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

In the Middle Atlas rangelands, traditional methods for estimating standing crop biomass are labour-intensive and impractical. Remote sensing offers an initiative for standing crop biomass large-scale monitoring. The aim of this study was to estimate standing crop biomass, comprising annual and perennial forbs, grasses and perennial shrubs, using remote sensing data. The vegetation indices (NDVI, DVI, RVI, MSAVI and OSAVI) were derived from medium-resolution Landsat 8 and MODIS imagery. Sixty sampling sites were used for the biomass data collection. These sites were located across three grazing areas and data were collected in May and June 2016. Regression models were established between biomass field data and the five indices. Correlation analysis indicated that among the five vegetation indices, only DVI had the lowest value (r = 0.60). Linear models developed between the biomass field data and vegetation indices showed that NDVI, OSAVI and RVI explained a reasonable percentage of the variance in biomass. Values for R2 were 0.74, 0.77 and 0.71, respectively. Among these indices, the OSAVI performed better, with a high R2 and low error (MAPE = 11.03%). The established models represent a key tool for long-term monitoring of these rangelands. [ABSTRACT FROM AUTHOR]

The remote sensing community usually confuses the Spatial Resolution Distance (SRD) of satellite images with their Ground Sampling Distance (GSD). This misconception has been highlighted by assessments of Planet's CubeSat images conducted independently by NASA and ESA, which found that their SRD is about five times larger than their GSD. The discrepancies between different metrics to compute the SRD have contributed to the confusion between SRD and GSD. A recently developed spatial resolution metric computed in terms of the imaging sensor's Point Spread Function (PSF), named the Spatial Resolution Function (SRF), allows a comprehensive assessment of the SRD as a function of the resolving contrast generated in the image, when there are two-point sources in the scene. The SRF shows under what conditions the GSD is a good approximation to the SRD and provides a tool to assess the predictions of other metrics used to compute the SRD. We use the SRF to compute the SRD of images captured by Landsat 8 and Planet's SuperDove satellites. The PSF of these sensors is calculated by using the specifications and measurements provided by their operators. The SRD predictions of the SRF metric agree with NASA and ESA findings, showing that the average SRD of SuperDove and Landsat 8 images is 4.9 and 1.4 times larger than their GSD, respectively. The stability of the SRD of these images is assessed by computing the degradation (increase) of SRD resulting from an increase of atmospheric turbulence and spacecraft vibrations. We conclude that the confusion between SRD and GSD has led to the development of CubeSat images with a small GSD whose SRD is several times larger than their GSD. These CubeSat images have an intrinsically unstable SRD in the space environment, a property that explains the numerous radiometric inconsistencies reported by their users. [ABSTRACT FROM AUTHOR]

Using an optical band index generated from Landsat 8 OLI data, an inventory of Antarctic supraglacial lakes (size >0.01 km2) during 2014-2023 is presented. Shaded snow, cloud and rock shadow were removed by using a band-based filter. Over the north Antarctica, we identified 639 lakes covering an area of 520.79 km2, 1025 lakes covering 272.82 km2 over the East Antarctica, and 1045 lakes covering 93 km2 over south Antarctica. The influence of air temperature, 10-m zonal/meridional wind components and climate indices on surface melt were investigated. Over the north Antarctica, air temperature promoted melt, while Föhn and katabatic winds warmed adiabatically along the leeward side of the elevated features and initiated melt in south Antarctica. Nearly all areas have exhibited a decrease in melt over the past few years, and the number of melt cases dropped after 2020. Negative SAM index favoured melt over the north Antarctica. This study identified new SGLs, which will be a benchmark for further studies. The undulating terrain posed a difficulty for collecting and interpreting satellite data, which should be addressed using advanced technologies and algorithms. [ABSTRACT FROM AUTHOR]

Seasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to the great spatial and temporal heterogeneity of the snowpack and the inaccessibility of high mountainous areas, gapless satellite remote sensing presents an unprecedented opportunity to monitor snow cover effectively and affordably on a fine scale, from different aspects, and with regular revisit time. This study derives the snow seasonality metrics (first day of snowfall, last day of snow melt; and snow cover duration) over a large semi-arid region in Morocco's Atlas Mountains. We calculate these metrics by combining over 10,000 images from Landsat 8 and Sentinel 2 satellites for four hydrological years (2016-2021) to create a harmonized product with a time interval of about 3 days using the Google Earth Engine (GEE) platform. This dense and large time series facilitates a gap-filling method to minimize and overcome the effect of cloud cover, and its assessment shows a positive correlation between the masked pixels and the interpolated ones. These methods allowed us to realize a map of the snow cover area and extract a homogeneous Normalized Difference Snow Index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence. The results showed that derived snow cover metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be noted. Overall, the snow duration ranges between November and April depending on the characteristics of each hydrological year. The retrieved Landsat 8 and Sentinel 2 snow dates had a high level of agreement with in-situ data observations with almost a day-and-a-half delay with an overall accuracy equal to 0.96. The analysis of snow cover dynamics via GEE has offered the ability to calculate the first day of snowfall, last day of snowmelt, and snow cover duration annually at a pixel level, providing the user with the ability to track the seasonal and interannual variability in the timing of snowmelt toward a better understanding of how the hydrological cycles of higher latitude and mountainous regions are responding to climate change. [ABSTRACT FROM AUTHOR]

The amount of water taken from non-renewable resources such as aquifers to fulfill irrigation requirements is rarely monitored, putting sustainable agriculture under threat in the face of changing climate. In the present research, an attempt was made to apply multi-sensor (Landsat suite, GRACE, GRACE-FO) satellite data to monitor spatiotemporal evolution of agriculture for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). For this purpose, time series of NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index), and MSAVI2 (Modified Soil-Adjusted Vegetation 2) was utilized to assess vegetation pattern change in the study area. The present investigation used High-resolution Planetscope (PS) nanosatellite data to validate the vegetation results. Mann Kendall trend analysis and linear regression were performed to study the temporal pattern, and the relationship between vegetation, GRACE, and climate variables was performed from 1984 to 2020. Water extraction based on the averaged value of JPL GWS and CSR GWS showed a decreasing trend of −10.24 ± 1.4 mm/year from 2003-2020. The annual rainfall showed a decreasing trend, while the annual temperature showed an increasing trend from 1982-2020. The correlation of vegetation indices with rainfall of one-month lag showed a significantly better relationship of 0.74, 0.74, and 0.75, respectively, for NDVI, SAVI, and MSAVI2. The correlation between temperature and all three vegetation indices is a strong negative correlation: −0.85 for NDVI and −0.9 for SAVI and MSAVI. [ABSTRACT FROM AUTHOR]

Nine remote sensing-based surface soil moisture (SSM) estimation models using images from Landsat 8, Sentinel-2 and Sentinel-1 satellites were compared. To evaluate these models, we measured SSM at 179 locations in a 50-ha sunflower field. The result showed that the Water Cloud-based model, a semi-empirical regression model, which used the synergy of Landsat 8 and Sentinel-1 data, was the best model, with an R2 of 0.73 and RMSE of 0.053 m3/m3. In sum, with the integration of images from multiple satellites, soil moisture maps with suitable spatial resolutions were retrieved that may be used for irrigation planning. [ABSTRACT FROM AUTHOR]

Land surface emissivity (LSE) represents an essential parameter for understanding land change and surface energy balance. While numerous algorithms have been proposed to retrieve LSE, they are seldom applied to remote sensing data with high spatial resolution. The SDGSAT-1 satellite provides an opportunity to retrieve LSE with high spatial resolution that can be regarded as references for validating LSE products generated from coarse-spatial-resolution satellite data. However, no study has yet explored LSE-retrieval based on SDGSAT-1. This research seeks to fill this gap through evaluating the radiometric calibration accuracy of SDGSAT-1, using five methods to retrieve pixellevel emissivity of Guangzhou, and comparing such results with emissivity generated from other satellites (e.g. Landsat 8, ECOSTRESS, and MODIS) that transited Guangzhou during the similar period. Our results indicated that SDGSAT-1 performed well in radiometric calibration. Heterogeneous vegetation and man-made surfaces registered significant emissivity differences in varying spatial resolutions, whereas relatively homogeneous water bodies registered smaller differences. Decreasing spatial resolution amplifies the emissivity differences of the same land cover among distinctive sensors. The five emissivity-retrieval methods performed exceptionally well based on SDGSAT-1, registering average absolute differences less than 0.026 as compared with Landsat 8; with decreasing spatial resolutions, the maximum difference between SDGSAT-1 and MODIS LSE product reached 0.124. [ABSTRACT FROM AUTHOR]

