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This study evaluated the feasibility of using free multispectral remote sensing data from Sentinel-2A satellites to predict soil properties in Northern Karnataka, India. Sentinel-2A images were downloaded for selected sites, covering Vertisol, Ultisol, and Alfisol soils. Multiple linear regression (MLR) models incorporated four Sentinel-2 bands and six spectral indices (NDVI, GNDVI, SAVI, TVI, EVI, and BI) as independent variables, with soil properties as dependent variables. Surface samples (0–15 cm depth) were collected from March to May 2022. The analysis showed significant correlations between individual bands and soil properties, with variations in Organic Carbon (OC) compared to sand, silt, clay, and pH. Sand positively correlated with all spectral indices, while silt, clay, and pH were negatively correlated. The red and Near-Infrared (NIR) bands showed a non-significant relationship with OC. No significant correlation was found between EVI and the soil properties. Strong regression coefficients were observed between Sentinel-2 predictions and laboratory measurements: sand (r² = 0.63), silt (r² = 0.73), clay (r² = 0.59), and pH (r² = 0.59). These results demonstrate the potential of Sentinel-2 data for predicting soil properties, offering a valuable tool for managing unsampled agricultural fields.

With the development of multispectral imaging technology, retrieving soil heavy metal content using multispectral remote sensing images has become possible. However, factors such as soil pH and spectral resolution affect the accuracy of model inversion, leading to low precision. In this study, 242 soil samples were collected from a typical area of the Pearl River Delta, and the Cu content in the soil was detected in the laboratory. Simultaneously, Sentinel-2 remote sensing image data were collected, and two-dimensional and three-dimensional spectral indices were established. Constructing independent decision trees based on pH values, using the Successive Projections Algorithm (SPA) combined with the Boruta algorithm to select the characteristic bands for soil Cu content, and this was combined with Optuna automatic hyperparameter optimization for ensemble learning models to establish a model for estimating Cu content in soil. The research results indicated that in the SPA combined with the Boruta feature selection algorithm, the characteristic spectral indices were mainly concentrated in the spectral transformation forms of TBI2 and TBI4. Full-sample modeling lacked predictive ability, but after classifying the samples based on soil pH value, the R2 of the RF and XGBoost models constructed with the samples with pH values between 5.85 and 7.75 was 0.54 and 0.76, respectively, with corresponding RMSE values of 22.48 and 16.12 and RPD values of 1.51 and 2.11. This study shows that the inversion of soil Cu content under different pH conditions exhibits significant differences, and determining the optimal pH range can effectively improve inversion accuracy. This research provides a reference for further achieving the efficient and accurate remote sensing of heavy metal pollution in agricultural soil. [ABSTRACT FROM AUTHOR]

Introduction: Technical limitations in classifying heterogeneous wetland environments, characterized by diverse vegetation cover, land use, and species diversity, often lead to interference in classification results and reduced accuracy in differentiating vegetation classes within wetland ecosystems. There is limited research available to improve classification methods in wetland environments. The main objective of this study is to investigate the combination of multi-spectral and radar data in improving the classification methods of wetland environments and to provide a method for fine separation of different plant covers in these biodiversity environments. In order to better examine the changes of the spectral index during a year, the open-source system of Google Earth Engine is used so that the spectral behavior of the phenomena during the year can be accurately studied. Material and Methods: In this study, a combination of Sentinel-1 and Sentinel-2 data was used as the first data series, and a combination of Sentinel-2 data with spectral indices such as NDVI, SAVI, and mNDWI was used as the second data series. The best image for each season (summer, autumn, winter, and spring) from 2016 to 2022 was selected to create classification maps and examine detailed changes in the wetland. For image classification, training areas were selected based on field sampling, combining satellite imagery and Google Earth images. Classification was performed using three supervised algorithms: Support Vector Machine, Artificial Neural Network, and Maximum Likelihood. Also, the index map was prepared in the Google Earth Engine system and the indices were calculated using the ready-made products available in this system and were reviewed monthly for one year. To ensure the classification and to evaluate the classification accuracy, the most common accuracy estimation parameters, overall accuracy, producer accuracy, user accuracy and Kappa coefficient were used. Results and Discussion: The results indicated that the combination of Sentinel-1 and Sentinel-2 data yielded better results compared to the combination of Sentinel-2 data with spectral indices. The overall accuracy and Kappa coefficient for the four periods were 92.99%, 87.43%, 83.80%, and 97.90% (in 2016, 2017, January 2022, and July 2022, respectively) when using the combination of Sentinel-1 and Sentinel-2 data, which were significantly higher than the results obtained with the combination of Sentinel-2 data and spectral indices. Furthermore, the combination of Sentinel-1 and Sentinel-2 data resulted in better detection of water bodies and lotus habitats within the wetland. NDVI, SAVI and mNDWI have a high correlation in examining the changes, so that an increasing trend was observed in the first six months of the year and a decreasing trend in the second six months, and the trend of vegetation and water changes is the same. Conclusion: Due to the complexity of wetland spatial structures and existing threats, identifying land cover types is challenging. This study demonstrates the use of multi-temporal Sentinel-1 and Sentinel-2 data to comprehensively assess wetland characteristics. The accuracy assessment for the four study periods from 2016 to 2022 using three classification algorithms, Support Vector Machine, Maximum Likelihood, and Artificial Neural Network, showed that the combination of Sentinel-2 and Sentinel-1 data outperformed the combination of Sentinel-2 data with spectral indices in terms of overall accuracy and Kappa coefficient. Among the three algorithms used, the Maximum Likelihood algorithm consistently achieved the highest overall accuracy and Kappa coefficient compared to the other two algorithms. [ABSTRACT FROM AUTHOR]

