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and water management in the field. Therefore, its measurement has special importance. The surface soil has a great diversity in soil moisture and different methods were used to measure this property. Due to the problems of contact methods of soil moisture measurement, remote sensing has gained attention because of the possibility of analyzing and monitoring soil moisture on a large and global scale. In this research, satellite data and moisture measured in selected fields located in Hormoaz Abad Plain have been analyzed and compared. Sentinel-2 satellite data have been analyzed using the Google Earth Engine system. The results of this research showed that the use of triple indices in the OPTRAM model to estimate moisture is not very accurate, but the use of the EVI plant index has provided better results than the other two indices.

To investigate effective techniques for estimating rice production in hilly and mountainous areas, in this study, we collected yield data at the field level, agro-meteorological data, and Sentinel-2/MSI remote sensing data in Chongqing, China, between 2020 and 2023. The integral values of vegetation indicators from the rice greening up to heading–filling stages were determined using the Newton–trapezoidal integration method. Using correlation analysis and importance analysis of permutation features, the effects of agro-meteorological variables and vegetation index integrals on rice yield were assessed. The chosen characteristics were then combined with three machine learning techniques—random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR)—to create six rice yield estimate models. The results showed that combined vegetation indices were more effective than indices used in separate development phases. Specifically, the correlation coefficients between the integral values of eight vegetation indices from rice greening up to heading–filling stages and rice yield were all above 0.65. By introducing agro-meteorological factors as new independent variables and combining them with vegetation indices as input parameters, the predictive capability of the model was evaluated. The results showed that the performance of PLSR remained stable, while the prediction accuracies of SVM and RF improved by 13% to 21.5%. After feature selection, the inversion performance of all three machine learning models improved, with the RF model coupled with variables selected during permutation feature importance analysis achieving the optimal inversion effect, which was characterized by a coefficient of determination of 0.85, a root mean square error of 529.1 kg/hm2, and a mean relative error of 5.63%. This study provides technical support for improving the accuracy of remote sensing-based crop yield estimation in hilly and mountainous regions, facilitating precise agricultural management and informing agrarian decision making. [ABSTRACT FROM AUTHOR]

A monthly correlation between urban vegetation growth and potential evapotranspiration (PET) is needed for better knowledge of controlling water resources and organized irrigation processes. This study aims to explore their relationship within an urban area like Baghdad, using a linear regression model to derive a best-fit line drawn in a scatterplot on a monthly time scale. Based on two different monthly data sources: weather variables (e.g., air temperature, solar radiation, and relative humidity) and Sentinel-2 satellite imagery of 2 years, 2018 and 2021, this study presented the interannual variations of PET and normalized difference vegetation index (NDVI). The choice of these years has a significant feature of climatic differences, which are arid and semi-arid, respectively. PET values were estimated by the Truc method, while the areas of vegetation (represented by NDVI) were calculated using the Geographic Information Sensing program. The results show that the maximum PET in both years was found in the summer months (June and July) with mean values of about 8.8 mm/day, while their minimum mean values of about 1.5 mm/day occurred in winter months (January and December). From the spatial distribution of NDVI, it was found that at positive pixels when NDVI >0.2, vegetation cover in March, April, and December 2018 had large areas with more than 200 km2 in 2018, while they were largest only in May 2021 with 197.8 km2. There was a linear correlation with slope (0.03) and intercept (= 1.8) and a strong correlation, R 2 = 0.72. The practical implications of the findings contribute to enhancing a solid scientific basis for improving agricultural water management, especially under dry conditions.

It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R2 of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands. [ABSTRACT FROM AUTHOR]

Estimating bathymetric information is vital for aquaculture and navigation applications. Free, high-resolution satellite imagery provides a cost-effective solution for routine bathymetric measurements. We tested six algorithms to retrieve water depth in the Mwanza Gulf of Lake Victoria using Sentinel-2 satellite imagery: the conventional Stumpf method, Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), Neural Network (NNET), and Support Vector Machine (SVM). In-situ depth points collected via echo sounders were used to train and validate the algorithms. Performance evaluation metrics included coefficient of determination (R2), mean absolute error (MAE), root-mean-square error (RMSE), and spatial autocorrelation of residuals. Among the algorithms tested, the Stumpf model exhibited moderate performance with an R2 of 0.441, higher MAE (2.078 m), and RMSE (2.964 m) values. The RF algorithm improved performance with an R2 of 0.957, lower MAE (0.476 m), and RMSE (0.823 m). The GBM and XGB algorithms achieved R2 values of 0.960 and 0.956, respectively, with low MAE (0.484 m for GBM, 0.482 m for XGB) and RMSE (0.795 m for GBM, 0.830 m for XGB) values. The NNET algorithm outperformed the GBM and XGB models, obtaining an R2 of 0.963, the lowest MAE (0.438 m), and RMSE (0.761 m). The SVM algorithm demonstrated the best performance with an R2 of 0.965, the lowest MAE (0.403 m), and RMSE (0.745 m), implying the highest accuracy in depth estimation. SVM also showed stable generalization across different locations with insignificant spatial autocorrelation of residuals. Therefore, SVM is recommended for repetitive bathymetry calculations.

To investigate effective techniques for estimating rice production in hilly and mountainous areas, in this study, we collected yield data at the field level, agro-meteorological data, and Sentinel-2/MSI remote sensing data in Chongqing, China, between 2020 and 2023. The integral values of vegetation indicators from the rice greening up to heading–filling stages were determined using the Newton–trapezoidal integration method. Using correlation analysis and importance analysis of permutation features, the effects of agro-meteorological variables and vegetation index integrals on rice yield were assessed. The chosen characteristics were then combined with three machine learning techniques—random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR)—to create six rice yield estimate models. The results showed that combined vegetation indices were more effective than indices used in separate development phases. Specifically, the correlation coefficients between the integral values of eight vegetation indices from rice greening up to heading–filling stages and rice yield were all above 0.65. By introducing agro-meteorological factors as new independent variables and combining them with vegetation indices as input parameters, the predictive capability of the model was evaluated. The results showed that the performance of PLSR remained stable, while the prediction accuracies of SVM and RF improved by 13% to 21.5%. After feature selection, the inversion performance of all three machine learning models improved, with the RF model coupled with variables selected during permutation feature importance analysis achieving the optimal inversion effect, which was characterized by a coefficient of determination of 0.85, a root mean square error of 529.1 kg/hm2, and a mean relative error of 5.63%. This study provides technical support for improving the accuracy of remote sensing-based crop yield estimation in hilly and mountainous regions, facilitating precise agricultural management and informing agrarian decision making.

