Sentinel-2 Sensor Research Articles

Inter-Sensor Level 1 Radiometric Comparisons Using Deep Convective Clouds

To evaluate the radiometric performance of top-of-atmosphere reflectance images, Deep Convective Clouds (DCCs) can be used as temporally, spatially and spectrally stable targets. The DCCs method has been developed more than 20 years ago and applied recently to Sentinel-2 and Sentinel-3 sensors. In this paper, among other developments, we built a new methodology upon those existing by using the bootstrap method and spectral band adjustment factors computed with the Hyper-Spectral Imager (HSI) from the Environmental Mapping and Analysis Program (EnMAP). This methodology is applied to the two Multi-Spectral Imager (MSI) instruments onboard Sentinel-2A and 2B, but also the two Operational Land Imager (OLI) instruments onboard Landsat 8 and 9, from visible wavelength at 442 nm to shortwave-infrared at 2200 nm, using images with a ground resolution spanning from 10 m to 60 m. The results demonstrate the good inter-calibration of MSI units A and B, which are within one percent of relative difference on average between January 2022 and June 2024 for all visible, near-infrared and shortwave-infrared bands, except for the band at 1375 nm for which saturation prevents the use of the method. Similarly, OLI and OLI-2 are found to have a relative difference on the same period lower than one percent for all 30 m resolution bands. Evaluation of the relative difference between the MSI sensors and the OLI sensors with the DCCs method gives values lower than three percent. Finally, these validation results are compared to those obtained with Pseudo-Invariant Calibration Sites (PICSs) over Libya-4: an agreement better than two percent is found between the DCCs and PICSs methods.

A novel optimized spectral-based data driven approach for ecoregion burned scar detection

ABSTRACT Climate change has led to an increase in fire frequency in some areas on Earth. Satellite-derived spectral indices such as the Burn Area Index (BAI), the Normalized Burn Ratio (NBR), and their various derivatives are important for monitoring fires. The Burn Area Index for Sentinel-2 (BAIS2), which relies on the spectral bands of Sentinel-2, has a limitation due to its dependence on the red-edge spectral region unique to this satellite. To address this limitation, two new spectral indices (the Optimized Burned Area Index (OBAI) and the Optimized Burned Area Thermal Index (OBATI)) for burned scar mapping have been proposed based on the spectral separability concept and inter-band correlations. These indices aimed to improve the effectiveness of burned scar mapping by utilizing a wider range of spectral bands, including Landsat-8 thermal band and visible, near infrared (NIR), and mid infrared (MIR) regions of either Landsat-8 or Sentinel-2 sensors, which are more responsive to burned scars. We used spatial and spectral accuracy assessments to compare the effectiveness of the standard burned scar mapping indices (BASI2 and NBR) with the newly developed indices in discriminating the burned scars in several world regions. Based on Emergency Management Service fire perimeters, the vector distance algorithm (VDA) revealed that the accuracy of the fire perimeters extracted from the newly developed index, OBAI, was more reliable than the perimeters extracted using the BAIS2 and NBR. The BAIS2 index had the lowest performance in terms of spectral sensitivity compared to the other indices in the detection of burned scars. The performance of the developed optical index was high once the Landsat-8 data used to calculate it compared to Sentinel-2 data. Our findings could be used to further optimize global burned scar products derived from spaceborne data.

Spectral indices with different spatial resolutions in recognizing soybean phenology.

The aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops. The experiment was carried out in a soybean cultivation area irrigated by central pivot, in Balsas, MA, Brazil, where weekly assessments of phenology and leaf area index were carried out. Throughout the crop cycle, spectral data from the study area were collected from sensors, onboard the Sentinel-2 and Amazônia-1 satellites. The images obtained were processed to obtain the VI based on NIR (NDVI, NDWI and SAVI) and RGB (VARI, IV GREEN and GLI), for the different phenological stages of the crop. The efficiency in identifying phenological stages by VI was determined through discriminant analysis and the Algorithm Neural Network-ANN, where the best classifications presented an Apparent Error Rate (APER) equal to zero. The APER for the discriminant analysis varied between 53.4% and 70.4% while, for the ANN, it was between 47.4% and 73.9%, making it not possible to identify which of the two analysis techniques is more appropriate. The study results demonstrated that the difference in sensors spatial resolution is not a determining factor in the correct identification of soybean phenological stages. Although no VI, obtained from the Amazônia-1 and Sentinel-2 sensor systems, was 100% effective in identifying all phenological stages, specific indices can be used to identify some key phenological stages of soybean crops, such as: flowering (R1 and R2); pod development (R4); grain development (R5.1); and plant physiological maturity (R8). Therefore, VI obtained from orbital sensors are effective in identifying soybean phenological stages quickly and cheaply.

Obtaining estimation algorithms for water quality variables in the Jaguari-Jacareí Reservoir using Sentinel-2 images

Satellite images are essential tools for monitoring aquatic ecosystems and assessing water quality, as they enable the measurement of parameters such as chlorophyll-a (Chl-a) concentration, phycocyanin (PC), and cyanobacteria density. These indicators aid in evaluating eutrophication processes and detecting cyanobacteria in aquatic ecosystems. This study utilized field data and images captured by the Sentinel-2 sensor from 2015 to 2022 to investigate the Jaguari-Jacareí reservoirs (JAG-JAC). Two atmospheric corrections from the Case 2 Regional Coast Color (C2RCC) processor, namely C2X and C2XC, were applied, and algorithms were developed to estimate the parameters using both in situ data measurements and reflectance data extracted from the images. For Chl-a concentration, the dataset was divided into two blocks: one for model calibration (70% of the data) and the other for validation (30% of the data). As for PC, the entire dataset was utilized to calibrate the model, and validation was conducted through cross-validation using the Automated Radiative Transfer Model Operator (ARTMO) software. Cyanobacteria density was indirectly estimated from the Chl-a concentrations determined in the field samples, as these variables exhibited a strong correlation, also validating the model previously proposed for the Cantareira system for estimating cyanobacteria density from Chl-a data. Additionally, the automatic chlorophyll-a products (con_chla) derived from the C2X and C2XC processors were validated. The findings revealed that the C2X processor exhibited the greatest potential for estimating water quality parameters. It was observed that the most effective algorithms were derived using the R705/R665 band ratio for Chl-a and the R705/R490 ratio for PC. For cyanobacteria density, the optimal algorithm was established based on the relationship between cyanobacteria density and Chl-a using the data obtained in this study.

Evaluating pasture cover density mapping: a comparative analysis of Sentinel-2 and Spot-5 multispectral sensor images

Vegetation density extraction is influenced by the characteristics of satellite images, vegetation type, classification algorithm, and region, but there is little information about multispectral imaging (MSI). Studying the compatibility of the information content of sensors to replace sensors in areas where there is no easy access to their data is necessary for remote sensing (RS) studies. This study aims to assess the suitability of MSI from Sentinel-2 and Spot-5 satellites for generating pasture density maps. The Middle Kashkan watershed in the Lorestan Province of Iran was the study area. Geometric correction of the images was performed using ground control points (GCP) and the area's digital elevation model, achieving an accuracy of 0.21 pixels or better. Supervised classification techniques including parallelogram, minimal distance, maximum likelihood, and artificial neural network (ANN) algorithms were applied to the primary MSI of each satellite, as well as the integrated image of Spot-5 and the resulting pasture density map. Three density categories were considered: 5–25%, 25–50%, and over 50%. To validate the accuracy of the classification, a ground truth map of the region was created by interpreting a referenced official digital orthophotomosaic image at a scale of 1:40,000. Comparative analysis of the classified images revealed that the Sentinel-2 image with PCA-2-8 band composition and ANN classification algorithm yielded superior results, with an overall accuracy of 65.72% and a kappa coefficient of 0.08, compared to the Spot-5 image with PCA-3-1 band composition and the ANN classification algorithm. Spot-5 satellite images demonstrated greater effectiveness in generating pasture cover maps across the three density categories. These findings suggest that satellite images with suitable spatial and spectral resolution can be effectively utilized for generating accurate pasture density maps and monitoring long-term pasture preservation, particularly in regions characterized by high aerial photography altitudes in pasture areas. This approach holds the potential for effective pasture management and conservation efforts on a global scale.