Desertification is the deterioration of land brought on by human activity, climate change, and a loss of vegetation cover and biodiversity. This paper assesses the agricultural drought and desertification levels of Khanaqin district in Diyala province, Iraq, using Sentinel-2 images with a high resolution of 10 m between July 22, 2016, and July 22, 2022. The Modified Soil-Adjusted Vegetation Index (MSAVI2), Topsoil Grain Size Index (TGSI), and Salinity Index (SI) derived from Sentinel-2 satellite images were used for this aim. The result showed that the area covered by low desertification intensity increased from (12.05%-9.41%) for the years (2016-2022). The area covered by high desertification intensity increased from (32.49-36.44% for the years (2016-2022), indicating an accelerated desertification process. Finally, the area covered by high desertification intensity increased from 12.34%-21.23% for years (2016-2022). Natural climate variability and human activities, such as land use, overgrazing, deforestation, and unsustainable farming practices, cause agricultural drought and desertification. [ABSTRACT FROM AUTHOR]

Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques, and geographic information systems (GIS) to assess fire severity. By evaluating various remote sensing attributes from the Sentinel-2 and Planetscope satellites, we compared different methodologies for fire severity classification. Specifically, we examined the effectiveness of reflectance indices-based metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2), alongside machine learning algorithms including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), implemented in ArcGIS Pro 3.1.0. Our analysis revealed promising results, particularly in identifying high-severity fire areas. By comparing the output of our methods with ground truth data, we demonstrated the robust performance of our approach, with both SVM and CNN achieving accuracy scores exceeding 0.84. An innovative aspect of our study involved semi-automating the process of training sample labeling using spectral indices rasters and masks. This approach optimizes raster selection for distinct fire severity classes, ensuring accuracy and efficiency in classification. This research contributes to the broader understanding of forest fire dynamics and provides valuable insights for fire management and environmental monitoring efforts in Algeria and similar regions. By accurately mapping fire severity, we can better assess the impacts of climate change and land use changes, facilitating proactive measures to mitigate future fire incidents. [ABSTRACT FROM AUTHOR]

Estimating various parameters such as the physical parameters of the soil using remote sensing is not easy. Therefore, intelligent statistical models such as M5 tree decision-making algorithms should be used for estimation. This study aimed to compare the effectiveness of Landsat 8 and Sentinel 2 satellite images in estimating soil surface moisture between Gorgan and Aqqala. Data mining methods were used to achieve this, including the stepwise M5 tree decision-making algorithm and Multivariate Linear Regression (MLR). A total of 151 samples were taken from the soil surface in the study area, and their Soil Moisture (SM) content was calculated. Spectral reflectance information was also obtained from satellite images. A data matrix was then created by computing spectral indices such as Normalized Difference Vegetation Index (NDVI), Normalized Multi-Band Drought Index (NMDI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI) for data mining purposes. The MLR method can estimate SM with 60–79% accuracy. To improve the model accuracy and overcome algorithm greediness, the m5 model was implemented in a stepwise algorithm. As a result, SM can be estimated with 80–89% accuracy for Sentinel 2 satellite and Landsat 8. Landsat 8 showed the highest accuracy among the Landsat satellites. Given the wide coverage of satellite images, improving the accuracy of results can make these satellites a great tool for obtaining information and managing various sectors, including soil management in agriculture. [ABSTRACT FROM AUTHOR]

Background: Fire behaviour assessments of past wildfire events have major implications for anticipating post-fire ecosystem responses and fuel treatments to mitigate extreme fire behaviour of subsequent wildfires. Aims: This study evaluates for the first time the potential of remote sensing techniques to provide explicit estimates of fire type (surface fire, intermittent crown fire, and continuous crown fire) in Mediterranean ecosystems. Methods: Random Forest classification was used to assess the capability of spectral indices and multiple endmember spectral mixture analysis (MESMA) image fractions (char, photosynthetic vegetation, non-photosynthetic vegetation) retrieved from Sentinel-2 data to predict fire type across four large wildfires Key results: MESMA fraction images procured more accurate fire type estimates in broadleaf and conifer forests than spectral indices, without remarkable confusion among fire types. High crown fire likelihood in conifer and broadleaf forests was linked to a post-fire MESMA char fractional cover of about 0.8, providing a direct physical interpretation. Conclusions: Intrinsic biophysical characteristics such as the fractional cover of char retrieved from sub-pixel techniques with physical basis are accurate to assess fire type given the direct physical interpretation. Implications: MESMA may be leveraged by land managers to determine fire type across large areas, but further validation with field data is advised. Satellite products with a direct physical meaning were accurate for estimating fire behaviour (surface fire, intermittent crown fire, and continuous crown fire) of past wildfire events across large burned Mediterranean ecosystems. This has major implications for anticipating post-fire ecosystem responses (e.g. delayed tree mortality). [ABSTRACT FROM AUTHOR]

The quantitative assessment of forest fire severity is significant for understanding the changes in ecological processes caused by fire disturbances. As a novel spectral index derived from the multi-objective optimization algorithm, the Analytic Burned Area Index (ABAI) was originally designed for mapping burned areas. However, the performance of the ABAI in detecting forest fire severity has not been addressed. To fill this gap, this study utilizes a ground-based dataset of fire severity (the composite burn index, CBI) to validate the effectiveness of the ABAI in detecting fire severity. First, the effectiveness of the ABAI regarding forest fire severity was validated using uni-temporal images from Sentinel-2 and Landsat 8 OLI. Second, fire severity accuracy derived from the ABAI with bi-temporal images from both sensors was evaluated. Finally, the performance of the ABAI was tested with different sensors and compared with representative spectral indices. The results show that (1) the ABAI demonstrates significant advantages in terms of accuracy and stability in assessing fire severity, particularly in areas with large numbers of terrain shadows and severe burn regions; (2) the ABAI also shows great advantages in assessing regional forest fire severity when using only uni-temporal remotely sensed data, and it performed almost as well as the dNBR in bi-temporal images. (3) The ABAI outperforms commonly used indices with both Sentinel-2 and Landsat 8 data, indicating that the ABAI is normally more generalizable and powerful and provides an optional spectral index for fire severity evaluation. [ABSTRACT FROM AUTHOR]