Algal blooms are major issues and an ongoing cause of water quality problems in inland waters globally. In the case of harmful algal blooms, the water temperature rises after nitrogen and phosphorus inflow, which occurs in the summer, is the main cause of the algae bloom. In South Korea, algae monitoring methods have been performed by collecting water in point monitoring stations. Recently, in order to overcome the limitations of these existing monitoring methods, spatial monitoring methods using hyperspectral images and satellite images has been researched. We used satellite images for analysis of the spatial algal variation. The accuracy of algal identification is imperative for effective spatial monitoring of algal blooms in the context of ecological health and assessment. In this study, we generated algal big-data with simultaneously observed chlorophyll-a concentrations based on fluorescence measurement and predicted chlorophyll-a concentrations using 13- band satellite images derived from Sentinel-2. In order to validate the values from the satellite images, we compared them with simultaneously observed chlorophyll-a concentrations based on fluorescence measurement. The goal of this study is to improve the accuracy of predictions induced from satellite images. The analytical techniques were comparatively evaluated. The results showed that Artificial Neural Networks exhibited the best performance among them, improving more than 30% accuracy compared to that of multiple linear regression. Furthermore, the accuracy of identifying algal blooms has been shown to increase at high algal concentrations. In the end, it was successful to create algal bloom maps using a new algorithm to analyze algal bloom management.

The TSS research using GEE Cloud Technology in Sibolga Waters was carried out from February to April 2021, Mey to July 2021, August to October 2021, and October to December 2021. The analysis was carried out using the Sentinel-2 Satellite. TSS results showed that the highest amount was 60-120 mg/liter and the lowest was 0-60 mg/l. The content of TSS is spread evenly around the edge of the Sibolga coast to the Middle of Sibolga Waters and has passed the quality standard limit according to the Minister of Environment of the Republic of Indonesia which means the Sibolga Water area is polluted and improper for drinking water as well as for fish cultivation. The result has been seasonal influence can determine the direction of the TSS distribution pattern, both tidal factors and weather conditions such as rain and dry season. The side effect on the TSS distribution pattern in Sibolga Waters causes the TSS value at high tide to be higher than at low tide. Sentinel-2 TOA Reflectance Data imagery can be used to map the TSS distribution pattern in the Sibolga Waters area.

Estimating bathymetric information is vital for aquaculture and navigation applications. Free, high-resolution satellite imagery provides a cost-effective solution for routine bathymetric measurements. We tested six algorithms to retrieve water depth in the Mwanza Gulf of Lake Victoria using Sentinel-2 satellite imagery: the conventional Stumpf method, Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), Neural Network (NNET), and Support Vector Machine (SVM). In-situ depth points collected via echo sounders were used to train and validate the algorithms. Performance evaluation metrics included coefficient of determination (R2), mean absolute error (MAE), root-mean-square error (RMSE), and spatial autocorrelation of residuals. Among the algorithms tested, the Stumpf model exhibited moderate performance with an R2 of 0.441, higher MAE (2.078 m), and RMSE (2.964 m) values. The RF algorithm improved performance with an R2 of 0.957, lower MAE (0.476 m), and RMSE (0.823 m). The GBM and XGB algorithms achieved R2 values of 0.960 and 0.956, respectively, with low MAE (0.484 m for GBM, 0.482 m for XGB) and RMSE (0.795 m for GBM, 0.830 m for XGB) values. The NNET algorithm outperformed the GBM and XGB models, obtaining an R2 of 0.963, the lowest MAE (0.438 m), and RMSE (0.761 m). The SVM algorithm demonstrated the best performance with an R2 of 0.965, the lowest MAE (0.403 m), and RMSE (0.745 m), implying the highest accuracy in depth estimation. SVM also showed stable generalization across different locations with insignificant spatial autocorrelation of residuals. Therefore, SVM is recommended for repetitive bathymetry calculations. [ABSTRACT FROM AUTHOR]

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Accurate and timely rice crop mapping is important to address the challenges of food security, water management, disease transmission, and land use change. However, accurate rice crop mapping is difficult due to the presence of mixed pixels in small and fragmented rice fields as well as cloud cover. In this paper, a phenology-based method using Sentinel-2 time series images is presented to solve these problems. First, the improved rice phenology curve is extracted based on Normalized Difference Vegetation Index and Land Surface Water Index time series data of rice fields. Then, correlation was taken between rice phenology curve and time series data of each pixel. The correlation result of each pixel shows the similarity of its time series behavior with the proposed rice phenology curve. In the next step, the maximum correlation value and its occurrence time are used as the feature vectors of each pixel to classification. Since correlation measurement provides data with better separability than its input data, training the classifier can be done with fewer samples and the classification is more accurate. The implementation of the proposed correlation-based algorithm can be done in a parallel computing. All the processes were performed on the Google Earth Engine cloud platform on the time series images of the Sentinel 2. The implementations show the high accuracy of this method. [ABSTRACT FROM AUTHOR]

It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R2 of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands.

Spatial distributions of soil total nitrogen (STN) can greatly contribute to the precision fertilization and crop yield in black soil area. Many efforts have been devoted to the accurate algorithms for the estimation of STN contents. This study aims to firstly propose the applicable integrated machine learning algorithms (e.g., Random Forest (RF), Adaptive boosting (AdaBoost) and Gradient boosting categorical features (CatBoost)) and Supervised learning algorithms (e.g., Simple linear regression (SLR), Support vector regression (SVR) and Back propagation neural network (BPNN)). The spectral indexes and environmental variables were then integrated using Multispectral Imager (MSI) product, in order to seamlessly retrieve the spatial distributions of STN. A large number of soil samples were collected in Suihua City, and the synchronous reflectance that embedded in better quality of Sentinel-2 Level-2A images. Likewise, two scenarios were considered, e.g., band 1-12 reflectance or combining them with spectral indexes and environmental variables (digital elevation model, temperature, precipitation and soil types). The results showed that the average STN of in situ measured samples was 1 904.06 mg/kg, with a coefficient of variation of 17.93%. The coefficients of determination (R²) were smaller than 0.6 between the measured and derived values from the developed STN algorithms, when the band 1-12 reflectance as the input variables. The performances of six STN algorithms for the validated dataset were ranked in the descending order of RF, CatBoost, AdaBoost, BPNN, SLR and SVR, whereas, the importance were ranked in the order of RF, SVR, BPNN, AdaBoost, CatBoost, and SLR. Once the band 1- 12 reflectance, spectral indexes, and environmental variables were as the input variables, the performance of STN algorithms was improved significantly in the validated dataset, of which the R² increased by 0.22 and root mean square error (RMSE) decreased by 35.30 mg/kg. In total, the accuracies of STN algorithms were in the descending order of RF, CatBoost, AdaBoost, BPNN, MLSR, and SVR. Hence, the RF can be expected to simulate the nonlinear relationships between reflectance and STN, and then obtain a better degree of measured- and derived- fitting, indicating powerful nonlinear ability. Furthermore, the STN content was mapped using Sentinel-2 level2A imagery and RF algorithm, in order to examine the spatial variation. The spatial distribution of STN content was higher in the northeast, whereas lower in southwest-decreasing gradually from north to southand slightly higher in middle of Suihua City. This was attributed to the large number of environmental variables. Anyway, much more attention can be paid for the decision-making on the protection of ‘Black soil’ and natural ecosystems. The finding can provide the technical assistances on dynamically monitoring STN contents, in order to evaluate the soil fertility for the sustainable agricultural development in black soil area of Northeast China. [ABSTRACT FROM AUTHOR]