The importance of spatial scale and vegetation complexity in woody species diversity and its relationship with remotely sensed variables

Plant species diversity is key to ecosystem functioning, but in recent decades anthropogenic activities have prompted an alarming decline in this community trait. Thus, developing strategies to understand diversity dynamics based on affordable and efficient remote sensing monitoring is essential, as well as examining the relevance of spatial scale and vegetation structural complexity to these dynamics. Here, we used two mathematical approaches to assess the relationship between tropical woody species diversity and spectral diversity in a human-modified landscape in two vegetation types differing in their degree of complexity. Vegetation complexity was measured through the fraction of species that concentrate different proportions of the cumulative importance value index. Species diversity was assessed using Hill numbers at three spatial scales, and metrics of spectral heterogeneity, vegetation indices, as well as raw data from Landsat 9 and Sentinel-2 sensors were calculated and analysed through general linear models (GLM) and Random Forest. Vegetation complexity emerged as an important variable in modelling species from remote sensing metrics, indicating the need to model species diversity by vegetation type rather than region. Hill numbers showed different relationships with remotely sensed metrics, in consistency with the scale-dependency of ecological processes on species diversity. Contrary to multiple previous reports, in our study, GLMs produced the best fits between Hill numbers of all orders and remotely sensed metrics. If we are to meet the need of conducting efficient and speedy woody species diversity monitoring globally, we propose modelling this diversity from remotely-sensed variables as an attractive strategy, so long as the intrinsic properties of each vegetation type are acknowledged to avoid under- or overestimation biases.

Mapping of Yangtze River Estuary Tidal Flat Based on Sentinel-2 Sensor and Google Earth Engine

Mapping of Yangtze River Estuary Tidal Flat Based on Sentinel-2 Sensor and Google Earth Engine

CResU-Net: a method for landslide mapping using deep learning

Landslides, which can occur due to earthquakes and heavy rainfall, pose significant challenges across large areas. To effectively manage these disasters, it is crucial to have fast and reliable automatic detection methods for mapping landslides. In recent years, deep learning methods, particularly convolutional neural and fully convolutional networks, have been successfully applied to various fields, including landslide detection, with remarkable accuracy and high reliability. However, most of these models achieved high detection performance based on high-resolution satellite images. In this research, we introduce a modified Residual U-Net combined with the Convolutional Block Attention Module, a deep learning method, for automatic landslide mapping. The proposed method is trained and assessed using freely available data sets acquired from Sentinel-2 sensors, digital elevation models, and slope data from ALOS PALSAR with a spatial resolution of 10 m. Compared to the original ResU-Net model, the proposed architecture achieved higher accuracy, with the F1-score improving by 9.1% for the landslide class. Additionally, it offers a lower computational cost, with 1.38 giga multiply-accumulate operations per second (GMACS) needed to execute the model compared to 2.68 GMACS in the original model. The source code is available at https://github.com/manhhv87/LandSlideMapping.git.