Knowledge of soil volumetric water content (VWC) on agricultural soils as influenced by different soil management practices is important, but the measurement outputs of frequently used traditional sampling techniques are restricted to point-based measurements with limited spatial coverage. Remote sensing (RS) techniques are therefore being explored because of their greater spatial and temporal availability as well as their ability to cover large-scale areas. But a general limitation for RS data is the presence of vegetation cover, cloud cover and the effect of topography. To minimize the effects of these factors during field sampling, the use of spectral indices and terrain attributes have proven successful in the estimation of several soil properties; however, the impact of these approaches for the estimation and mapping of soil VWC, an important factor in crop growth and development, especially where different soil management practices are employed, remains limited. To contribute to the knowledge base of RS under varying soil systems, this study explores the possibility of combining in-situ measurements with remotely sensed (explanatory variables) data obtained under four different tillage practices to produce an estimated soil VWC that represents the entire study field. The tillage practices used include reduced till (RT), strategic till (ST), no-till (NT) and conventional till (CT). From these tillage plots, three explanatory datasets, namely Sentinel-2 (S2), spectral indices (SI) and terrain attributes (TA), were collected as predictors. In addition, each of the explanatory variables was structured into four groups, representing each of the four tillage methods. The eXtreme Gradient Boosting (XGBoost) model was used, and the best results were selected based on the root mean squared error (RMSE), the coefficient of determination (R2) and the mean absolute error (MAE). Prior to soil VWC prediction, the Pearson correlation matrix was used to determine the linear relationship between each of the selected explanatory variables (S2 bands, SI and TA) and soil VWC. Furthermore, spatial distribution maps of soil VWC were constructed using the inverse distance weighting (IDW) interpolation technique. For soil VWC estimation, the TA outperformed the SI and S2 datasets (R2 = 0.84). Similarly, the spatial distribution maps obtained from the TA data show a study area with a large concentration of VWC compared with the other datasets. The study also identified CT as the tillage approach that most impacts soil VWC estimation because all predicted results were poor without the addition of data from the CT plot. According to our findings, using TA data collected from various tillage management systems to estimate soil VWC is very promising because they can be used as predictors to improve soil VWC estimation and mapping. This study demonstrates conclusively that remote sensing data collected from different tillage management systems can be used as predictors to enhance the estimation and mapping of soil VWC, complementing the basis for the development of reliable and consistent precision irrigation management systems. [ABSTRACT FROM AUTHOR]

Understanding spatial and temporal variability in soil organic carbon (SOC) content helps simultaneously assess soil fertility and several parameters that are strongly associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration. Therefore, it appears necessary to monitor SOC regularly and investigate rapid, non-destructive, and cost-effective approaches for doing so, such as proximal and remote sensing. To increase the accuracy of predictions of SOC content, this study evaluated combining remote sensing time series with laboratory spectral measurements using machine and deep-learning algorithms. Partial least squares (PLS) regression, random forest (RF), and deep neural network (DNN) models were developed using Sentinel-2 (S2) time series of 58 sampling points of bare soil and according to three approaches. In the first approach, only S2 bands were used to calibrate and compare the performance of the models. In the second, S2 indices, Sentinel-1 (S1) indices, and S1 soil moisture were added separately during model calibration to evaluate their effects individually and then together. In the third, we added the laboratory indices incrementally and tested their influence on model accuracy. Using only S2 bands, the DNN model outperformed the PLS and RF models (ratio of performance to the interquartile distance RPIQ = 0.79, 1.36 and 1.67, respectively). Additional information improved performances only for model calibration, with S1 soil moisture yielding the most stable improvement among three iterations. Including equivalent indices of the S2 indices calculated using soil spectra obtained under laboratory conditions improved prediction of SOC, and the use of only two indices achieved good validation performances for the RF and DNN models (mean RPIQ = 2.01 and 1.77, respectively). [ABSTRACT FROM AUTHOR]

Forest fires have become a major concern in the northern parts of Morocco, particularly in the Tangier-Tetouan-Al Hoceima (TTA) region, causing significant damage to the environment and human lives. To address this pressing issue, this study proposes an approach that utilizes remote sensing (RS) and machine learning (ML) techniques to detect burned areas in the TTA region within the Google Earth Engine platform. The study focuses on burned areas resulting from forest fires in three specific locations in the TTA region that have experienced such fires in recent years, namely Tangier-Assilah in 2017, M'diq Fnideq in 2020, and Chefchaouen in 2021. In our study, we extensively explored multiple combinations of spectral indices, such as normalized burn ratio (dNBR), normalized difference vegetation index (dNDVI), soil-adjusted vegetation index (dSAVI), and burned area index (dBAI), in conjunction with Sentinel-2 (S2) satellite images. These combinations were employed within the Random Forest (RF) algorithm, allowing us to draw important conclusions. Initially, we assess the individual effectiveness of the dNBR index, which yields accuracy rates of 83%, 90%, and 82% for Tangier-Assilah, Chefchaouen, and M'diq Fnideq, respectively. Recognizing the need for improved outcomes, we expand our analysis by incorporating spectral indices and S2 bands. However, the results obtained from this expanded combination lack consistency and stability across different locations. While Tangier-Assilah and M'diq Fnideq experience accuracy improvements, reaching 95% and 88%, respectively, the inclusion of Sentinel bands has an adverse effect on Chefchaouen, resulting in a decreased accuracy of 87%. To achieve optimal accuracy, our focus shifted towards the combination of dNBR and the other spectral indices. The results were truly remarkable, with accuracy rates of 96%, 97%, and 97% achieved for Tangier-Assilah, Chefchaouen, and M'diq Fnideq, respectively. Our decision to prioritize the spectral indices was based on the feature importance method, which highlights the significance of each feature in the classification process. The practical implications of our study extend to fire management and prevention in the TTA region. The insights gained from our analysis can inform the development of effective policies and strategies to mitigate the impact of forest fires. By harnessing the potential of RS and ML techniques, along with the utilization of spectral indices, we pave the way for enhanced fire monitoring and response capabilities in the region. [ABSTRACT FROM AUTHOR]