The multispectral image captured by the Sentinel-2 satellite contains 13 spectral bands with different resolutions, which may hider some of the subsequent applications. In this article, we design a novel method to super-resolve 20- and 60-m coarse bands of the S2 images to 10 m, achieving a complete dataset at the 10-m resolution. To tackle this inverse problem, we leverage the deep image prior expressed by the convolution neural network (CNN). Specifically, a plain ResNet architecture is adopted, and the 3-D separable convolution is utilized to better capture the spatial–spectral features. The loss function is tailored based on the degradation model, enforcing the network output obeying the degradation process. Meanwhile, a network parameter initialization strategy is designed to further mine the abundant fine information provided by existing 10-m bands. The network parameters are inferred solely from the observed S2 image in a self-supervised manner without involving any extra training data. Finally, the network outputs the super-resolution result. On the one hand, our method could utilize the high model capacity of CNNs and work without large amounts of training data required by many deep learning techniques. On the other hand, the degradation process is fully considered, and each module in our work is interpretable. Numerical results on synthetic and real data illustrate that our method could outperform compared state-of-the-art methods.

In the Sendai Bay Area of Japan's Miyagi Prefecture, a 10-year coastal forest recovery project began in 2011 following the Tohoku Earthquake earlier that year. We should pay attention to the status of the restoration in order to maintain regional resilience in the aftermath of the earthquake and tsunami as well as to promote the recovery of agriculture and daily life. Therefore, we evaluated the recovery of coastal forests in Miyagi Prefecture following the 2011 Tohoku Earthquake. We analyzed satellite images taken by the Sentinel-2 satellite for several years following the disaster. Using the Google Earth Engine, we searched for and loaded Sentinel-2 images taken from 2016 to 2020. We concluded that the vegetation activity in the coastal forests in Iwanuma City gradually recovered during the 5-year research period. Therefore, we assume that the coastal forests in Miyagi Prefecture's Iwanuma City have been recovering well following the 2011 Tohoku Earthquake. Nevertheless, some field surveys or other proper methods would be needed to confirm the computed results and to make a final conclusion, because the normalized difference vegetation index (NDVI) is a relative rather than absolute value. [ABSTRACT FROM AUTHOR]

Extensive floating macroalgae have drifted from the East China Sea to Japan's offshore area, and field observation cannot sufficiently grasp their extensive spatial and temporal changes. High-spatial-resolution satellite data, which contain multiple spectral bands, have advanced remote sensing analysis. Several indexes for recognizing vegetation in satellite images, namely, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and floating algae index (FAI), are useful for detecting floating macroalgae. Thresholds are defined to separate macroalgae-containing image pixels from other pixels, and adaptive thresholding increases the reliability of image segmentation. This study proposes adaptive thresholding using Sentinel-2 satellite data with a 10 m spatial resolution. We compare the abilities of Otsu's, exclusion, and standard deviation methods to define the floating macroalgae detection thresholds of NDVI, NDWI, and FAI images. This comparison determines the most advantageous method for the automatic detection of floating macroalgae. Finally, the spatial coverage of floating macroalgae and the reproducible combination needed for the automatic detection of floating macroalgae in Kagoshima, Japan, are examined. [ABSTRACT FROM AUTHOR]

The TSS research using GEE Cloud Technology in Belawan Waters was carried out from January to May 2021. The analysis was carried out using the Sentinel-2 Satellite. TSS results obtained that the amount is 0,011010879-53,74369064 mg/liter. The content of TSS is spread evenly around the outskirts of Belawan Waters to the Middle of Belawan Waters and has passed the quality standard limit according to the Minister of Environment of Republic Indonesia that means the Harbour area is polluted and improper for drinking water as well as for fish cultivation.

Background: Relying solely on radiata pine (Pinus radiata D.Don) leaves New Zealand's plantation forest industry vulnerable to fluctuations in market demand and at risk from a potentially devastating pest or disease outbreak. Therefore, the New Zealand government and forestry industry urge to diversify the forest resource and wood supply beyond the reliance on radiata pine. Unfortunately, the lack of accurate information on minor species' area, composition, and location poses challenges to forecasting potential log supply and logistics planning. Methods: The objective of this study is to classify minor species in New Zealand using imagery and phenological features extracted from data collected by the Copernicus Sentinel-2 satellite. The study collected reference data of minor species from large-scale forest owners and applied Random Forest classification using Sentinel-2 imagery to classify nine minor species classes in the Hawke's Bay region of New Zealand. Results: The study achieved an overall classification accuracy of 92.2% for minor species in New Zealand's Hawke's Bay region. Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and Eucalyptus species had the highest accuracies, exceeding 90% for both producer's and user's accuracies. Acacia, larch, and other mixed species had lower accuracies, likely due to their lower occurrence. The most important input variable for classification was the Digital Elevation Model, indicating the significance of elevation in differentiating plantation species. The Greenness Index (GI) and Red edge bands also proved useful in the classification. The phenological measure Mean-EVI2 was found useful in classifying deciduous species such as larch and poplar. Conclusions: To the best of our knowledge, this study is the first to map the spatial extent and distribution of minor plantation species in New Zealand at the regional level, providing promising results for potentially expanding the study to national-level species mapping. [ABSTRACT FROM AUTHOR]

The article presents a study of the automated detection specifics within forest-covered areas traversed by fires based on the different time satellite imagery from the Sentinel-2A and Sentinel-2B using the differential normalised burn ratio index (dNBR) for the pre-fire and post-fire periods the calculation. The studies carried out on the research topic are given and a review of the currently functioning forest fire monitoring systems has been implemented. The urgency of the development and testing of an automated system for assessing the forest fire consequences using open source software and Earth remote sensing data has been substantiated. It has been established that the differential index dNBR, calculated from the Sentinel-2A and Sentinel-2B satellite images captured on different dates makes it possible to effectively detect burned-out areas. It is shown that the Python ecosystem makes it possible to successfully create systems for automated processing of Earth remote sensing data. A prototype of a system for the automated detection of forest-covered areas traversed by fires has been developed, based on the materials of different dates satellite imagery from Sentinel-2A and Sentinel-2B spacecraft. The flowchart of the algorithm of processing Earth remote sensing data using the proposed system was presented. For the Sentinel-2 satellite images for the dates before and after the fire, the differential index dNBR was calculated, the analysis of the results of which showed a close correlation of the dNBR index with the degree of burnout of the territory. A schematic map of the areas affected by the fire has been drawn up and the accuracy of identifying burnedout areas has been assessed by calculating the confusion matrix. An assessment of the effectiveness of the automated system for identifying areas affected by forest fires, ways of its modernisation and improvement, as well as the prospects for implementation in production has been carried out. It is noted that the results of the created system have high reliability indicators. At the same time, the need was revealed to increase the sensitivity of the system when identifying areas that have undergone partial burnout. A variant of improving the algorithms used in the work by introducing the multilevel Otsu’s method, intended to significantly increase the sensitivity of the system, has been proposed.