Utilizing InSAR for Surface Stability Monitoring in Mining Sites: A Case Study of Sukari Gold Mine in Egypt

Abstract. The monitoring of mining sites is crucial as a result of the safety and environmental issues linked to mining activities. Traditional surveying and monitoring tools exhibit limitations in terms of spatial coverage and are associated with various concerns regarding time and cost efficiency. Hence, the mining industry has shown increased interest in the time-series analysis of interferometric synthetic aperture radar (InSAR). This is primarily due to its precision and wide spatial coverage advantages. This study employed an InSAR technology, persistent scatterer interferometry (PSI), to identify potential geohazards within the Sukari gold mine (SGM), located in Egypt. A total of 23 images were collected from the ascending orbit of Sentinel-1 sensor between January 7, 2022, and November 3, 2022, for the SGM mining location. Results revealed relatively stable measurements in non-mining areas of the site, with a surface displacement within ±10mm per year in the line-of-sight (LOS) direction. However, several active mining operation areas showed significant surface motion. Various sections of the open pit of the SGM exhibited displacement, including the southwestern edge (-16.8mm) and the northeastern edge (35.7mm), and the highest velocity was detected at a section of the western edge, which demonstrated an annual displacement of -64mm in the LOS. Additionally, notable surface motion was observed in some parts of the two tailings storage facilities at the site. The results of this study illustrate the importance of InSAR techniques in mining site-wide coverage stability monitoring.

Integrated Remote Sensing for Geological and Mineralogical Mapping of Pb-Zn Deposits: A Case Study of Jbel Bou Dahar Region Using Multi-Sensor Imagery

This research applies remote sensing methodologies for the first time to comprehensively explore the geological and mineralogical characteristics of the Jbel Bou Dahar region. An integrated approach with multi-sensor satellite images, including ASTER, Landsat-8, and Sentinel-2 was applied with the aim to discriminate the different lithological units in the study area. We implemented a suite of well-established image processing techniques, including Band Ratios, Principal Component Analysis, and Spectral Angle Mapper, to successfully identify, classify, and map the spatial distribution of carbonate minerals, OH-bearing minerals, and iron oxide minerals. Due to its high spectral resolution in the short-wave infrared region (SWIR), the ASTER sensor provided the most accurate results for mapping carbonate and OH-bearing minerals compared to the Sentinel-2 and Landsat-8 sensors. Conversely, Sentinel-2 offers high spectral and spatial resolution in visible and near-infrared (VNIR) corresponding to the regions where iron oxide minerals exhibit their characteristic absorption peaks. The results confirm the advantages of remote sensing technologies in the geological and mineralogical exploration of the study area and the importance of selecting the appropriate sensors for specific mapping objectives.

A TomoSAR regularization-based method for height change detection in urban areas

Over the past years, urban change detection has always been a challenging issue for researchers. Due to the two-dimensional intrinsic property of satellite imageries and the inherent less intuitive interpretability of radar images, height change detection and SAR imagery-based change detection have been even more complicated tasks. However, TomoSAR-based height change detection is a novel topic on which only a few research works have ever been realized. This study explores the viability of tomographic-based change detection in urban areas, tackling the common challenges encountered in 2D change detection with SAR images. In contrast with the standard TomoSAR methods that usually suffer from the ill-posed inverse problem, the TomoSAR regularization-based methods are considered powerful aids for reconstructing the reflectivity function of the observed scenes. The TomoSAR regularization-based method utilizes the Constrained Least Squares (CLS) and the Weighted Constrained Least Squares (WCLS) as two efficient strategies for coming up with a well-posed solution to the prevalent ill-posed TomoSAR problem. They can assist SAR tomography to deal with the possible impairing issues arising from low numbers and the distribution of baselines. This advantage helps enhance the accuracy of the results. The experimental results are investigated in terms of completeness and correctness measures to evaluate the feasibility and reliability of the proposed method. The proposed method for height change detection has been performed on two datasets acquired by the TerraSAR-X sensor in 2013 and the Sentinel-1 sensor in 2022 over Tehran City, Iran. The final results demonstrate that the proposed algorithm can detect height changes with good feasibility and reliability in terms of completeness and correctness of better than 84% and 77%, respectively.