One of the most important challenges of agriculture today is increasing its productivity gains, while controlling its environmental footprint. Because of that plastic covered greenhouses (PCG) mapping via remote sensing is receiving a great attention throughout this century. In this study, a fair comparison was carried out in four PCG study areas around the world to test 14 spectral indices mainly focused on the detection of plastic. To the best knowledge of the authors, this is the first research that fairly compares all these spectral indices in such variable number of study sites. The applied OBIA approach was based on the combined use of very high-resolution satellite data (Deimos-2 pansharpened images) to address the segmentation process and Sentinel-2 time series to compute the spectral indices. When dealing with Sentinel-2 single images, the Plastic GreenHouse Index (PGHI) stood out among all the indices tested in the study areas dedicated to the cultivation of vegetables, such as the cases of Almería (Spain), Agadir (Morocco) and Antalya (Turkey). Better Overall Accuracy (OA) values of 94.09%, 92.27%, 92.77% and 92.17% were achieved for Almería, Agadir, Bari and Antalya study sites, respectively, when using statistical seasonal spectral indices based on Sentinel-2 time series, being the maximum and mean values of PGHI (MAX (PGHI) and MEAN (PGHI)) the best ranked. Meanwhile, the PCG area of Bari (Italy), with a monoculture in vineyards, presented the worst and most irregular results.

This paper presents empirical models developed through stepwise multiple linear regression to estimate the live fuel moisture content (LFMC) in a Mediterranean area. The models are based on LFMC data measured in 50 field plots, considering four groups with similar bioclimatic characteristics and vegetation types (trees and shrubs). We also applied a species-specific LFMC model for Rosmarinus officinalis in plots with this dominant species. Spectral indices extracted from Sentinel-2 images and their averages over the study time period in each plot with a spatial resolution of 10 m were used as predictors, together with interpolated meteorological, topographic, and seasonal variables. The models achieved adjusted R2 values ranging between 52.1% and 74.4%. Spatial and temporal variations of LFMC in shrub areas were represented on a map. The results highlight the feasibility of developing satellite-derived LFMC operational empirical models in areas with various vegetation types and taking into account bioclimatic zones. The adjustment of data through GAM (generalized additive models) is also addressed in this study. The different error metrics obtained reflect that these models provided a better fit (most adjusted R2 values ranged between 65% and 74.1%) than the linear models, due to GAMs being more versatile and suitable for addressing complex problems such as LFMC behavior. [ABSTRACT FROM AUTHOR]

• Performance of Sentinel-2 and Landsat-8 based models has been compared for predicting canopy cover using the RF model. • Sentinel-2 based model was superior compared to Landsat-8 based model. • Use of suitable parameters value of RF model is effective to maximize the model performance. • SWIR bands and indices were important predictor variables and highly correlated with canopy cover. Quantifying canopy cover using Random Forest (RF) model's appropriate tuning parameters value and sensor based predictor variables is always challenging, especially in fragmented dry deciduous forests. Therefore, this study was designed to compare the performances of Sentinel-2 and Landsat-8 based models using the RF model for predicting canopy cover with assessing variables' relative importance and correlation. Sentinel-2 and Landsat-8 based bands and spectral indices were used as predictor variables. We compared different mtry, ntree and bag fraction values of the RF model. R-square (R2) and root mean square error (RMSE) were used for comparing the model performance. The results showed that the lowest RMSE value was associated with the default value (predictors/3) or more than the default value of mtry, with bag fraction 0.3–0.7 for Sentinel-2 and 0.3–0.4 for Landsat-8. Model accuracy has increased and stabilized with increase of ntree, and received the lowest RMSE to ntree of more than 1000. Except for SWIR indices based model of Landsat-8, all other Landsat-8 based model's accuracy was lesser compared to Sentinel-2 based models. Model accuracy of Sentinel-2 based full model (except red edge indices) was marginally better (R2 = 0.899, RMSE = 6.883 %) than Landsat-8 based full model (R2 = 0.886, RMSE = 7.089 %). But with the incorporation of red edge indices, full model RMSE had decreased further from 6.883 % to 6.747 %, and R2 had increased from 0.899 to 0.918. The full model of Sentinel-2 tended to spread variable importance among more variables, but the full model of Landsat-8 slightly tends to concentrate variable importance with fewer variables. However, SWIR bands and indices were the most important predictor variables and highly correlated with canopy cover. These findings can solve the parameter value choice of RF model, and the use of the Sentinel-2 based model will be superior to Landsat-8 based model. [ABSTRACT FROM AUTHOR]

The impacts of fires on society and the environment are wide-ranging, and examining such fire incidents is of critical importance to communities, governments, and scientists. Satellite imagery analysis enables the monitoring of burned scars and can help us better understand the social, ecological, and economic consequences of fires in more detail. However, most satellite studies measuring fire area and impacts focus on large areas in natural landscapes, typically forests. We highlight the need to develop methods for fire detection and analysis in human-dominated landscapes, specifically, such vulnerable populations as those found in refugee camps globally. We calculate a suite of spectral indices (SI’s) developed from Sentinel-2 data with a random forest algorithm to quantify the fire impact on the Rohingya refugee camps in Teknaf, Bangladesh. For this study, we developed a method called the Fractional Charred Index (FCI) using the field plots within the burn areas inside the refugee camp settlements and compared the FCI with differenced Spectral Indices (dSI’s) to quantitively assess the burn severity at four levels: (i) high severity, (ii) moderate severity, (iii) low severity and (iv) unburned camp. Our study identified 140 acres of burn area, damaging over 8,000 refugee shelters, 1,760 of which were severely damaged and 3,452 of which suffered moderate damage. The FCI-dSIs relationship was measured by a simple linear model which indicated that the differenced Normalized Burned Index (dNBR) and the differenced Char Soil Index (dCSI) have the strongest positive correlation and can be used interchangeably to assess the scale and intensity of the fire-caused damage and fire severity assessment in this and similar types of case studies. The novel method presented here to determine fire severity in human-dominated landscapes, specifically refugee camps, may be quickly deployed and is easily interpretable and computationally inexpensive. Therefore, the proposed method of identifying burned settlements in refugee camps has significant potential for impact monitoring, disaster management, and recovery efforts after such fire events.

