Sentinel-2 Time Series Research Articles

Discrimination and monitoring of rice cultural types using dense time series of Sentinel-1 SAR data

Details about the spatial extent of rice cultivation in different seasons are essential for ensuring global food security as well as addressing environmental issues and climate change. This study aims to introduce a novel approach to discriminate between different rice cultural types and subtypes and identify their optimum stages to represent crop growth profiles using Synthetic Aperture Radar (SAR) data. Dense time series Sentinel-1 SAR backscatter data were analyzed at 12 days intervals throughout the growth period of summer and kharif rice. Statistical analysis using ANOVA at 5% level of significance was conducted to identify the critical stages of discrimination for all cultural types and subtypes. Knowledge-based decision tree algorithm was employed to classify all major types of rice grown during summer and kharif season. A comprehensive analysis revealed significant variations in backscatter profiles among different rice types. The most critical period for effectively distinguishing between various rice cultural types and subtypes was found to be from transplanting to tillering stage. Decision tree algorithm was able to discriminate between rice cultural types and subtypes with high overall accuracy and Kappa coefficient, viz., 94.74% and 0.94 for summer rice and 91.80% and 0.90 for kharif rice, respectively. The findings of this study revealed that C-band co and cross-polarized Sentinel-1 data have the potential to detect differences between different cultural types and subtypes of rice and, therefore, will be helpful in crop growth monitoring and agro-ecological change detection in the cultivation pattern.

PSI Spatially Constrained Clustering: The Sibari and Metaponto Coastal Plains

PSI data are extremely useful for monitoring on-ground displacements. In many cases, clustering algorithms are adopted to highlight the presence of homogeneous patterns; however, clustering algorithms can fail to consider spatial constraints and be poorly specific in revealing patterns at lower scales or possible anomalies. Hence, we proposed a novel framework which combines a spatially-constrained clustering algorithm (SKATER) with a hypothesis testing procedure which evaluates and establishes the presence of significant local spatial correlations, namely the LISA method. The designed workflow ensures the retrieval of homogeneous clusters and a reliable anomaly detection; to validate this workflow, we collected Sentinel-1 time series from the Sibari and Metaponto coastal plains in Italy, ranging from 2015 to 2021. This particular study area is interesting due to the presence of important industrial and agricultural settlements. The proposed workflow effectively outlines the presence of both subsidence and uplifting that deserve to be focused and continuous monitoring, both for environmental and infrastructural purposes.

Tide2Topo: A new method for mapping intertidal topography accurately in complex estuaries and bays with time-series Sentinel-2 images

Intertidal zones serve as a critical transition between land and ocean and are periodically inundated by tides. They are extremely important to coastal communities as it provides diverse habitats and buffers against marine hazards. However, the reduced river sediment together with sea level rise and coastal reclamation have caused the widespread loss of intertidal areas. Reliable, high-resolution, and up-to-date intertidal topography maps are key information for coastal vulnerability assessment and restoration. Existing approaches to intertidal topography reconstruction involve significant ground surveys, with limited spatial coverage, accuracy, and efficiency, thus, restricting their potential to generalize globally. To address these issues, we propose a pixel-based approach to construct intertidal topography from dense Sentinel-2 satellite time-series and limited ground truth surveys, named as Tide2Topo. Tide2Topo differs conceptually from the widely used waterline method since it considers tidal inundation frequency as a topographic indicator rather than instantaneous waterlines. Therefore, Tide2Topo does not involve manual selection of images at different tide levels. The relationship between intertidal inundation frequency and elevation is calibrated using linear and polynomial models based on a few transect measurements instead of tidal height, preventing errors introduced by inaccurate tidal data. The proposed Tide2Topo was applied and validated in the UK and China over several complicated estuaries and bays with extensive muddy flats or sandy beaches. The results demonstrated that the topography derived from intertidal inundation frequency compared to LiDAR or UAV photogrammetric observations has a root mean square error ranging from 16 to 38 cm. The large errors were found in the tidal channel areas where the inundation frequency calculation was prone to uncertainty. Dense Sentinel-2 time-series observations ensure finer sampling of the tidal cycle, thereby not only eliminating errors caused by spatial interpolation but also maintaining the accuracy of elevation estimations at pixel level. Tide2Topo is a robust, portable and rapid method that is well suited to large-scale intertidal topography reconstruction. Future work could use Sentinel-1 SAR as an alternative data source for Tide2Topo, realizing seasonal or annual monitoring of intertidal geomorphological changes.