Extensive floating macroalgae have drifted from the East China Sea to Japan’s offshore area, and field observation cannot sufficiently grasp their extensive spatial and temporal changes. High-spatial-resolution satellite data, which contain multiple spectral bands, have advanced remote sensing analysis. Several indexes for recognizing vegetation in satellite images, namely, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and floating algae index (FAI), are useful for detecting floating macroalgae. Thresholds are defined to separate macroalgae-containing image pixels from other pixels, and adaptive thresholding increases the reliability of image segmentation. This study proposes adaptive thresholding using Sentinel-2 satellite data with a 10 m spatial resolution. We compare the abilities of Otsu’s, exclusion, and standard deviation methods to define the floating macroalgae detection thresholds of NDVI, NDWI, and FAI images. This comparison determines the most advantageous method for the automatic detection of floating macroalgae. Finally, the spatial coverage of floating macroalgae and the reproducible combination needed for the automatic detection of floating macroalgae in Kagoshima, Japan, are examined.

Updated and accurate land cover maps are essential and crucial for sustainable crop production and efficient land management. However, accurate and efficient land cover mapping is still a challenge for agricultural regions with complicated landscapes. This study proposed a novel spectral-phenological based land cover classification (SPLC) method to identify the land cover for fragmented agricultural landscapes, with less requirement of ground truth data. The SPLC method integrated a pixel-based support vector machine (SVM) algorithm for cropland and various non-cropland classification, and a phenology-based automatic decision tree algorithm for identification of various crop types. It was then tested and applied in two typical case areas (i.e., Jiyuan in the upstream and Yonglian in the downstream) of Hetao Irrigation District (Hetao) in the upper Yellow River basin (YRB), northwest China. The field survey sampling data and the regional visual interpretation maps were jointly used to evaluate the accuracy of land cover classification. Results indicated that stable phenological rules can be established for crop identification even with complex planting patterns, and the SPLC method performed well in land cover mapping in case areas. Four high-accuracy land cover maps were produced for Jiyuan in 2020 and 2021, Yonglian in 2021, and Hetao in 2021. The overall accuracies (OA) can reach 0.90–0.94 based on evaluation with abundant ground truth data, and land cover maps agreed well with the visual interpretation maps in space. Overall, the case application validated the applicability and efficiency of the SPLC method in land cover mapping for regions with fragmented agricultural landscapes, and also implied the potential use in other similar regions. [ABSTRACT FROM AUTHOR]

The effects of clear and turbid water on the contribution of prefitons, which play an important role in aquatic nutrition and in the treatment of contaminated waters, are very important in a way that is more abundant in clear waters than opaque waters. In this paper, using Sentinel-2 measuring images and using spectral properties of opaque and clear water, and emphasizing statistical quantities, they are to be detected in the Sefidrud Dam during two seasons (March 27 , 2017) and summer (September 13, 2017). In this regard, after applying the required preprocesses (geometric and radiometer correction), by examining the spectral behavior curve of these two phenomena as well as the OIF index, optimal color combinations were detected by the test and error method. Accordingly, the most suitable color combination contains the largest amount of information for the spring, the color combination (a4,8,8) and for the summer, the color combination (8.1, a8) was determined. On the other hand, using the spectral-velocity study of opaque and clear water, within the range of 4/0 – 87/0 μm (bands 1 through a8), these two phenomena are well-differentiated from each other and other phenomena. Therefore, the NDVI index, which examines the standardized difference of the near-infrared spectral range (band 8) and visible red (band 4), was considered for revealing cloudy and transparent water, and eventually, by applying thresholds on it, Cloudy and clear water was distinguished from each other.

Dynamic change of Soil Salinity Content (SSC) is widely used to control soil salinization for high production efficiency in modern agriculture. This study aimed to enhance the correlation between SSC and spectral reflectance under vegetation coverage using Sentinel-2 multispectral remote sensing. Soil samples of salinity and moisture were collected at 100 sampling sites with different depth ranges, including <20, 20-40, and >40-60 cm, in Shahaoqu Irrigation Area, Inner Mongolia, China, from June to August in 2019. The multispectral data of Sentinel-2 satellite was acquired synchronously according to the sample time and location. The specific procedure was as follows. Firstly, a depth decision tree was constructed with the normalized difference vegetation index as a branching criterion, where the best one was then selected to determine the optimal depth range for the SSC retrieval of soil samples. Secondly, a classification decision tree was used to divide the soil samples into different categories, according to the normalized vegetation index and Soil Moisture Content (SMC). As such, the category of each soil sample was determined using the classification decision tree. Thirdly, the optimal spectral combination for each category was calculated to serve as an input variable into the SSC inversion model. Several machine learning models were adopted for the SSC inversion models to monitor the SSC at the optimal depth range from the salinity depth decision tree, including Adaptive boosting algorithm (Adaboost), Partial Least Squares Regression (PLSR), Support Vector Machines (SVM), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF). The results showed that the correlation coefficient between SSC and spectral reflectance was above 0.66, considering the decision tree. In terms of soil depth range, the optimal inversion depth for the SSC under vegetation cover was >40-60 cm, followed by <20 cm, but the SSC inversion model presented some limitations in the middle layer (20-40 cm). Furthermore, the inversion accuracy was ranked in the descending order of RF, GBDT, Adaboost, PLSR, and SVM, where the RF, GBDT, and Adaboost presented similar inversion. Correspondingly, the SSC inversion model using ensemble learning demonstrated a strong generalization ability to achieve the ideal and stable inversion under different application scenarios, compared with the other machine learning. Specifically, the SSC inversion model performed the best using RF, where the coefficient of determination, the root mean square error, the residual predictive interquartile range, and the residual predictive deviations were 0.77, 0.27%, 2.65, and 8.99, respectively. The correlation between SSC and spectral reflectance was 0.38 without considering the decision tree, indicating there was no significance in the SSC inversion model. Considering the decision tree, the correlation coefficient between soil salinity and spectral reflectance was above 0.66, and the coefficient of determination of the SSC inversion model was 0.72, indicating that the decision tree effectively enhanced the sensitivity of spectral reflectance to SSC for the high accuracy, particularly when the vegetation on the surface of the soil. Consequently, this finding can provide a promising potential way to monitor the soil salinization in the irrigated areas during crop growth using multi-spectral satellites in modern agriculture. [ABSTRACT FROM AUTHOR]