Forest fires cause environmental and economic damage, destroy large areas of land and displace entire communities. Accurate extraction of fire-affected areas is of vital importance to support post-fire management strategies and account for the environmental impact of fires. In this paper, an analytical burned area index, called ABAI, was proposed to map burned areas from the newly launched Sentinel-2 images. The innovation of this method is to separate the fire scars from other typical land covers by formulating different objective functions, which involved three main components: First, spectral differences between the burned land and other land covers were characterized by analyzing the spectral features of the existing burned area indices. Then, for each type of land cover, we formed an objective function by linear combination of bands with the values of band ratios. Second, all the objective functions and possible constraints were formulated as a multi-objective optimization problem, and then it was solved using a linear programming approach. Finally, the ABAI spectral index was achieved with the optimizing coefficients derived from the multi-objective problem. To validate the effectiveness of the proposed spectral index, three experimental datasets, clipped from Sentinel-2 images at different places, were tested and compared with baseline indices, such as normalized burned area (NBR) and burned area index (BAI) methods. Experimental results demonstrated that the injection of a green band to the spectral index has led to good applicability in burned area detection, where the ABAI can avoid most of the confusion presented by shadows or shallow water. Compared to other burned area indices, the proposed ABAI achieved the best classification accuracy, with the overall accuracy being over 90%. Visually, our approach significantly outperforms other spectral indexed methods, especially in confused areas covered by water bodies and shadows. [ABSTRACT FROM AUTHOR]

The understanding of the spatial and temporal dynamics of farmland processes is essential to ensure the proper crop monitoring and early decision making needed to support efficient resource management in agriculture. By creating appropriate crop management strategies, one can increase harvest efficiency while reducing costs, waste, chemical spraying, and inhibiting the impact of biotic and abiotic factors on crop stress. Only reliable spatial information makes it possible to comprehend the influence of various factors on the environment. The main objective of the research presented in the paper was to assess the possibility of using maps of vegetation and soil indices, such as NDVI, SAVI, IRECI, CIred-edge, PSRI and HMSSI, calculated on the basis of images from the Sentinel-2 satellite, to qualitatively determine the increased amount of heavy metals in the soil in the areas of small agricultural plots around the Barania Góra nature reserve in Poland. The conducted pilot project shows that the spectral indices: NDVI, SAVI, IRECI, CIred-edge, PSRI, and HMSSI, calculated on the basis of images from Sentinel-2, have the potential to assess the content of nickel zinc, chromium and cobalt in the soil on agricultural plots. However, the confirmation of the obtained results requires continuation of the research. [ABSTRACT FROM AUTHOR]

The assessment of flood disasters is considered an essential factor in land use management, being necessary to understand and define the magnitude of past events. In this regard, several flood diagnoses have been developed using Sentinel-2 multispectral imagery, especially in large water bodies. However, one of the main challenges is still related to floods, where water surfaces have sizes similar to the spatial resolution of the analyzed satellite images, being difficult to detect and map. Therefore, the present study developed a combined methodology for flood mapping in small-sized water bodies using Sentinel-2 MSI imagery. The method consisted of evaluating the effectiveness of the application and combination of (a) a super-resolution algorithm to improve image resolution, (b) a set of seven spectral indices for highlighting water-covered areas, such as AWE indices, and (c) two methods for flood mapping, including a machine learning method based on unsupervised classification (EM cluster) and 14 thresholding methods for automatic determination. The processes were evaluated in the Carrión River, Palencia, Spain. It was determined that the approach with the best results in flood mapping was the one that combined AWE spectral indices with methods such as Huang and Wang, Li and Tam, Otsu, moment preservation, and EM cluster classification, showing global accuracy and Kappa coefficient values higher than 0.88 and 0.75, respectively, when applying the quantitative accuracy index. [ABSTRACT FROM AUTHOR]

For proper forest management, accurate detection and mapping of burned areas are needed, yet the practice is difficult to perform due to the lack of an appropriate method, time, and expense. It is also critical to obtain accurate information about the density and distribution of burned areas in a large forest and vegetated areas. For the most efficient and up-to-date mapping of large areas, remote sensing is one of the best technologies. However, the complex image scenario and the similar spectral behavior of classes in multispectral satellite images may lead to many false-positive mistakes, making it challenging to extract the burned areas accurately. This research aims to develop an automated framework in the Google Earth Engine (GEE) cloud computing platform for detecting burned areas in Andika and Behbahan, located in the south and southwest of Iran, using Sentinel-2 time-series images. After importing the images and applying the necessary preprocessing, the Sentinel-2 Burned Areas Index (BAIS2) was used to create a map of the Primary Burned Areas (PBA). Detection accuracy was then improved by masking out disturbing classes (vegetation and water) on the PBA map, which resulted in Final Burned Areas (FBA). The unimodal method is used to calculate the ideal thresholds of indices to make the proposed method automatic. The final results demonstrated that the proposed method performed well in both homogeneous and heterogeneous areas for detecting the burned areas. Based on a test dataset, maps of burned areas were produced in the Andika and Behbahan regions with an overall accuracy of 90.11% and 92.40% and a kappa coefficient of 0.87 and 0.88, respectively, which were highly accurate when compared to the BAIS2, Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Mid-Infrared Bispectral Index (MIRBI), and Normalized Difference SWIR (NDSWIR) indices. Based on the results, accurate determination of vegetation classes and water zones and eliminating them from the map of burned areas led to a considerable increase in the accuracy of the obtained final map from the BAIS2 spectral index. [ABSTRACT FROM AUTHOR]