Inter-Continental Transfer of Pre-Trained Deep Learning Rice Mapping Model and Its Generalization Ability

Monitoring of rice planting areas plays an important role in maintaining food security. With powerful automatic feature extraction capability, crop mapping based on deep learning methods has become one of the most important research directions of crop remote sensing recognition. However, the training of deep learning models often requires a large number of samples, which restricts the application of these models in areas with a lack of samples. To address this problem, based on time-series Sentinel-1 SAR data, this study pre-trained the temporal feature-based segmentation (TFBS) model with an attention mechanism (attTFBS) using abundant samples from the United States and then performed an inter-continental transfer of the pre-trained model based on a very small number of samples to obtain rice maps in areas with a lack of samples. The results showed that an inter-continental transferred rice mapping model was feasible to achieve accurate rice maps in Northeast China (F-score, kappa coefficient, recall, and precision were 0.8502, 0.8439, 0.8345, and 0.8669, respectively). The study found that the transferred model exhibited a strong spatiotemporal generalization capability, achieving high accuracy in rice mapping in the three main rice-producing regions of Northeast China. The phenological differences of rice significantly affected the generalization capability of the transferred model, particularly the significant differences in transplanting periods, which could have resulted in a decrease in the generalization capability of the model. Furthermore, the study found that the model transferred based on an extremely limited number of samples could attain a rice recognition accuracy equivalent to that of the model trained from scratch with a substantial number of samples, indicating that the proposed method possessed strong practicality, which could dramatically reduce the sample requirements for crop mapping based on deep learning models, thereby decreasing costs, increasing efficiency, and facilitating large-scale crop mapping in areas with limited samples.

Delineation of Orchard, Vineyard, and Olive Trees Based on Phenology Metrics Derived from Time Series of Sentinel-2

This study aimed to propose an accurate and cost-effective analytical approach for the delineation of fruit trees in orchards, vineyards, and olive groves in Southern France, considering two locations. A classification based on phenology metrics (PM) derived from the Sentinel-2 time series was developed to perform the classification. The PM were computed by fitting a double logistic model on temporal profiles of vegetation indices to delineate orchard and vineyard classes. The generated PM were introduced into a random forest (RF) algorithm for classification. The method was tested on different vegetation indices, with the best results obtained with the leaf area index. To delineate the olive class, the temporal features of the green chlorophyll vegetation index were found to be the most appropriate. Obtained overall accuracies ranged from 89–96% and a Kappa of 0.86–0.95 (2016–2021), respectively. These accuracies are much better than applying the RF algorithm to the LAI time series, which led to a Kappa ranging between 0.3 and 0.52 and demonstrates the interest in using phenological traits rather than the raw time series of the remote sensing data. The method can be well reproduced from one year to another. This is an interesting feature to reduce the burden of collecting ground-truth information. If the method is generic, it needs to be calibrated in given areas as soon as a phenology shift is expected.

Mapping understory plant communities in deciduous forests from Sentinel-2 time series

Understory plant communities are an integral component of deciduous forests, playing a vital role in the overall health of the ecosystem. However, remote sensing of understory plant communities is challenging due to the obstruction by the forest canopy. In this study, we proposed an automated dense Sentinel-2 time series-based approach for understory plant communities and created maps of four understory classes (i.e., native shrubs of greenbrier and mountain laurel, and invasive shrubs of barberry and the assemblage of mixed invasive) at 10 m resolution in Connecticut's deciduous forests in 2020. A harmonic time series model and three years of Sentinel-2 time series from 2019 to 2021 were used to classify understory species based on their unique, intra-annual phenology characteristics. The time series model coefficients captured the subtle phenology differences and created synthetic cloud-free images within a short temporal window in the spring prior to canopy leaf-on (hereafter called “observation window”). During the observation window, Sentinel-2 data penetrated the deciduous overstory canopy and observed the unique trajectories of different understory species due to their phenology differences. We also calculated spatial texture features (i.e., mean, second moment, and contrast from gray level co-occurrence matrix) based on the synthetic images created within the observation window to capture the different conditions of leaf growth and distinct spatial patterns within deciduous forests. By using the spectral, temporal, and spatial features as input variables from dense Sentinel-2 data, auxiliary data (i.e., LiDAR and soil drainage layer), a random forest classifier, and a new strategy to iteratively select representative sample (namely ISRS), understory species maps were created with an overall accuracy of approximately 93%, and the user's and producer's accuracies varied from 39% to 99% for the three mapped understory species and one assemblage of species. The proposed method created an accurate binary map of understory presence with an overall accuracy of 95%, a producer's accuracy of 84%, and a user's accuracy of 68%. Additionally, we separated the invasive (i.e. barberry and mixed invasive of multi-flora rose, oriental bittersweet, honeysuckle, winged euonymus, and autumn olive) and native (greenbrier and mountain laurel) species with an overall accuracy of 94%. We estimated that the invasive species cover an area of 649.33 ± 140.59 km2, which occupied a large proportion (∼53%) of the shrub understory in Connecticut's deciduous forests.

