in affecting Beijing SUHI. The proposed method provides one useful tool to analyze the drivers of SUHI from the aspect of heat formation, which can be potentially applied worldwide for large-scale comparisons of how urbanization affects UHI.

Utilization of Sentinel Image Multitemporal Data for Landslide Potential Identification in Pujon Sub-District

Landslides are one of the most common disasters in Indonesia. Landslides are often triggered by high rainfall and occur for several days. Unstable soil structure is very easy to experience landslides. The case study of this research is in Pujon Sub-district, Malang Regency. Pujon sub-district has a hilly and highland topography. Its hilly-mountainous relief and steep slopes cause Pujon Sub-district to experience frequent natural disasters, especially landslides. The research was conducted by processing Sentinel 1 image using DInSAR method to obtain data on land surface change and weighting scoring method to obtain information on landslide prone areas. The processing of DInSAR method produces the value of vertical land surface change. The largest increase in land surface with a displacement value of 0.2972 m. And the largest land subsidence is -0.2234 m. Such movement can be an indicator of landslide occurrence. The GIS method produces 3 classes of landslide vulnerability level, namely low, medium and high in each year in Pujon sub-district. The average landslide prone area in the last 5 years is 15081.1331 ha. The processing results of DInSAR method and weighting scoring method can describe the landslide prone areas in Pujon Sub-district which is expected to be useful for landslide mitigation efforts.

Integrating time series Sentinel-2 images and tide height to mapping tidal flats in the Chinese mainland

As a unique and important ecosystem, tidal flats provide a variety of ecosystem functions and services. Mapping tidal flats is essential for the protection and management of coastal ecosystems. However, large-scale tidal flats mapping still faces challenges due to the tidal variation and spectral similarity between tidal flats and inland wetlands. Previous methods rely on the coastlines or maximum seawater extent to exclude inland areas, which is limited by its inability to effectively differentiate tidal flats from spectrally similar inland wetlands. To address these issues, we proposed a new tidal-flat mapping method by integrating Sentinel-2 time series imagery with tide height (TH) data from ground-based tide stations on Google Earth Engine. We first generated images at the lowest and highest tidal stages, established the statistical relationship between the Normalized Difference Water Index (NDWI) of each pixel and TH, and then concatenated them into a Random Forest classifier for further classification. The statistical relationship between NDWI and TH amplified the difference between tidal flats and inland wetlands, thus significantly reducing the influence of spectral similarity. This method could produce a high-precision tidal flats map with an overall accuracy of 97.30% in the coastal zone of the Chinese mainland. By quantitatively comparing with the previous tidal flat maps, we found that the strategies of tidal-level information simulation and inland area exclusion were the two main reasons producing the differences among the maps. The proposed method does not rely on space constraints to exclude inland wetlands and can capture more estuarine tidal flats, so it can be used as a reliable means to monitor the tidal flats in large-scale areas.

Investigating compressed SAR technique efficiency for land deformation analysis along Hwanggang dam

Abstract Monitoring and determining the land deformation in dam reservoirs is very crucial as it is one of the main factors of dam failure around the world. Hwanggang Dam is located at a strategic location near the inter-Korean border. North Korea frequently released water from the Hwanggang Dam, which often triggers floods and landslides in areas near the inter-Korea border in South Korea. Consistent monitoring of the Hwanggang Dam is very crucial to avoid catastrophic loss of infrastructure and life. This research paper exploits the potential of the Compressed SAR (ComSAR) technique for deformation analysis along Hwanggang Dam. A total of 171 Sentinel-1 images of Hwanggang Dam were downloaded from Alaska Satellite Facility in ascending track acquired between 2016 and 2023 to generate a time-series interferogram stack. The generated interferogram stack was exploited by using the ComSAR technique to estimate deformation along Hwanggang Dam. The resultant estimated deformation shows instability along the Hwanggang Dam embarkment.

Crop cover identification based on different vegetation indices by using machine learning algorithms

In this article, three different indices NDVI (Normalized Difference Vegetation Index), BNDVI (Blue Normalized Difference Vegetation Index) and GNDVI (Green Normalized Difference Vegetation Index) are used for the identification of wheat, mustard and sugarcane crop of Saharanpur district’s region of Uttar Pradesh. Sentinel 2B satellite images are collected from October 02, 2018 to April 15, 2019. These images are processed using Google Earth Engine. These sentinel images are used to generate NDVI, BNDVI and GNDVI images using GEE. These three different indices images are further processed using SNAP software and particular indices values for 210 different locations are calculated. The same process is used for calculating BNDVI and GNDVI values. ARIMA, LSTM and Prophet models are used to train the time series indices values (NDVI, BNDVI and GNDVI) of wheat, mustard and sugarcane crop. these models are used to analyse MSE (mean absolute percentage error) and RMSE values by considering various parameters. Using ARIMA Model, for wheat crop GNDVI indices shows minimum RMSE 0.020, For Sugarcane crop NDVI indices shows minimum RMSE 0.053, For Mustard crop GNDVI indices shows minimum RMSE 0.024. Using LSTM model, for wheat crop NDVI indices shows minimum RMSE 0.036, For Sugarcane crop BNDVI indices shows minimum RMSE 0.054, For Mustard crop GNDVI indices shows minimum RMSE 0.026. Using Prophet model, for wheat crop GNDVI indices shows minimum RMSE 0.055, For Sugarcane crop NDVI indices shows minimum RMSE 0.088, For Mustard crop GNDVI indices using Prophet model shows minimum RMSE 0.101.