Land cover mapping of mixed classes using 2D CNN with multi-frequency SAR data

Synthetic aperture radar (SAR) data obtained at multiple frequencies and polarizations offers valuable complementary information for classifying mixed classes that exhibit similar backscattering response. Although deep learning-based convolutional neural networks (CNNs) effectively extract features from multi-frequency SAR data, the arbitrary ordering of SAR features may hinder optimal convolution of the best feature sub-space for a specific class and underutilize available multi-frequency data. To address this, a novel CNN transforming SAR feature-space from 1-D to 2-D and employing varied dilation-rate convolutions is introduced. This transformation maximizes unique and localized feature combinations, efficiently utilizing the available feature sub-spaces and extracting discriminative features for accurate classifications, addressing the challenge of arbitrary band neighborhoods. Utilizing dual-polarization SAR data from ALOS-2 PALSAR-2 and Sentinel-1 sensors, the proposed CNN achieves an average f-score of 0.97 and a kappa coefficient of 0.97, an improvement of 11 %, 7 % and 3 % in OA compared to the 1-D, 2-D and 3-D CNN classifiers, without feature transformation. The classifier's generalization ability is evaluated using ground truth knowledge of various heterogeneous classes, and the proposed CNN classifier outperforms others in terms of accuracy metrics and generalization ability.

A deterministic descriptive regularization-based method for SAR tomography in urban areas

ABSTRACT In recent years, Synthetic Aperture Radar (SAR) Tomography (TomoSAR) has ascertained great potential for the three-dimensional (3-D) reconstruction of observed scenes, especially in urban areas. However, the number of proceed snapshots (observations) is usually less than that of slant height samples (unknowns) in TomoSAR inversion processes. This impairs the quality of the reconstructed vertical information. To cope with this issue and improve the reliability of reconstructed vertical information, this paper investigates the possible potential of a deterministic descriptive regularization-based method. Deterministic descriptive regularization is a well-conditioned optimization framework based on the descriptive idea of a regularization solution. This strategy can help to mitigate the effect of the ill-posed problem. Thus, it can assist SAR tomography to deal with the possible impairing issues arising from low numbers and the distribution of baselines. For this purpose, the result of the proposed strategy is compared with the outcomes from the standard TomoSAR techniques, including Beamforming, Capon, and Minimum Norm. The proposed method for reconstruction of the reflectivity function of the observed scene has been performed on a dataset acquired by the Sentinel-1 sensor in 2022 over Tehran City, Iran. The experimental results indicate that the proposed algorithm can estimate building heights with a vertical accuracy of better than 91%. These results demonstrate the great potential of the proposed method for reconstructing the full 3-D images of urban areas.

A river runs through it: Robust automated mapping of riparian woodlands and land surface phenology across dryland regions

Riparian woodlands in drylands are critically important to human society, global biodiversity, and regional water and energy budgets. These sensitive ecosystems have experienced substantial degradation over the last several decades from climatic change and direct human activity. Nevertheless, quantifying long-term change in dryland riparian woodlands remains a major challenge, and much uncertainty exists in their remaining extent, historical breadth, and likely future trajectories. Dryland landscapes show large, fine-scale spatial heterogeneity in seasonal greenness patterns, driven in part by spatial variation in water availability. Riparian woodlands occur where water is concentrated in the landscape, either as aboveground streamflow or subsurface groundwater. In arid and semi-arid climates, this renders them phenologically distinctive from upland ecosystems. However, despite their importance and distinctiveness, there are currently no automated methods for delineating dryland riparian woodlands across regional extents in the cloud. Here we designed and implemented a cloud-based algorithm to retrieve dryland land surface phenology patterns from multispectral satellite imagery and conducted sensitivity analyses using real and simulated data to demonstrate that the approach is robust for MODIS, Sentinel-2, and Landsat over realistic ranges of noise and cloud cover. We then designed a series of random forest vegetation classifiers that integrate phenological and spectral information, vegetative structure from LiDAR, and topography from LiDAR or the Shuttle Radar Topography Mission. We implemented classifiers for three local study sites and then generalized our model to run regionally across the southwestern United States, with balanced accuracy for the riparian woodland class ranging from 94.5% to 97.5% when validated with local to regional datasets. Generally, phenological information proved more important than any other data source for mapping riparian woodlands, which showed more stability in interannual phenology than did upland vegetation types. To our knowledge, ours is the first regional, annual, automatically-generated and updated approach for mapping dryland riparian woodlands in the southwestern United States, paving the way for improved modeling and management efforts on watershed to regional scales. We also provide one of the first operational, exclusively cloud-based methods to extract dryland land surface phenology patterns using Landsat, Sentinel-2, MODIS, or other sensors, providing a framework for future studies investigating other aspects of long-term or spatial variation in dryland vegetative seasonality across the globe.