Soil salinity is a widespread environmental hazard and the main causes of land degradation and desertification, especially in arid and semi-arid regions. The first step in finding such a solution is providing accurate information about the severity and extent of the salinity spread in affected areas; this can be done by mapping the electrical conductivity (EC) of the soil. Utilizing the potential of high-resolution satellite imagery along with remote sensing techniques is a promising method to map salinity, as it allows for large-scale monitoring and provides high accuracy and efficiency. This paper, therefore, aims at assessing soil salinity by mapping the EC of soils, using satellite imagery from the newly launched Sentinel-2 satellite as well as Landsat-8 data. A field study was carried out using those data, and various salt features were extracted that relate the EC values of field samples to satellite-derived salt features. The study used two different regression approaches MLP and SVR. Additionally, two feature selection algorithms, GA and SFS, were implemented on the data to improve model performance. The study concludes that the proposed method for modeling salinity and the mapping of soil EC can be considered an effective approach for soil salinity monitoring.

Updated and accurate land cover maps are essential and crucial for sustainable crop production and efficient land management. However, accurate and efficient land cover mapping is still a challenge for agricultural regions with complicated landscapes. This study proposed a novel spectral-phenological based land cover classification (SPLC) method to identify the land cover for fragmented agricultural landscapes, with less requirement of ground truth data. The SPLC method integrated a pixel-based support vector machine (SVM) algorithm for cropland and various non-cropland classification, and a phenology-based automatic decision tree algorithm for identification of various crop types. It was then tested and applied in two typical case areas (i.e., Jiyuan in the upstream and Yonglian in the downstream) of Hetao Irrigation District (Hetao) in the upper Yellow River basin (YRB), northwest China. The field survey sampling data and the regional visual interpretation maps were jointly used to evaluate the accuracy of land cover classification. Results indicated that stable phenological rules can be established for crop identification even with complex planting patterns, and the SPLC method performed well in land cover mapping in case areas. Four high-accuracy land cover maps were produced for Jiyuan in 2020 and 2021, Yonglian in 2021, and Hetao in 2021. The overall accuracies (OA) can reach 0.90–0.94 based on evaluation with abundant ground truth data, and land cover maps agreed well with the visual interpretation maps in space. Overall, the case application validated the applicability and efficiency of the SPLC method in land cover mapping for regions with fragmented agricultural landscapes, and also implied the potential use in other similar regions.

Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and supervised classification methods. However, a high-performance classifier is desirable but challenging due to the existence of model hyperparameters. Conventional approaches generally rely on manual tuning, which is time-consuming and far from satisfying. Therefore, this work aims to propose a systematic method to automatically tune the hyperparameters by Bayesian parameter optimization for the random forest classifier. The recently launched Sentinel-2A/B satellites are drawn to provide the remote sensing imageries for land cover classification case study in Beijing, China, which have the best spectral/spatial resolutions among the freely available satellites. The improved random forest with Bayesian parameter optimization is compared against the support vector machine (SVM) and random forest (RF) with default hyperparameters by discriminating five land cover classes including building, tree, road, water, and crop field. Comparative experimental results show that the optimized RF classifier outperforms the conventional SVM and the RF with default hyperparameters in terms of accuracy, precision, and recall. The effects of band/feature number and the band usefulness are also assessed. It is envisaged that the improved classifier for Sentinel-2 satellite image processing can find a wide range of applications where high-resolution satellite imagery classification is applicable. [ABSTRACT FROM AUTHOR]

Large-scale land cover classification from satellite imagery is still a challenge due to the big volume of data to be processed, to persistent cloud-cover in cloud-prone areas as well as seasonal artefacts that affect spatial homogeneity. Sentinel-2 times series from Copernicus Earth Observation program offer a great potential for fine scale land cover mapping thanks to high spatial and temporal resolutions, with a decametric resolution and five-day repeat time. However, the selection of best available scenes, their download together with the requirements in terms of storage and computing resources pose restrictions for large-scale land cover mapping. The dataset presented in this paper corresponds to global cloud-free pixel based composite created from the Sentinel-2 data archive (Level L1C) available in Google Earth Engine for the period January 2017- December 2018. The methodology used for generating the image composite is described and the metadata associated with the 10 m resolution dataset is presented. The data with a total volume of 15 TB is stored on the Big Data platform of the Joint Research Centre. It can be downloaded per UTM grid zone, loaded into GIS clients and displayed easily thanks to pre-computed overviews.

Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management. This work investigates snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine-learning methods. To this end, the largest dataset for snow coverage mapping (to our best knowledge) with three typical classes (snow, cloud and background) is first collected and labeled via the semi-automatic classification plugin in QGIS. Then, both random forest-based conventional machine learning and U-Net-based deep learning are applied to the semantic segmentation challenge in this work. The effects of various input band combinations are also investigated so that the most suitable one can be identified. Experimental results show that (1) both conventional machine-learning and advanced deep-learning methods significantly outperform the existing rule-based Sen2Cor product for snow mapping; (2) U-Net generally outperforms the random forest since both spectral and spatial information is incorporated in U-Net via convolution operations; (3) the best spectral band combination for U-Net is B2, B11, B4 and B9. It is concluded that a U-Net-based deep-learning classifier with four informative spectral bands is suitable for snow coverage mapping.

Sentinel-2 satellite, launched by the European Space Agency, plays a critical role in various Earth observation missions. However, the spatial resolutions of Sentinel-2 images are different across its spectral bands, including four bands with a resolution of 10 m, six bands with a resolution of 20 m, and three bands with a resolution of 60 m. To facilitate the effectiveness of analyzing these images, super-resolving of the low-/medium-resolution bands to a higher resolution is desired. As in any image restoration inverse problems, we exploit image self-similarity, a commonly observed property in natural images, which underlies the state-of-the-art techniques, e.g., in image denoising. However, the design of self-similarity priors in nondiagonal inverse problems is challenging; often, a denoiser based on self-similarity is plugged into the iterations of an algorithm, without a guarantee of convergence in general. In this article, for the first time, we introduce a convex and scene-adapted regularizer built explicitly on a self-similarity graph directly learned from the Sentinel-2 images. We then develop a fast algorithm, termed Sentinel-2 super-resolution via scene-adapted self-similarity (SSSS). We experimentally show the superiority of SSSS over four commonly observed scenes, indicating the potential usage of our convex self-similarity regularization in other imaging inverse problems. [ABSTRACT FROM AUTHOR]