Mapping tea plantation area using phenology algorithm, time-series Sentinel-2 and Landsat images

ABSTRACT Tea plants are evergreen broad-leaved perennial shrubs, and their spectral characteristics are very similar to those of other evergreen vegetation, making it difficult to distinguish them. Currently, the most commonly used method of tea plantation extraction is machine learning, which classifies tea plantation through various feature combinations and algorithms. The disadvantage of these methods is that they require a large number of local training samples, making it challenging to produce an accurate model applicable to a large region. Furthermore, complex feature combinations and indicators may result in over-fitting, reducing the accuracy of the results. Therefore, it is necessary to develop a new algorithm suitable for tea plantation extraction in extensive regions. This paper uses Shihe District, Henan Province, China as a case in 2019, combined with Landsat-7/8 and Sentinel-2A/B images, and develops a new phenological-based algorithm to extract the tea plantation area. Firstly, we generated an evergreen vegetation map. Secondly, based on high-quality time series curves, the tea plant growth period was divided into seven parts to extract phenological indicators for classification. Finally, the tea plantation in the study area was extracted on a per-pixel basis. The overall accuracy of the algorithm is 87.59%, and the Kappa coefficient is 0.80. This study demonstrates the potential of the phenology-algorithms in extracting tea plantation areas and provides an advanced scheme and scientific basis for extracting tea plantation in other years, and also offers a reference for identifying tea plantation in other regions. Additionally, this paper generated a map of classified phenological indicators to provide guidance for monitoring tea plants growth.

Delineation and optimization of cotton farmland management zone based on time series of soil-crop properties at landscape scale in south Xinjiang, China

Traditional farmland management approaches treat farmland as a homogeneous unit, leading to uneven distribution and waste of resources, which is contrary to precision agriculture. Management zones (MZ) delineation is an essential method of site-specific management (SSM) for accomplishing variable rate input of resources in subregions by recognizing the heterogeneity of farmland soils. Aimed at the soil salinization problem in Xinjiang, this study estimates the salt content of soil profiles and essential nutrient content using time series Sentinel-2 images and machine learning methods, and describes crop growth conditions through dynamic time warping. Subsequently, geographically weighted principal component analysis and possibilistic fuzzy C-means algorithm were employed to outline MZ based on soil-crop characteristics. Three scales (10 m, 100 m, and field scales divided by ridges) were examined to obtain the optimal scale of delineation. Soil-crop properties were estimated at a spatial resolution of 10 m using machine learning methods with R2 > 0.65. In the zoning process, the field scale was selected with accuracy and practicality in agricultural practices, with comparatively higher homogeneity within zones and variation between zones. The soils in the study area were categorized into three zones at field scale with a fuzzy performance index of 0.68, a normalized classification entropy of 0.71, and fuzzy class of 0.30. Soil function is primarily restricted by salinity and fertility. Fertilizer recommendation strategies were given for the three zones. This study uses time-series remote sensing images to obtain a high-precision estimation of soil-crop characteristics at 10 m, and optimize the scale of farmland management zoning, which provides a valuable technology and scale selection for SSM for cotton management zone delineation in saline farmland in arid and semi-arid regions.