Mapping weed infestation in maize fields using Sentinel-2 data

Weed management in maize farms is a time-specific activity and requires timely detection of weed infestations. The challenge to early detection of weeds is that many dicotyledonous crops and broad-leaved weeds often display similar reflectance profile in the early growth stage and requires hyperspectral data to detect them. However, the advent of Sentinel-2 sensor series, with enhanced spectral configuration featuring red-edge bands that are known for species-level discrimination of plants, presents an affordable opportunity to detect weeds using multispectral data. The present study explores the question of whether Sentinel-2 sensor with its advanced spectral configuration can differentiate weeds from maize (Zea mays) in the early growth stages of maize. The study recorded 165 GPS points of weeds, maize, and mixed class in six maize farms during the early stages of maize growth. These GPS points were overlaid on Sentinel-2 images acquired within two days of field data gathering to guide the collection of spectral signatures of the maize, mixed, and weed classes. Spectral signatures were divided into training (70%) and validation (30%) data in a Random Forest (RF) model with S-2 spectral bands and vegetation indices as predictor variables. Spectral signatures were firstly tested for spectral separability between classes using ANOVA. The results of spectral analysis showed that the weed class had higher interclass variability from the maize and mixed class particularly in the red-edge and NIR regions of Sentinel-2. The classification matrix consistently showed that weeds were detected with high user and producers’ accuracy of 95%. These results indicate the utility of the enhanced spectral configuration of Sentinel-2 data in the early detection of weeds in maize farms.

Multidecadal grassland fractional cover time series retrieval for Germany from the Landsat and Sentinel-2 archives

Time series data provided by the Sentinel-2 and Landsat satellite missions offer manifold opportunities for grassland monitoring. The high intra-annual observation density of Sentinel-2 combined with the continuous long-term data record of Landsat enable analyses at seasonal, annual, and decadal scales. Fractional cover estimates of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil provide essential information to describe grassland conditions and processes. Yet, retrieving consistent grassland fractional cover time series from Landsat and Sentinel-2 imagery represents a major challenge. In this study, we implemented a multisensor spectral unmixing approach for retrieving multidecadal (i.e., 1984 to 2021) fractional cover time series of PV, NPV, and soil for Germany's permanent grasslands from the Landsat and Sentinel-2 archives. The spectral consistency of Landsat 5/7/8 and Sentinel-2A/B imagery as well as the coherency of a Sentinel-2-based spectral library to be used across Landsat and Sentinel-2 sensors served as the foundation for implementing the unmixing approach. We then employed regression-based unmixing using synthetic training data from spectral libraries for developing spatially and temporally generalized models. Applying these models to the Landsat and Sentinel-2 data facilitated multidecadal fractional cover mapping at a national-scale. We evaluated the quality of our multidecadal grassland fractional cover time series using statistical validation and linear correspondence analysis. The statistical validation was based on a multitemporal reference dataset spanning 2017 to 2021, derived from very high-resolution (VHR) imagery. Landsat 7/8- and Sentinel-2A/B-derived fractions showed similar Mean Absolute Errors (MAEs), i.e., 0.067 and 0.08 for PV, 0.149 and 0.15 for NPV, and 0.135 and 0.129 for soil. Linear correspondence analysis confirmed consistent PV and NPV fractional cover estimates among Landsat and Sentinel-2 sensors, suggesting similar errors beyond the statistical validation period. However, higher errors and weaker linear correspondence pointed to remaining uncertainties in soil fractional cover estimates. We further showed that the differences in spatial and spectral resolutions, i.e., the pixel size and the number of spectral bands, between Landsat and Sentinel-2 had a minor effect and were well mitigated by the spectral unmixing approach. We finally illustrated the value of the dense time series available for more recent years for describing seasonal trajectories of grassland conditions and land use intensities, as well as the use of the entire time series for analyzing long-term grassland dynamics based on annual fraction anomalies. Our study emphasizes the efficacy of generalized multisensor spectral unmixing approaches for retrieving consistent PV, NPV, and soil cover fractions across space, time, and sensors, providing a valuable means for grassland monitoring.