Knowledge of the number of building stories (NoS) is critical for understanding and regulating the urban development process. Existing approaches often transform building heights into numbers of stories using a specific empirical story-height coefficient, e.g., 3 meters for 1 story. However, the story heights of different buildings might differ for various reasons, such as different functional types within a city, differences in urban planning regulations among cities, or the regulations in different construction years. Based on a theoretical analysis and empirical statistics regarding the changes in vertical building information in seasonal composite images, we present a novel method for directly estimating the NoS in individual buildings from optical images. Specifically, four seasonal composite Sentinel-2 images taken within a year are utilized to estimate the NoS of each building with a modified object-detection network to make full use of vertical building information. The proposed method is called the Stories number EstimAtion from Seasonal composite images with an Object detection Network (SEASONet) method. Both theoretical analysis and empirical statistics are used to determine why seasonal composite optical images can effectively provide vertical building information. To validate the performance of the proposed method, we collect data from 61 Chinese cities with various building types, train the model with data from 47 cities (1365 998 buildings) and quantitatively test the model using data from the remaining 14 cities (246 584 buildings). In addition, M 3 Net using ZY-3 multiview images for the pixel-level estimation of building heights is adapted for comparison. The experimental results show that SEASONet achieves lower mean absolute error (MAE) and root mean square error (RMSE) values than M 3 Net over all 14 cities used for testing. Ablation experiments show that the four seasonal composite images are the keys for improving the estimation of the number of stories in high-rise buildings. A comparison with the results of two state-of-the-art methods that use empirical coefficients to convert building height to story number further confirms the superiority of the proposed method, especially its effectiveness in estimating the number of stories in high-rise buildings. • Geometric relationship among the Sun, building height and date of an image. • Empirical statistics on seasonal changes of building shadows in China. • Seasonal composite Sentinel-2 images for building story number estimation. [ABSTRACT FROM AUTHOR]

The spatial monitoring of plant diversity in the endangered species-rich grasslands of European mountain pastoral systems is an important step for fairer and more efficient Agri-Environmental policy schemes supporting conservation. This study assessed the underlying support for a spatially explicit monitoring of plant species richness at parcel level (policy making scale) in Southern European mountain grasslands, with statistical models informed by Sentinel-2 satellite and environmental factors. Twenty-four grassland parcels were surveyed for species richness in the Peneda-Gerês National Park, northern Portugal. Using a multi-model inference approach, three competing hypotheses guided by the species-scaling theoretical framework were established: species–area (P1), species–energy (P2) and species–spectral heterogeneity (P3), each representing a candidate spatial pathway to predict species richness. To evaluate the statistical support of each spatial pathway, generalized linear models were fitted and model selection based on Akaike information criterion (AIC) was conducted. Later, the performance of the most supported spatial pathway(s) was assessed using a leave-one-out cross validation. A model guided by the species–energy hypothesis (P2) was the most parsimonious spatial pathway to monitor plant species richness in mountain grassland parcels (P2, AICc = 137.6, ∆AIC = 0.0, wi = 0.97). Species–area and species–spectral heterogeneity pathways (P1 and P3) were less statistically supported (ΔAICc values in the range 5.7–10.0). The underlying support of the species–energy spatial pathway was based on Sentinel-2 satellite data, namely on the near-infrared (NIR) green ratio in the spring season (NIR/Greenspring) and on its ratio of change between spring and summer (NIR/Greenchange). Both predictor variables related negatively to species richness. Grassland parcels with lower values of near-infrared (NIR) green ratio and lower seasonal amplitude presented higher species richness records. The leave-one-out cross validation indicated a moderate performance of the species–energy spatial pathway in predicting species richness in the grassland parcels covered by the dataset (R2 = 0.44, RMSE = 4.3 species, MAE = 3.5 species). Overall, a species–energy framework based on Sentinel 2 data resulted in a promising spatial pathway for the monitoring of species richness in mountain grassland parcels and for informing decision making on Agri-Environmental policy schemes. The near-infrared (NIR) green ratio and its change in time seems a relevant variable to deliver predictions for plant species richness and further research should be conducted on that.

Timely and accurate estimation of the winter wheat planting area and its spatial distribution is essential for the implementation of crop growth monitoring and yield estimation, and hence for the development of national agricultural production and food security. In remotely sensed winter wheat mapping based on spectral similarity, the reference curve is obtained by averaging multiple standard curves, which limits mapping accuracy. We propose a spectral reconstruction method based on singular value decomposition (SR-SVD) for winter wheat mapping based on the unique growth characteristics of crops. Using Sentinel-2 A/B satellite data, we tested the SR-SVD method in Puyang County, and Shenzhou City, China. Performance was increased, with the optimal overall accuracy and the Kappa of Puyang County and Shenzhou City were 99.52% and 0.99, and 98.26% and 0.97, respectively. We selected the spectral angle mapper (SAM) and Euclidean Distance (ED) as the similarity measures. Compared to spectral similarity methods, the SR-SVD method significantly improves mapping accuracy, as it avoids excessive extraction, can identify more detailed information, and is advantageous in distinguishing non-winter wheat pixels. Three commonly used supervised classification methods, support vector machine (SVM), maximum likelihood (ML), and minimum distance (MD) were used for comparison. Results indicate that SR-SVD has the highest mapping accuracy and greatly reduces the number of misidentified pixels. Therefore, the SR-SVD method can achieve high-precision crop mapping and provide technical support for monitoring regional crop planting structure information.

Forest fires are one of the most affected natural disasters in our country. According to the forest statistics shared by the Ministry of Forestry and Water Management of Republic of Turkey, 3188 forest fires are occurred just in 2016 [1]. After each forest fire, detection of affected regions is crucial with regard to land management and fast planning. In this respect, remote sensing technologies have become a popular topic. In this work, detection of forest fire regions are investigated using multispectral images which are acquired by Sentinel-2A satellite. For the detection of forest fire regions, classical spectral indices are used. Additionally, a novel method with coarse-to-fine search strategy is proposed. Firstly, forest fire regions are coarsely detected, and detailed regions are detected efficiently using a fine search step. In order to evaluate spatial and spectral information together, a superpixel segmentation based approach is used for both coarse and fine search step. The experimental results of proposed method and other methods are obtained for the forest fire located in İzmir-Menderes region in 1 July 2017 [2]. Obtained results are compared using receiver operating characteristics and the proposed method is found to provide better detection performance than the other methods in the literature. [ABSTRACT FROM AUTHOR]

Soil salinity is a widespread environmental hazard and the main causes of land degradation and desertification, especially in arid and semi-arid regions. The first step in finding such a solution is providing accurate information about the severity and extent of the salinity spread in affected areas; this can be done by mapping the electrical conductivity (EC) of the soil. Utilizing the potential of high-resolution satellite imagery along with remote sensing techniques is a promising method to map salinity, as it allows for large-scale monitoring and provides high accuracy and efficiency. This paper, therefore, aims at assessing soil salinity by mapping the EC of soils, using satellite imagery from the newly launched Sentinel-2 satellite as well as Landsat-8 data. A field study was carried out using those data, and various salt features were extracted that relate the EC values of field samples to satellite-derived salt features. The study used two different regression approaches MLP and SVR. Additionally, two feature selection algorithms, GA and SFS, were implemented on the data to improve model performance. The study concludes that the proposed method for modeling salinity and the mapping of soil EC can be considered an effective approach for soil salinity monitoring. [ABSTRACT FROM AUTHOR]

Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and supervised classification methods. However, a high-performance classifier is desirable but challenging due to the existence of model hyperparameters. Conventional approaches generally rely on manual tuning, which is time-consuming and far from satisfying. Therefore, this work aims to propose a systematic method to automatically tune the hyperparameters by Bayesian parameter optimization for the random forest classifier. The recently launched Sentinel-2A/B satellites are drawn to provide the remote sensing imageries for land cover classification case study in Beijing, China, which have the best spectral/spatial resolutions among the freely available satellites. The improved random forest with Bayesian parameter optimization is compared against the support vector machine (SVM) and random forest (RF) with default hyperparameters by discriminating five land cover classes including building, tree, road, water, and crop field. Comparative experimental results show that the optimized RF classifier outperforms the conventional SVM and the RF with default hyperparameters in terms of accuracy, precision, and recall. The effects of band/feature number and the band usefulness are also assessed. It is envisaged that the improved classifier for Sentinel-2 satellite image processing can find a wide range of applications where high-resolution satellite imagery classification is applicable.

Establishing the remote sensing yield estimation model of wheat-maize rotation cultivated land can timely and accurately estimate the comprehensive grain yield. Taking the winter wheat-summer maize rotation cultivated land in Caoxian County, Shandong Province, as test object, using the Sentinel-2 images from 2018 to 2019, we compared the time-series feature classification based on QGIS platform and support vector machine algorithm to select the best method and extract sowing area of wheat-maize rotation cultivated land. Based on the correlation between wheat and maize vegetation index and the statistical yield, we screened the sensitive vegetation indices and their growth period, and obtained the vegetation index integral value of the sensitive spectral period by using the Newton-trapezoid integration method. We constructed the multiple linear regression and three machine learning (random forest, RF; neural network model, BP; support vector machine model, SVM) models based on the integral value combination to get the best and and optimized yield estimation model. The results showed that the accuracy rate of extracting wheat and maize sowing area based on time-series features using QGIS platform reached 94.6%, with the overall accuracy and Kappa coefficient were 5.9% and 0.12 higher than those of the support vector machine algorithm, respectively. The remote sensing yield estimation in sensitive spectral period was better than that in single growth period. The normalized differential vegetation index and ratio vegetation index integral group of wheat and enhanced vegetation index and structure intensify pigment vegetable index integral group of maize could more effectively aggregate spectral information. The optimal combination of vegetation index integral was difference, and the fitting accuracy of machine learning algorithm was higher than that of empirical statistical model. The optimal yield estimation model was the difference value group-random forest (DVG-RF) model of machine learning algorithm ( R 2 =0.843, root mean square error=2.822 kg·hm -2 ), with a yield estimation accuracy of 93.4%. We explored the use of QGIS platform to extract the sowing area, and carried out a systematical case study on grain yield estimation method of wheat-maize rotation cultivated land. The established multi-vegetation index integral combination model was effective and feasible, which could improve accuracy and efficiency of yield estimation.

Forest fires are one of the most affected natural disasters in our country. According to the forest statistics shared by the Ministry of Forestry and Water Management of Republic of Turkey, 3188 forest fires are °Ccurred just in 2016 [1]. After each forest fire, detection of affected regions is crucial with regard to land management and fast planning. In this respect, remote sensing technologies have become a popular topic. In this work, detection of forest fire regions are investigated using multispectral images which are acquired by Sentinel-2A satellite. For the detection of forest fire regions, classical spectral indices are used. Additionally, a novel method with coarse-to-fine search strategy is proposed. Firstly, forest fire regions are coarsely detected, and detailed regions are detected efficiently using a fine search step. In order to evaluate spatial and spectral information together, a superpixel segmentation based approach is used for both coarse and fine search step. The experimental results of proposed method and other methods are obtained for the forest fire located in İzmir-Menderes region in 1 July 2017 [2]. Obtained results are compared using receiver operating characteristics and the proposed method is found to provide better detection performance than the other methods in the literature. [ABSTRACT FROM AUTHOR]

Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management. This work investigates snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine-learning methods. To this end, the largest dataset for snow coverage mapping (to our best knowledge) with three typical classes (snow, cloud and background) is first collected and labeled via the semi-automatic classification plugin in QGIS. Then, both random forest-based conventional machine learning and U-Net-based deep learning are applied to the semantic segmentation challenge in this work. The effects of various input band combinations are also investigated so that the most suitable one can be identified. Experimental results show that (1) both conventional machine-learning and advanced deep-learning methods significantly outperform the existing rule-based Sen2Cor product for snow mapping; (2) U-Net generally outperforms the random forest since both spectral and spatial information is incorporated in U-Net via convolution operations; (3) the best spectral band combination for U-Net is B2, B11, B4 and B9. It is concluded that a U-Net-based deep-learning classifier with four informative spectral bands is suitable for snow coverage mapping. [ABSTRACT FROM AUTHOR]

The spatial monitoring of plant diversity in the endangered species-rich grasslands of European mountain pastoral systems is an important step for fairer and more efficient Agri-Environmental policy schemes supporting conservation. This study assessed the underlying support for a spatially explicit monitoring of plant species richness at parcel level (policy making scale) in Southern European mountain grasslands, with statistical models informed by Sentinel-2 satellite and environmental factors. Twenty-four grassland parcels were surveyed for species richness in the Peneda-Gerês National Park, northern Portugal. Using a multi-model inference approach, three competing hypotheses guided by the species-scaling theoretical framework were established: species–area (P1), species–energy (P2) and species–spectral heterogeneity (P3), each representing a candidate spatial pathway to predict species richness. To evaluate the statistical support of each spatial pathway, generalized linear models were fitted and model selection based on Akaike information criterion (AIC) was conducted. Later, the performance of the most supported spatial pathway(s) was assessed using a leave-one-out cross validation. A model guided by the species–energy hypothesis (P2) was the most parsimonious spatial pathway to monitor plant species richness in mountain grassland parcels (P2, AICc = 137.6, ∆AIC = 0.0, wi = 0.97). Species–area and species–spectral heterogeneity pathways (P1 and P3) were less statistically supported (ΔAICc values in the range 5.7–10.0). The underlying support of the species–energy spatial pathway was based on Sentinel-2 satellite data, namely on the near-infrared (NIR) green ratio in the spring season (NIR/Greenspring) and on its ratio of change between spring and summer (NIR/Greenchange). Both predictor variables related negatively to species richness. Grassland parcels with lower values of near-infrared (NIR) green ratio and lower seasonal amplitude presented higher species richness records. The leave-one-out cross validation indicated a moderate performance of the species–energy spatial pathway in predicting species richness in the grassland parcels covered by the dataset (R2 = 0.44, RMSE = 4.3 species, MAE = 3.5 species). Overall, a species–energy framework based on Sentinel 2 data resulted in a promising spatial pathway for the monitoring of species richness in mountain grassland parcels and for informing decision making on Agri-Environmental policy schemes. The near-infrared (NIR) green ratio and its change in time seems a relevant variable to deliver predictions for plant species richness and further research should be conducted on that. [ABSTRACT FROM AUTHOR]