The monitoring of burned areas can easily be performed using satellite multispectral images: several indices are available in the literature for highlighting the differences between healthy vegetation areas and burned areas, in consideration of their different signatures. However, these indices may have limitations determined, for example, by the presence of clouds or water bodies that produce false alarms. To avoid these inaccuracies and optimize the results, this work proposes a new index for detecting burned areas named Normalized Burn Ratio Plus (NBR+), based on the involvement of Sentinel-2 bands. The efficiency of this index is verified by comparing it with five other existing indices, all applied on an area with a surface of about 500 km2 and covering the north-eastern part of Sicily (Italy). To achieve this aim, both a uni-temporal approach (single date image) and a bi-temporal approach (two date images) are adopted. The maximum likelihood classifier (MLC) is applied to each resulting index map to define the threshold separating burned pixels from non-burned ones. To evaluate the efficiency of the indices, confusion matrices are constructed and compared with each other. The NBR+ shows excellent results, especially because it excludes a large part of the areas incorrectly classified as burned by other indices, despite being clouds or water bodies. [ABSTRACT FROM AUTHOR]

As wildland fires amplify in size in many regions in the western USA, land and water managers are increasingly concerned about the deleterious effects on drinking water supplies. Consequences of severe wildfires include disturbed soils and areas of thick ash cover, which raises the concern of the risk of water contamination via ash. The persistence of ash cover and depth were monitored for up to 90 days post-fire at nearly 100 plots distributed between two wildfires in Idaho and Washington, USA. Our goal was to determine the most 'cost' effective, operational method of mapping post-wildfire ash cover in terms of financial, data volume, time, and processing costs. Field measurements were coupled with multi-platform satellite and aerial imagery collected during the same time span. The image types spanned the spatial resolution of 30 m to sub-meter (Landsat-8, Sentinel-2, WorldView-2, and a drone), while the spectral resolution spanned visible through SWIR (short-wave infrared) bands, and they were all collected at various time scales. We that found several common vegetation and post-fire spectral indices were correlated with ash cover (r = 0.6-0.85); however, the blue normalized difference vegetation index (BNDVI) with monthly Sentinel-2 imagery was especially well-suited for monitoring the change in ash cover during its ephemeral period. A map of the ash cover can be used to estimate the ash load, which can then be used as an input into a hydrologic model predicting ash transport and fate, helping to ultimately improve our ability to predict impacts on downstream water resources. [ABSTRACT FROM AUTHOR]

Characterisation of native forests is essential for sustainable forest management and for maintenance of ecological and socio-economical functions. In New Zealand, knowledge of forest composition and extent informs predator control measures to protect native bird life, particularly in forests dominated by Southern beeches (Nothofagaceae). As high-resolution (> 1:50,000) maps of beech cover do not exist at national scale, we present a method to identify and map beech cover that combines multi-temporal spectral signatures from ESA’s Sentinel-2 satellite with forest plot survey data. A temporal stack of satellite imagery from 2016 to 2019 is used to derive annual metrics (mean and standard deviation) of vegetation indices which are used as input to a pixel-wise classification. A random forest classification, discriminating between beech/non-beech areas (with a beech relative cover threshold of 25%), and trained using 880 forest plots from the Land Use and Carbon Analysis System (LUCAS) natural forest network, achieved an accuracy of 87.7% (± 2.2%). This spectral classification captures both large- and local-scale spatial patterns of beech cover, which is confirmed by field visits and multi-source species occurrence information.

Accurate mapping of burned area is of key importance for fire emissions modeling and post-fire rehabilitation planning. In this research, Sentinel-2 data were used to analyze the difference in spectral signature between burned area and false positives from agricultural harvests. 26 fires that were mapped in the field using Global Navigation Satellite System during 2017 and 2018 were analyzed over California and Utah, USA. Individual Sentinel-2 bands and a wide range of commonly used spectral indices for burned area were tested using a spectral separability index. The separability index assessed discrimination between the classes burned area and 1) unburned area and 2) area in agricultural land that were flagged as false positive from agricultural harvest. Separability values higher than one indicate good separation and the higher the values, the better the separation. For each class, we first determined the multitemporal difference, i.e. the absolute value of the pre-minus-post-change value. Second, we compared with other classes by using the spectral separability index. We found that for the burned-to-unburned comparison the near and shortwave infrared spectral regions and spectral indices that make use of these spectral regions, were the best discriminators (separability (M) values of approximately 2), corroborating findings from earlier works. For the burned-to-agricultural false positive comparison Sentinel-2 bands 4 and 5, corresponding with the Red and Red-edge spectral regions, were the best discriminator (M−values greater than 2). Consequently, spectral indices containing the Red band show a similar strong separability of agricultural false positives. The results from our research reveal an additional layer of information that could be exploited to minimize false positives in agricultural lands in space-borne burned area products.

The accurate and timely assessment of pasture quantity and quality (i.e., nutritive characteristics) is vital for effective pasture management. Remotely sensed data can be used to predict pasture quantity and quality. This study investigated the ability of Sentinel-2 multispectral bands, convolved from proximal hyperspectral data, in predicting various pasture quality and quantity parameters. Field data (quantitative and spectral) were gathered for experimental plots representing four pasture types—perennial ryegrass monoculture and three mixtures of swards representing increasing species diversity. Spectral reflectance data at the canopy level were used to generate Sentinel-2 bands and calculate normalised difference indices with each possible band pair. The suitability of these indices for prediction of pasture parameters was assessed. Pasture quantity parameters (biomass and Leaf Area Index) had a stronger influence on overall reflectance than the quality parameters. Indices involving the 1610 nm band were optimal for acid detergent fibre, crude protein, organic matter and water-soluble carbohydrate concentration, while being less affected by biomass or LAI. The study emphasises the importance of accounting for the quantity parameters in the spectral data-based models for pasture quality predictions. These explorative findings inform the development of future pasture quantity and quality models, particularly focusing on diverse swards.

Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development in arid and semi-arid areas. Soil salinization is often characterized with significant spatiotemporal dynamics. The salt-affected soil is predominant in the Ebinur Lake region in the Northwestern China. However, detailed local soil salinity information is ambiguous at the best due to limited monitoring techniques. Nowadays, the availability of Multi-Spectral Instrument (MSI) onboard Sentinel-2, offers unprecedented perspectives for the monitoring and mapping of soil salinity. The use of MSI data is an innovative attempt for salinity detection in arid land. We hypothesize that field observations and MSI data and MSI data-derived spectral indices using the partial least square regression (PLSR) approach will yield fairly accurate regional salinity map. Based on electrical conductivity of 1:5 soil:water extract (EC) of 72 ground-truth measurements (out of 116 sample sites) and various spectral parameters, such as satellite band reflectance, published satellite salinity indices, red-edge indices, newly constructed two-band indices, and three-band indices from MSI data, we built a few inversion models in an attempt to produce the regional salinity maps. Different algorithms including Pearson correlation coefficient method (PCC), variable importance in projection (VIP), Gray relational analysis (GRA), and random forest (RF) were applied for variable selection. The results suggest that both the newly proposed normalized difference index (NDI) [(B12 − B7) / (B12 + B7)] and three-band index (TBI4) [(B12 − B3) / (B3 − B11)] show a better correlation with validation data and could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the remaining 44 independent ground-based measurements. The RF-PLSR model performed the best across the five models with R2 V , RMSE V , and RPD of 0.92, 7.58 dS m−1, and 2.36, respectively. The result from this model was then used to map the soil salinity over the study area. Our analyses suggest that soil salinization changes quite significantly in different seasons. Specifically, soil salinity in the dry season was higher than in the wet season, mostly in the lake area and nearby shores. We contend that the results from the study will be useful for soil salinization monitoring and land reclamation in arid or semi-arid regions outside the current study area. • The introduction of red-edge bands can enhance the sensitivities of the indices to soil salinity. • Three-band index [(B12 − B3) / (B3 − B11)] shows a best correlation (r = 0.544) with measured EC. • RF-PLSR model was proved a suitable method for soil salinity estimating and mapping. • The study shows a large variability in soil salinity in dry and wet seasons. [ABSTRACT FROM AUTHOR]

The main problem related to exploitation of reservoirs is the overgrowth of aquatic vegetation, which leads to the gradual disappearance of water bodies. Currently, satellite imagery data are an advantageous source for monitoring aquatic vegetation. The main goal of this study was to assess different spectral indices (ARVI, NDVI, NDCI, NDAVI, WAVI) for detection of the overgrowing process in reservoirs. Three reservoirs located in the western part of Poland were selected for analysis: Przebędowo, Jeżewo and Jezioro Kowalskie. The analysis was carried out on the basis of Sentinel-2 satellite imagery. All calculations were performed in ArcGIS 10.5 and Quantum GIS software. Results obtained for each of the spectral indices were verified on the basis of high-resolution color orthophotomaps. The results show that selected indices detect different overgrowth areas. The WAVI index shows the strongest agreement with reference data. The agreement between reference data for each pixel was calculated on the basis of the Kappa coefficient. Regardless of analyzed reservoir, the WAVI index has the highest value of the Kappa coefficient. Additionally, the analysis suggests that Sentinel-2 data can be used to identify emergent plant areas for reservoirs characterized by elongation ratio, width and inundation area. The highest uncertainty of results is shown by emergent plants characterized by small, dispersed areas and located near the banks of the water bodies. [ABSTRACT FROM AUTHOR]

Liana removal – the cutting of over-abundant woody climbing plants (lianas) – has the potential to substantially increase tree growth and biomass accumulation across millions of hectares of degraded tropical forest. Satellite imagery could provide data capable of observing the effect of liana removal on the forest canopy, enabling the large-scale monitoring and validation of liana removal, which remains a key hurdle to its widespread implementation. Using a 320-ha liana removal experiment in Sabah, Malaysian Borneo, we tested whether a time series of Sentinel-2 images could observe the canopy signature of liana removal. Calculating a range of metrics derived from the Normalized Burn Ratio – a vegetation index based on spectral reflectance that differentiates leaf from non-leaf – we quantified satellite-derived canopy disturbance and fragmentation across a range of liana removal intensities and examined how canopy disturbance changed in the 12-months following removal treatments. We find that liana removal significantly increases canopy disturbance and fragmentation metrics one month after removal, with partial removal having a smaller effect than complete removal. The impact of liana removal on the canopy metrics declined over time, with measures of canopy disturbance and fragmentation largely indistinguishable from control forest within 12-months of treatment. Our findings evidence that freely available satellite imagery can be used to efficiently monitor large-scale liana removal applied at a range of intensities and suggest that partial liana removal could significantly reduce canopy disturbance of this restoration method. • Liana removal can be observed using Sentinel-2-derived Normalized Burn Ratio. • Partial liana removal caused smaller canopy disturbance signals than complete removal. • Canopy disturbance metrics recovered within a year of liana removal treatment. [ABSTRACT FROM AUTHOR]