TESTING OPTICAL SPECTRAL INDICES FOR ASSESSMENT OF SURFACE CHANGES DUE TO PERMAFROST MELTING ON LIVINGSTON ISLAND, ANTARCTICA

The present study aims to examine the potential of optical satellite data and spectral indices to assess surface changes induced by permafrost melting. Surface changes related to permafrost melting on Livingston Island, Antarctica were examined using optical satellite data from Sentinel-2 sensors of the European Space Agency (ESA). The study area coincides with previous field studies by electrical resistivity tomographic profiles made to establish and visualize the presence of permafrost. Utilizing the advantages of remote sensing methods and calculation of optical indices, it was tracked whether and to what extent there was a surface change and melting of the permafrost in the study area. The observation period encompasses the astral summer seasons from 2016 to 2023. The results show that the combination of different optical indices gives a better understanding of changes in the terrain. The combined use of the Normalized Difference Glacier Index (NDGI), Normalized Difference Snow Index (NDSI), Normalized Difference Snow and Ice Index (NDSII), Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and Moisture Stress Index (MSI) indicates for a pronounced trend of melting of the active layer of the permafrost periglacial area of research in March 2016 and 2017, and from January to mid of March 2023.

Geo-Based Assessment of Vegetation Health Related to Agroecological Practices in the Southeast of Togo

In the context of climate change, the need to contribute to achieving Sustainable Development Goal (SDG) 2 is no longer in doubt, especially in sub-Saharan Africa. In this study of the landscape within 10 km of the Donomadé model farm, southeastern Togo, researchers sought to assess vegetation health in ecosystems and agrosystems, including their capacity to produce biomass for agroecological practices. Sentinel-2 sensor data from 2015, 2017, 2020, and 2022 were preprocessed and used to calculate the normalized vegetation fire ratio index (NBR), the vegetation fire severity index (dNBR), and CASA-SEBAL models. From these different analyses, it was found that vegetation stress increased across the landscape depending on the year of the time series. The research estimated that 9952.215 ha, 10,397.43 ha, and 9854.90 ha were highly stressed in 2015, 2017, and 2020, respectively. Analysis of the level of interannual severity revealed the existence of highly photosynthetic areas that had experienced stress. These areas, which were likely to have been subjected to agricultural practices, were estimated to be 8704.871 ha (dNBR2017–2015), 8253.17 ha (dNBR2020–2017), and 7513.93 ha (dNBR2022–2020). In 2022, the total available biomass estimated by remote sensing was 3,741,715 ± 119.26 kgC/ha/y. The annual average was 3401.55 ± 119.26 kgC/ha/y. In contrast, the total area of healthy vegetation was estimated to be 4594.43 ha, 4301.30 ha, and 4320.85 ha, in 2015, 2017, and 2022, respectively. The acceptance threshold of the net primary productivity (NPP) of the study area was 96%. The coefficient of skewness (0.81 ± 0.073) indicated a mosaic landscape. Productive and functional ecosystem components were present, but these were highly dispersed. These findings suggest a great opportunity to promote agroecological practices. Mulching may be an excellent technique for enhancing overall ecosystem services as targeted by the SDGs, by means of reconversion of plant biomass consumed by vegetation fires or slash-and-burn agricultural practices.