Synergistic Integration of Time Series Optical and SAR Satellite Data for Mariculture Extraction

Mariculture is an important part of aquaculture, and it is important to address global food security and nutrition issues. However, seawater environmental conditions are complex and variable, which causes large uncertainties in the remote sensing spectral features. At the same time, mariculture types are distinct because of the different types of aquaculture (cage aquaculture and raft aquaculture). These factors bring great challenges for mariculture extraction and mapping using remote sensing. In order to solve these problems, an optical remote sensing aquaculture index named the marine aquaculture index (MAI) is proposed. Based on this spectral index, using time series Sentinel-1 and Sentinel-2 satellite data, a random forest classification scheme is proposed for mapping mariculture by combining spectral, textural, geometric, and synthetic aperture radar (SAR) backscattering. The results revealed that (1) MAI can emphasize the difference between mariculture and seawater; (2) the overall accuracy of mariculture in the Bohai Rim is 94.10%, and the kappa coefficient is 0.91; and (3) the area of cage aquaculture and raft aquaculture in the Bohai Rim is 16.89 km2 and 1206.71 km2, respectively. This study details an effective method for carrying out mariculture monitoring and ensuring the sustainable development of aquaculture.

Grassland cut detection based on Sentinel-2 time series to respond to the environmental and technical challenges of the Austrian fodder production for livestock feeding

The relationship between yield and quality of grassland fodder is an important factor for livestock farmers to consider when deciding the timing of grassland cuts. Even for experienced farmers, estimating this relationship is difficult because external factors such as weather can affect the growth rate of plants. Satellite remote sensing techniques have good potential for the modelling of fodder biomass and quality. However, more accurate grassland cut dates are needed. Unfortunately, current cut detection methods vary in both form, function and accuracy and a common framework for cut detection does not yet exist, especially for in-season cut estimation. The aim of this study is to develop an improved methodology for Austrian fodder production systems that can be used for both post-season and in-season cut detection. In the first case, cuts are detected at the end of the vegetation period and used for post-season assessment at national and regional levels. In the second case, cuts are detected in near-real-time during the season, and they can be used to model yield and quality. The approach is tested across the Austrian landscape on mown grasslands using freely available Copernicus Sentinel-2 imagery on the Google Earth Engine (GEE). The method uses a fitted NDVI curve obtained by iteratively smoothing the upper envelope of the actual observations using the Whittaker smoother. Cuts are detected by meeting a threshold in the difference between the fitted NDVI curve and the actual observed NDVI values. Additional innovative steps were developed to reduce the impact of cloud cover on the results for timely and accurate cut detection, including a gradient boosting algorithm to detect false positive cuts due to missed clouds. A set of rules for the method was trained on 97 plots and validated on a further 502 plots over three years (2020, 2021 and 2022). The method performed well in plots with two to four cuts per season, but occasionally struggled in more intensively managed systems, most likely due to a rule preventing two cuts within 27 days. A pooled post-season cut date detection f-score of 0.79 suggests that the method can accurately detect cuts, with absolute detection errors of <5 days. The main misclassifications were the results of (thin and semi-transparent) clouds being erroneously detected as cuts. With an f-score of 0.80 and total absolute delayed detection of approximately 6 days, the in-season cut date detection performs similarly to the post-season configuration. This indicates a high potential for near-real-time applications. Our results moreover indicate a general improvement of existing methodologies despite performing the cut detection across the fragmented, mountainous and diverse Austrian landscape. Future work will include grazed pastures and natural grassland with less intensive human activity.

An unsupervised domain adaptation deep learning method for spatial and temporal transferable crop type mapping using Sentinel-2 imagery

Accurate crop type mapping is essential for crop growth monitoring and yield estimation. Recently, various machine learning methods have been increasingly used for crop type mapping, but they often lose their validity when directly applied to other regions and years due to differences in the distribution of source and target data, that is, domain shift. To address the problem, we developed a deep adaptation crop classification network (DACCN) based on the idea of unsupervised domain adaptation (UDA). The proposed DACCN mainly consists of two parts, a feature extractor that converts the original input into high-level representations, and a domain aligner where the discrepancy between source and target distributions is measured using multiple kernel variant of maximum mean discrepancy (MK-MMD). Four states in the United States (U.S.) Corn Belt and two provinces in northeastern China were used as study areas, where samples used for model building and accuracy evaluation were collected based on time-series Sentinel-2 imagery and reference maps in 2018 and 2019. Then, three experiments were designed to verify the transferability of DACCN across space, time, and space–time, respectively. In each experiment, the proposed DACCN was compared to deep crop classification network (DCCN), a model with a similar structure to DACCN but without the domain adaptation mechanism, and two machine learning methods, random forest (RF) and support vector machines (SVM). The experimental results showed that DACCN outperformed other models in most transfer cases with overall classification accuracies ranging from 0.835 to 0.922. The DACCN also performed better in spatially continuous mapping with its predicted crop type maps more consistent with the reference ones. As an innovative application of transfer learning in crop type mapping, the methodology proposed in this study effectively addressed the problem of missing labels in target domains and alleviated the negative impact of domain shift.