Effect of Atmospheric Mixing on Spectral Reflectivity in Sentinel Images of Baghdad Province

The lowest layer of the atmosphere is called the atmospheric mixed layer, characterized by small-scale, irregular air motions defined by winds that change in speed and direction. Aerosol radiative effects impact the atmospheric boundary layer (ABL), which holds most aerosols in the lower atmosphere. Aerosol absorption and scattering both lower the quantity of solar energy that reaches the ground, which has an impact on the spectral signature of the land coverings. In this study, 51 locations in downtown Baghdad were chosen for four different types of land cover (water bodies, farms, open areas, and residential areas) for Sentinel 2 satellite imagery, and the time the pictures were taken was 8:00 am ( 22 March, 22 June, 20 September, and 22 December) 2021. Their spectral reflectance was calculated at the NIR band using a mathematical equation in the ArcGIS program for Sentinel 2 satellite images that had been processed and analyzed. Also, atmospheric boundary layer height and solar radiation values were downloaded for the same date as the satellite images and compared with the spectral reflectivity values of the land covers(agriculture area, residential area, open area, and river) and knowing the effect of the mixing layer and solar radiation on the spectral reflectance values. The highest value of spectral reflectivity, mixing layer height, and solar radiation was in June at (0.065, 1965.524629, and 897.7088) respectively. The spectral reflectivity of plants at near-infrared (NIR) was higher than the rest of the earth’s features because plants reflect near-infrared radiation and absorb the red and blue parts of the spectrum.

Mangrove mapping in China using Gaussian mixture model with a novel mangrove index (SSMI) derived from optical and SAR imagery

As an important shoreline vegetation and highly productive ecosystem, mangroves play an essential role in the protection of coastlines and ecological diversity. Accurate mapping of the spatial distribution of mangroves is crucial for the protection and restoration of mangrove ecosystems. Supervised classification methods rely on large sample sets and complex classifiers and traditional thresholding methods that require empirical thresholds, given the problems that limit the feasibility and stability of existing mangrove identification and mapping methods on large scales. Thus, this paper develops a novel mangrove index (spectral and SAR mangrove index, SSMI) and Gaussian mixture model (GMM) mangrove mapping method, which does not require training samples and can automatically and accurately map mangrove boundaries by utilizing only single-scene Sentinel-1 and single-scene Sentinel-2 images from the same time period. The SSMI capitalizes on the fact that mangroves are differentiated from other land cover types in terms of optical characteristics (greenness and moisture) and backscattering coefficients of SAR images and ultimately highlights mangrove forest information through the product of three expressions (f(S) = red egde/SWIR1, f(B) = 1/(1 + e-VH), f(W)=(NIR-SWIR1)/(NIR+SWIR1)). The proposed SSMI was tested in six typical mangrove distribution areas in China where climatic conditions and mangrove species vary widely. The results indicated that the SSMI was more capable of mapping mangrove forests than the other mangrove indices (CMRI, NDMI, MVI, and MI), with overall accuracys (OA) higher than 0.90 and F1 scores as high as 0.93 for the other five areas except for the Maowei Gulf (S5). Moreover, the mangrove maps generated by the SSMI were highly consistent with the reference maps (HGMF_2020、LASAC_2018 and IMMA). In addition, the SSMI achieves stable performance, as shown by the mapping results of the other two classification methods (K-means and Otsu’s algorithm). Mangrove mapping in six typical mangrove distribution areas in China for five consecutive years (2019–2023) and experiments in three Southeast Asian countries with major mangrove distributions (Thailand, Vietnam, and Indonesia) demonstrated that the SSMIs constructed in this paper are highly stable across time and space. The SSMI proposed in this paper does not require reference samples or predefined parameters; thus, it has great flexibility and applicability in mapping mangroves on a large scale, especially in cloudy areas.

Transformation trajectory of wetland and suitability of migratory water bird habitat in the moribund Ganges delta.

Wetland is a suitable habitat for water birds, and it enhances cultural ecosystem services. But the rapid transformation of such habitat, especially in floodplain environments, is an emerging crisis. Wetland reclamation and fragmentation are two major issues leading to poor habitat and landscape. The present paper aimed to explore the spatio-temporal changes in the suitability of wetland bird habitat, wetland landscape pattern, and the connection between them. Two wetlands, including a wetland of national importance, were taken as cases for this study. Time series Landsat and Sentinel images were taken for developing modeling parameters and Land Use Land Cover (LULC) for the years 2016 and 2020. The first transformation of wetland was accounted from the LULC maps of both years. Machine learning algorithm-based spatial models were developed for mapping the poor landscape condition of the existing wetland parts. Finally, semi-subjective analytic hierarchy approach (AHP)-based models were developed for assessing waterbird habitat suitability. Results demarcated more than 48% area belonging primarily to edges and tiny patches of wetlands under a poor state in 2020. Although the total wetland area was reduced between 2016 and 2020, the wetland area found to be highly suitable habitat increased from 25.5 to 59.44% of the total area during that period. The suitability of edge-preferring bird habitat showed a 10% increase. The increasing poverty of the landscape was caused by declining edge-preferring bird habitat suitability. From 1990 to 2020, 27% of wetlands were converted to single-cropped lands, and 5% were converted to multi-cropped agricultural land. Since the study spatially identified the potential suitable area and trend of wetland habitat transformation, this could help policymakers define suitable planning for the restoration and conservation of such promising bird habitat.