Crescentic nearshore sandbars (CNSBs) that are observed in the shallow waters (< 10 m) of sandy beaches are important for understanding coastal dynamics because of their strong interaction with nearshore circulation. However, their formation, originating from shore-parallel straight nearshore sandbars (SNSBs), has rarely been observed in the field because their occurrence is typically short (less than a week). In this study, a process in which a nearly SNSB changed into a fully developed CNSB was observed using satellite images, field surveys, and video monitoring data at a sandy beach in South Korea. Freely available Sentinel-2 satellite images and the bathymetry data measured by echosounders were used to find out formation of an SNSB system after attack of Typhoon Tapah in September 2019 and that the SNSB changed to a CNSB in February 2020. The process was narrowed down using video monitoring data and hydrodynamic measurements, observing that two storm waves with maximum wave heights of >3 m developed in the site over a one-month period in January 2020 when the CNSB formed. The first storm wave had a sharp peak wave height that reached ~5 m and lasted ~2 days. The second storm wave had lower wave energy but several peak waves of ~4 m height and a total storm period of ~6 days. During both storm periods, the infragravity wave energy increased and strong (>0.5 m/s) offshore and longshore (northwest) currents developed for ~1 day and ~ 3 days for the first and second storm respectively. The results from video monitoring data show that a nearly developed SNSB system transformed into a weakly developed CNSB system after the first storm when alongshore variability on the nearly SNSB was intensified to become horns and bays of the weak CNSB. During the second storm, the CNSB system fully developed as the horns moved further onshore and the bays further offshore shaping the clear horn and bay pattern. This indicates that positive feedback between the flows and sediments played a key role for the formation of the CNSB and, therefore, the self-organization mechanism might be appropriate to describe the process during the two storm periods. Specifically, the high infragravity wave energy and strong quasi-steady currents were important because they could trigger the development of rip channels during the first storm whereas the channels were further strengthened by the long consistency of currents during the second storm. • Formation of CSNB under storm wave conditions was observed using satellite images, video monitoring and field experiment data. • Crescentic pattern was triggered by the first storm and the CNSB was fully formed during the second storm. • Development of quasi-steady flows during the two storms was a key factor in the CNSB formation through positive feedback between the flows and sediments, supporting the self-organization as a primary mechanism. • Long-term consistency of high waves during the second storm played a role in shaping the crescentic pattern by moving the sediments in the horns/bays further onshore/offshore. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of the winter wheat planting area and its spatial distribution is essential for the implementation of crop growth monitoring and yield estimation, and hence for the development of national agricultural production and food security. In remotely sensed winter wheat mapping based on spectral similarity, the reference curve is obtained by averaging multiple standard curves, which limits mapping accuracy. We propose a spectral reconstruction method based on singular value decomposition (SR-SVD) for winter wheat mapping based on the unique growth characteristics of crops. Using Sentinel-2 A/B satellite data, we tested the SR-SVD method in Puyang County, and Shenzhou City, China. Performance was increased, with the optimal overall accuracy and the Kappa of Puyang County and Shenzhou City were 99.52% and 0.99, and 98.26% and 0.97, respectively. We selected the spectral angle mapper (SAM) and Euclidean Distance (ED) as the similarity measures. Compared to spectral similarity methods, the SR-SVD method significantly improves mapping accuracy, as it avoids excessive extraction, can identify more detailed information, and is advantageous in distinguishing non-winter wheat pixels. Three commonly used supervised classification methods, support vector machine (SVM), maximum likelihood (ML), and minimum distance (MD) were used for comparison. Results indicate that SR-SVD has the highest mapping accuracy and greatly reduces the number of misidentified pixels. Therefore, the SR-SVD method can achieve high-precision crop mapping and provide technical support for monitoring regional crop planting structure information. [ABSTRACT FROM AUTHOR]

Modern cities are constantly growing; this fact provokes strong environmental pressure as pollution, health problems, stress, and other troubles which as a whole reduces the citizens' life quality. Some decades ago, the concept sustainable urban planning was created; the concept intends to generate friendly cities with a planned development. This research contributes to this task evaluating the potential that urban forests could have in the design of green corridors for Latin American intermediate cities (case study, Temuco, Chile). For the analysis and the generation of data, the Geographic Information System was applied. Also, the multispectral images were used with data derived from the Normalized Difference Vegetation Index (NDVI). The satellite used was the Sentinel-2, which gives red and infrared spectral information with a resolution of 10 × 10 m pixels providing vital information to analyze the quality of the vegetation. Methodologies applied were based on forestry ecosystem samples as well as on satellite technology. This helped to define the quality of the urban green areas allowing the connection to future green corridors. From these tools it was found that the green areas possess good quality vegetation, establishing the sanity, the form, the plant vigor, the stress, the chlorophyll activity and the vegetal cover. The results demonstrate that these combined methodologies of forestry ecology and geospatial tools, such as NDVI, are a good possibility to generate a continuous monitoring and follow up system of public areas, which in turn will allow a planning of those cities that contribute to a sustainable urban planning and with a better life quality for their population. [ABSTRACT FROM AUTHOR]

Ore processing is a source of soil heavy metal pollution. Vegetation traits (structural characteristics such as spatial cover and repartition; biochemical parameters—pigment and water contents, growth rate, phenological cycle...) and plant species identity are indirect and powerful indicators of residual contamination detection in soil. Multi-temporal multispectral satellite imagery, such as the Sentinel-2 time series, is an operational environment monitoring system widely used to access vegetation traits and ensure vegetation surveillance across large areas. For this purpose, methodology based on a multi-temporal fusion method at the feature level is applied to vegetation monitoring for several years from the closure and revegetation of an ore processing site. Features are defined by 26 spectral indices from the literature and seasonal and annual change detection maps are inferred. Three indices—CIred-edge (CIREDEDGE), IRECI (Inverted Red-Edge Chlorophyll Index) and PSRI (Plant Senescence Reflectance Index)—are particularly suitable for detecting changes spatially and temporally across the study area. The analysis is conducted separately for phyto-stabilized vegetation zones and natural vegetation zones. Global and specific changes are emphasized and explained by information provided by the site operator or meteorological conditions. [ABSTRACT FROM AUTHOR]