Monitoring and Mapping Floods and Floodable Areas in the Mekong Delta (Vietnam) Using Time-Series Sentinel-1 Images, Convolutional Neural Network, Multi-Layer Perceptron, and Random Forest

The annual flood cycle of the Mekong Basin in Vietnam plays an important role in the hydrological balance of its delta. In this study, we explore the potential of the C-band of Sentinel-1 SAR time series dual-polarization (VV/VH) data for mapping, detecting and monitoring the flooded and flood-prone areas in the An Giang province in the Mekong Delta, especially its rice fields. Time series floodable area maps were generated from five images per month taken during the wet season (6–7 months) over two years (2019 and 2020). The methodology was based on automatic image classification through the application of Machine Learning (ML) algorithms, including convolutional neural networks (CNNs), multi-layer perceptrons (MLPs) and random forests (RFs). Based on the segmentation technique, a three-level classification algorithm was developed to generate maps of the development of floods and floodable areas during the wet season. A modification of the backscatter intensity was noted for both polarizations, in accordance with the evolution of the phenology of the rice fields. The results show that the CNN-based methods can produce more reliable maps (99%) compared to the MLP and RF (97%). Indeed, in the classification process, feature extraction based on segmentation and CNNs has demonstrated an effective improvement in prediction performance of land use land cover (LULC) classes, deriving complex decision boundaries between flooded and non-flooded areas. The results show that between 53% and 58% of rice paddies areas and 9% and 14% of built-up areas are affected by the flooding in 2019 and 2020 respectively. Our methodology and results could support the development of the flood monitoring database and hazard management in the Mekong Delta.

Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data

Abstract. Over 90 % of the world's rice is produced in the Asia–Pacific region. Synthetic-aperture radar (SAR) enables all-day and all-weather observations of rice distribution in tropical and subtropical regions. The complexity of rice cultivation patterns in tropical and subtropical regions makes it difficult to construct a representative data-relevant rice crop model, increasing the difficulty in extracting rice distributions from SAR data. To address this problem, a rice area mapping method for large regional tropical or subtropical areas based on time-series Sentinel-1 SAR data is proposed in this study. Based on the analysis of rice backscattering characteristics in mainland Southeast Asia, the combination of spatiotemporal statistical features with good generalization ability was selected and then input into the U-Net semantic segmentation model, combined with WorldCover data to reduce false alarms, finally the 20 m resolution rice area map of five countries in mainland Southeast Asia in 2019 was obtained. The proposed method achieved an accuracy of 92.20 % on the validation sample set, and the good agreement was obtained when comparing our rice area map with statistical data and other rice area maps at the national and provincial levels. The maximum coefficient of determination R2 was 0.93 at the national level and 0.97 at the provincial level. These results demonstrate the advantages of the proposed method in rice area mapping with complex cropping patterns and the reliability of the generated rice area maps. The 20 m annual paddy rice area map for mainland Southeast Asia is available at https://doi.org/10.5281/zenodo.7315076 (Sun et al., 2022b).

Towards precision forestry: Drought response from remote sensing-based disturbance monitoring and fine-scale soil information in Central Europe