Mapping of Evapotranspiration and Determination of the Water Footprint of a Potato Crop Grown in Hyper-Arid Regions in Saudi Arabia

Seasonal quantification of a crop’s evapotranspiration (ET) and water footprint (WF) is essential for sustainable agriculture. Therefore, this study was conducted to estimate the ET and WF of an irrigated potato crop using satellite imagery of Landsat and Sentinel-2 sensors. The Simplified Surface Energy Balance (SSEB) algorithm was used to evaluate the crop water use (ETa) for potato fields belonging to the Saudi Agricultural Development Company, located in the Wadi-Ad-Dawasir region, Saudi Arabia. Normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and land surface temperature (LSD) were computed for Landsat and Sentinel-2 datasets, which were used as inputs for mapping the potato tuber yield and, subsequently, the WF. The results indicated that the NDVI showed the best accuracy for the prediction of the potato tuber yield (R2 = 0.72, P > F = 0.021) followed by the SAVI (R2 = 0.64, P > F = 0.018), compared to the field harvested actual yield (YA). A comparison between the satellite-based ETa and the actual amount of water applied (WA) for irrigation showed a good correlation (R2 = 0.89, RMSE = 4.4%, MBE = 12.9%). The WF of the potatoes in the study area was estimated at values between 475 and 357 m3 t−1 for the early (September–December) and late (December–April) growing periods, respectively. A major portion (99.2%) of the WF was accounted for from irrigation with variations of 18.5% and 3.5% for early- and late-planted potatoes, respectively, compared to the baseline (crop planted in season). In conclusion, the results showed the possibility of satisfactorily estimating the WF using the SSEB algorithm by integrating the Landsat-8 and Sentinel-2 datasets. In general, the high rates of ET in the early planting season led to higher WF values compared to the in-season and late planting dates; this will help in selecting suitable planting dates for potato crops in the study area and areas with similar environments, which enhances the opportunities for sustainable management of irrigation water.

Estimating net primary productivity of semi-arid Crimean pine stands using biogeochemical modelling, remote sensing, and machine learning

The aim of the paper was to predict net primary productivity (NPP) in pure Pinus nigra J.F. Arnold (Crimean pine) stands by consecutively implementing remote sensing, biogeochemical modelling, and machine learning techniques. In this context, NPP was estimated using Carnegie-Ames-Stanford Approach (CASA). Following, NPP was re-modelled with spectral characteristics of the P.nigra using multi-temporal remotely sensed images (Landsat 8 OLI and Sentinel-2), land use, soils and meteorological information in a total of 180 temporary sample plots. The model results were validated using litterfall samples from 30 stations for each forest stand, including needle, branch, cone, bark, male flower, and others. The highest relationship was between NPP and male flowers (r =‐−0.75). In addition, reflectance (R), vegetation indices (VI) and texture (TEX) values (calculated according to filter and degree) for each sample plot were calculated from each sensor. Multiple linear regression (MLR) was applied to define the best subset to model the NPP values with R, VI and TEX values using MLR, support vector machines (SVM) and deep learning (DL) methods. The best prediction accuracy was obtained in TEX data in the SVM method and Sentinel-2 sensor combination. NPP testing determination co-efficiency (R2) values were 0.95. The performance of the male flower litterfall in the validation control was promising for the modelling of NPP in Crimean pine. The TEX properties of the satellite images were well reflected by using different filters, degrees, and functions, resulting in achieving a high success.