Plastic litter floating in the ocean is a significant problem on a global scale. This study examines whether Sentinel-2 satellite images can be used to identify plastic litter on the sea surface for monitoring, collection and disposal. A pilot study was conducted to determine if plastic targets on the sea surface can be detected using remote sensing techniques with Sentinel-2 data. A target made up of plastic water bottles with a surface measuring 3 m × 10 m was created, which was subsequently placed in the sea near the Old Port in Limassol, Cyprus. An unmanned aerial vehicle (UAV) was used to acquire multispectral aerial images of the area of interest during the same time as the Sentinel-2 satellite overpass. Spectral signatures of the water and the plastic litter after it was placed in the water were taken with an SVC HR1024 spectroradiometer. The study found that the plastic litter target was easiest to detect in the NIR wavelengths. Seven established indices for satellite image processing were examined to determine whether they can identify plastic litter in the water. Further, the authors examined two new indices, the Plastics Index (PI) and the Reversed Normalized Difference Vegetation Index (RNDVI) to be used in the processing of the satellite image. The newly developed Plastic Index (PI) was able to identify plastic objects floating on the water surface and was the most effective index in identifying the plastic litter target in the sea.

The improved spatial and temporal resolution of latest-generation Earth Observation missions, such as Landsat 8 and Sentinel-2, has increased the potential of remote sensing for mapping land surface phenology in inland water systems. The ability of a time series of medium-resolution satellite data to generate quantitative information on macrophyte phenology was examined, focusing on three temperate shallow lakes with connected wetlands in Italy, France, and Romania. Leaf area index (LAI) maps for floating and emergent macrophyte growth forms were derived from a semi-empirical regression model based on the best-performing spectral index, with an error level of 0.11 m 2  m −2 . Phenology metrics were computed from LAI time series using TIMESAT to analyze the seasonal dynamics of macrophyte spatial distribution patterns and species-dependent variability. Particular seasonal patterns seen in the autochthonous and allochthonous species across the three study areas related to local ecological and hydrological conditions. How characteristics of the satellite dataset (cloud cover threshold, temporal resolution, and missing acquisitions) influenced the phenology metrics obtained was also assessed. Our results indicate that, with a full-resolution time series (5-day revisit time), cloud cover introduced a bias in the phenology metrics of less than 2 days. Even when the temporal resolution was reduced to 15 days (like the Landsat revisit time) the timing of the start and the peak of macrophyte growth could still be mapped with an error of no more than 2–3 days. [ABSTRACT FROM AUTHOR]

Heavy metal stress in crops is a worldwide problem that requires accurate and timely monitoring. This study aimed to improve the accuracy of monitoring heavy metal stress levels in rice by using multiple Sentinel-2 images. The selected study areas are in Zhuzhou City, Hunan Province, China. Six Sentinel-2 images were acquired in 2017, and heavy metal concentrations in soil were measured. A novel vegetation index called heavy metal stress sensitive index (HMSSI) was proposed. HMSSI is the ratio between two red-edge spectral indices, namely the red-edge chlorophyll index (CIred-edge) and the plant senescence reflectance index (PSRI). To demonstrate the capability of HMSSI, the performances of CIred-edge and PSRI in discriminating heavy metal stress levels were compared with that of HMSSI at different growth stages. Random forest (RF) was used to establish a multitemporal monitoring model to detect heavy metal stress levels in rice based on HMSSI at different growth stages. Results show that HMSSI is more sensitive to heavy metal stress than CIred-edge and PSRI at different growth stages. The performance of a multitemporal monitoring model combining the whole growth stage images was better than any other single growth stage in distinguishing heavy metal stress levels. Therefore, HMSSI can be regarded as an indicator for monitoring heavy metal stress levels with a multitemporal monitoring model. [ABSTRACT FROM AUTHOR]

A forest fire started on August 8th, 2016 in several places on Madeira Island causing damage and casualties. As of August 10th the local media had reported the death of three people, over 200 people injured, over 950 habitants evacuated, and 50 houses damaged. This study presents the preliminary results of the assessment of several spectral indices to evaluate the burn severity of Madeira fires during August 2016. These spectral indices were calculated using the new European satellite Sentinel-2A launched in June 2015. The study confirmed the advantages of several spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVIreXn) using red-edge spectral bands to assess the post-fire conditions. Results showed high correlation between NDVI, GNDVI, NBR and NDVIre1n spectral indices and the analysis performed by Copernicus Emergency Management Service (EMSR175), considered as the reference truth. Regarding the red-edge spectral indices, the NDVIre1n (using band B5, 705 nm) presented better results compared with B6 (740 nm) and B7 (783 nm) bands. These preliminary results allow us to assume that Sentinel-2 will be a valuable tool for post-fire monitoring. In the future, the two twin Sentinel-2 satellites will offer global coverage of the Madeira Archipelago every five days, therefore allowing the simultaneous study of the evolution of the burnt area and reforestation information with high spatial (up to 10 m) and temporal resolution (5 days). [ABSTRACT FROM AUTHOR]

Fires are a problematic and recurrent issue in Mediterranean ecosystems. Accurate discrimination between burn severity levels is essential for the rehabilitation planning of burned areas. Sentinel-2A MultiSpectral Instrument (MSI) record data in three red-edge wavelengths, spectral domain especially useful on agriculture and vegetation applications. Our objective is to find out whether Sentinel-2A MSI red-edge wavelengths are suitable for burn severity discrimination. As study area, we used the 2015 Sierra Gata wildfire (Spain) that burned approximately 80 km 2 . A Copernicus Emergency Management Service (EMS)-grading map with four burn severity levels was considered as reference truth. Cox and Snell, Nagelkerke and McFadde pseudo-R 2 statistics obtained by Multinomial Logistic Regression showed the superiority of red-edge spectral indices (particularly, Modified Simple Ratio Red-edge, Chlorophyll Index Red-edge, Normalized Difference Vegetation Index Red-edge) over conventional spectral indices. Fisher's Least Significant Difference test confirmed that Sentinel-2A MSI red-edge spectral indices are adequate to discriminate four burn severity levels. [ABSTRACT FROM AUTHOR]