Prolonged drought and susceptibility to biotic stressors induced an extensive calamity in Norway spruce (Picea abies (L.) Karst.) and widespread crown defoliation in European beech (Fagus sylvatica L.) in Central Europe. For future management decisions, it is crucial to link changes in canopy cover to site conditions. However, current knowledge on the role of soil properties for drought-induced forest disturbance is limited due to the scarcity and low spatial resolution of soil information. We present a fine-scale assessment on the role of soil properties for forest disturbance in Norway spruce and European beech derived from optical remote sensing. A forest disturbance modeling framework based on Sentinel-2 time series was applied on 340 km2 in low mountain ranges of Central Germany. Spatio-temporal information on forest disturbance was calculated at 10 m spatial resolution in the period 2019–2021 and intersected with high-resolution soil information (1:10,000) based on roughly 2850 soil profiles. We found distinct differences in disturbed area, depending on soil type, texture, stoniness, effective rooting depth and available water capacity (AWC). For spruce, we found a polynomial relationship between AWC (R2 = 0.7) and disturbance, with highest disturbed area (65 %) for AWC between 90 and 160 mm. Interestingly, we found no evidence for generally higher disturbance on shallow soils, although stands on the deepest soils were significantly less affected. Noteworthy, sites affected first did not necessarily exhibit highest proportions of disturbed area post-drought, indicating recovery or adaptation. We conclude that site- and species-specific understanding of drought impacts benefits from a combination of remote sensing and fine-scale soil information. Since our approach revealed which sites were affected first and most, it qualifies for prioritizing in situ monitoring activities to most vulnerable stands in acute drought conditions as well as for developing long-term strategies for reforestation and site-specific risk assessment for precision forestry.

Use of time series Sentinel-1 and Sentinel-2 image for rice crop inventory in parts of Bangladesh

Use of time series Sentinel-1 and Sentinel-2 image for rice crop inventory in parts of Bangladesh

Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes

Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP) models and their ability to fuse efficiently Sentinel-1 (S1) Radar Vegetation Index (RVI) and Sentinel-2 (S2) vegetation water content (VWC) time series over a dry agri-environment in southern Argentina. MOGP models not only exploit the auto-correlations of S1 and S2 data streams independently but also the inter-channel cross-correlations. The S1 RVI and S2 VWC time series at the selected study sites being the inputs of the MOGP models proved to be closely correlated. Regarding the set of assessed models, the Convolutional Gaussian model (CONV) delivered noteworthy accurate data fusion results over winter wheat croplands belonging to the 2020 and 2021 campaigns (NRMSEwheat2020 = 16.1%; NRMSEwheat2021 = 10.1%). Posteriorly, we removed S2 observations from the S1 &amp; S2 dataset corresponding to the complete phenological cycles of winter wheat from September to the end of December to simulate the presence of clouds in the scenes and applied the CONV model at the pixel level to reconstruct spatiotemporally-latent VWC maps. After applying the fusion strategy, the phenology of winter wheat was successfully recovered in the absence of optical data. Strong correlations were obtained between S2 VWC and S1 &amp; S2 MOGP VWC reconstructed maps for the assessment dates (R2¯wheat−2020 = 0.95, R2¯wheat−2021 = 0.96). Altogether, the fusion of S1 SAR and S2 optical EO data streams with MOGP offers a powerful innovative approach for cropland trait monitoring over cloudy high-latitude regions.

Mapping evergreen forests using new phenology index, time series Sentinel-1/2 and Google Earth Engine

Evergreen forests are sensitive to climate change and their role in the exchange of carbon, water, and energy in terrestrial ecosystems is irreplaceable. The temporal and spatial variation of the evergreen forest is closely related to forest monitoring and management in China, which has a far-reaching impact on forest protection and sustainable development. However, the lack of annual fine resolution maps of evergreen forests limits our exploration of the evolution of the spatiotemporal patterns of evergreen forests. Therefore, it is necessary to timely and accurately maps evergreen forests with high spatial resolution. We used a new phenology index of NDVImax-NDVIwinter_max to identify evergreen forests at the 10-m scale. First, we mapped annual 2019 land cover types to obtain the forest mask. Second, we calculated the classification phenology index by extracting the difference between evergreen forests and deciduous forests in phenological characteristics. Finally, we extracted the evergreen forest in 2019 from the annual forest map based on the constructed phenological indicators. The kappa coefficient of our annual forest map for 2019 was 0.92, and the overall, producer and user accuracies were 97.98%, 90.09%, and 97.46%, respectively. The kappa coefficient of the annual evergreen forest map in 2019 was 0.98, and the overall, producer and user accuracies were 99.73%, 97.84%, and 99.68%, respectively. Our study shows that the new phenological index can identify and map evergreen forests on complex landforms dominated by evergreen-deciduous mixed forests, which can be applied to other regions and years in China. The results of this study have reference value for evaluating the spatial distribution and resource management of evergreen forests.

Spatio-temporal dynamics of tree cover change in semi-arid Ghana: evidence from Sentinel-2 time series data

ABSTRACT Trees provide vital ecosystem services, yet anthropogenic activities have continued to reduce tree cover (TC) globally, leading to disparities in the distribution of ecosystem services at various scales. Similarly, empirical analysis of indicators that measure variations in TC at various temporal and spatial scales is limited in the semi-arid regions where global environmental challenges such as climate change are already affecting TC distribution. This paper quantified recent (2017 - 2019) TC changes in the semi-arid region of Ghana using Sentinel-2 times series data and the application of landscape metrics. The results showed that TC declined by 30% within two years. Urbanizing areas showed more rapid TC depletion rates, while some rural areas experienced a slight increase in TC. The findings suggest the need for local environmental governance by consolidating actions towards conserving semi-arid TC. Sustainable charcoal and fuelwood production are imperative for a long-term sustainable supply of ecosystem services.

Extending polarimetric optimization of multi-temporal InSAR techniques on dual polarized Sentinel-1 data

The rationale of combining polarimetric SAR data with multi-temporal InSAR techniques, which is based on polarimetric optimization, is to improve their performance in identifying coherent pixels and increasing the accuracy of deformation monitoring. This paper extended the polarimetric optimization of Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques on dual-polarized Sentinel-1 data. For this purpose, 20 dual-polarization (VV/VH) SAR images acquired by the Sentinel-1A sensor were used. The results indicated that by applying polarimetric optimization on dual-polarized data in the SBAS technique, the number of candidate and final coherent pixels increased 2.95 and 1.42 times, respectively. Similarly, the number of candidate and final coherent pixels (CP) increased 3.12 and 1.45 times in the PSI technique, respectively. Also, the effect of the polarimetric optimization on increasing the density of coherent points in different regions was investigated. In this framework, Sentinel-2 time-series images were classified into four classes (bare soil, vegetation, build-up, and others) using Google Earth Engine by applying remote sensing indices. This demonstrated that polarimetric optimization of the SBAS technique was more effective in bare soil and vegetated regions than in urban areas. However, polarimetric optimization of the PSI technique was more successful in non-urban regions than in urban areas. An external comparative analysis was also performed between the deformation results derived by PSI and SBAS techniques and GPS observation. The evaluation demonstrated that polarimetric optimization coupled with multi-temporal InSAR analysis yielded more reliable deformation results than conventional single-polarimetric data.

Continental-scale wetland mapping: A novel algorithm for detailed wetland types classification based on time series Sentinel-1/2 images

Wetlands have suffered from considerable degradation due to anthropogenic and natural disturbances in recent decades. Although some advancements have been made, effective methods that can produce large-scale wetland maps with detailed categories are still lacking due to the diversity and complexity of wetland ecosystems. To address this issue, we developed a novel algorithm for detailed wetland types classification integrating k-fold random forest and hierarchical decision tree, and so named two-step classification algorithm. Firstly, the phenology-based features were composited based on time series Sentinel-1/2 images, and the k-fold random forest was used to extract five rough wetland types in Google Earth Engine platform. Secondly, the hierarchical decision tree designed based on geometric features was used to separate the rough wetland types into fourteen detailed types. Application of the two-step classification method in Northern, Central and Southern Asia (NCSA) resulted in a continental-scale wetland map with an overall accuracy of 90.0 ± 0.5%. Wetland types, including inland marsh, lake, river, coastal swamp, estuarine water, lagoon, shallow marine water, reservoir, canal/channel and agricultural pond, had good accuracy with both UA and PA over 77%. The remaining wetland types had moderate accuracy, with both UA and PA over 58%. As we calculated, total wetland areas of NCSA were 1,375,489.27 km2. Among the fourteen wetland categories, the inland marsh had the largest area (544,584.38 km2) and was primarily distributed in subarctic and humid continental climates, while the canal/channel had the smallest area (1,651.57 km2) and was primarily scattered in desert, semiarid and humid subtropical climates. The lake and floodplain shared generally large areas with value of 392,413.55 km2 and 173,255.71 km2 respectively, which were typically distributed across desert and semiarid climates. This study successfully mapped continental-scale wetlands with detailed categories at a 10-m spatial resolution, which can provide valuable information for the management of wetland ecosystems and facilitate the implementation of wetland-related sustainable development goals.