Sub-Saharan Africa faces a number of essential issues, including water quality. As such, evaluating the surface water quality of lakes and reservoirs is a crucial part of environmental monitoring and management. Especially in a region where these water bodies serve as a source of livelihood for communities living around them. Water quality parameters (WQPs) are usually taken from the site and sent to the laboratory for measurement and analysis. However, this traditional method is time-consuming, costly, and labor-intensive. Combining geographic information system and remote sensing (RS) allows researchers to analyze WQPs more conveniently. This study, therefore, used RS technology to map and estimate WQPs and correlated it with in-situ measurement. Using the empirical regression model and Landsat 8, WQPs such as chlorophyll-a (Chl-a), total suspended solids (TSS) and turbidity were estimated. The results from RS were correlated with the in-situ measurements of water quality. The results showed that the in-situ Chl-a levels varied from 0.206 to 13.5 mg/L, averaging 5.1 mg/L. The Chl-a values estimated from Landsat 8 had R2 of 0.883 and 0.853, respectively, for both periods (17 December 2022 and 16 March 2023). The green band (B3) was more instrumental in detecting Chl-a. The in-situ measurement for TSS ranged between 18 and 48 mg/L, with a mean value of 28.7 mg/L. These readings were low and within tolerable bounds of 50 mg/L. High TSS concentrations were found near farms and communities with a significant influx of silt into the surrounding lake. The comparison of in-situ water quality and the reflectance from satellite data showed that the turbidity estimated from the sensor from the two periods has R2 > 0.65. The study showed that the combination of the Landsat image and in-situ measurement offers great ways to provide timely and affordable estimation from WQPs. [ABSTRACT FROM AUTHOR]

Spatiotemporal variations of surface urban heat island (SUHI) and canopy urban heat island (CUHI) are compared in this study for the arid city of Isfahan, Iran, using two datasets for land surface temperature (LST) and air temperature (AT), and three different indices: urban-rural LST difference, urban thermal field variance index (UTFVI) and urban-rural AT difference. The inverted SUHI intensity was shown to vary between −13.9 °C in summer and −1.5 °C in winter, while the hourly average CUHI intensity ranged between −4.4 °C in summer and 6.3 °C in autumn. The spatial variation of CUHI showed significant differences in UHI intensity between urban sites (p < 0.05). Based on UTFVI variation, the non-SUHI and strongest SUHI were dominant phenomena in urban sites. In contrast, SUHI intensities were negative, representing only the non-SUHI phenomenon. The correlation between SUHI and CUHI values was not significant, indicating that intensities of CUHI and corresponding SUHI are not comparable in arid climates. These results demonstrate that for arid climates, determining UHI based on UTFVI, and the urban-rural AT difference, can provide detailed information about spatiotemporal variations of UHI. [ABSTRACT FROM AUTHOR]

Mapping river water quality at a basin scale with satellite image data represents a tremendous technical challenge, since traditional empirical remote sensing models calibrated for specific reaches of a river cannot be applied to all the rivers and streams in the basin with an adequate prediction accuracy. In this study, we developed a multi-predictor ensemble model to map and assess water turbidity for all rivers and streams in the Tombigbee River Basin with Landsat 8 multispectral images. Coincident with Landsat 8 satellite overpass on 2 May 2019, we conducted boat-based water quality data collection in the confluence area of Tombigbee River and Black Warrior River near the city of Demopolis, Alabama. Based on the coincident satellite image and field water samples, we calibrated and evaluated the multi-predictor ensemble model, which consists four component empirical models. Our evaluations suggest that the multi-predictor ensemble model can improve turbidity estimation accuracy by 79% in comparison with the best traditional empirical model. More importantly, our multi-predictor ensemble model can be applied to other river and stream reaches across three major sub-basins with a high prediction accuracy. With the multi-predictor ensemble model, we derived a turbidity map for all rivers and streams with a width greater than 90 m in the basin, by processing three Landsat 8 image scenes. This basin-wide turbidity map indicates that the turbidity of rivers and streams exhibits longitudinal gradients and distinct spatial trends within the basin, which were related to the basin land cover/land use, hydrometeorological condition, and the spatial distribution of dams. Owing to its strong spatial transferability and high prediction accuracy, the ensemble model, in conjunction with synoptic multispectral satellite image data, is recommended as an effective and efficient tool for mapping and monitoring river and stream water quality at a large basin or regional scale. [ABSTRACT FROM AUTHOR]

Surface reflectance (SR) is one of the most important components for deriving different biophysical parameters from remotely sensed data. For accurate retrieval of SR, it is essential to accurately estimate the degree of atmospheric alteration to the target signals reaching at-sensor. Simplified and robust SR estimation method (SREM) is one such approximate estimation method that could demonstrate the retrieval of SR from top-of-atmosphere (ToA) signals, without using any precalculated LUTs or aerosol information. The present study attempts to assess the accuracy of the SREM algorithm over varied Indian land surfaces using data from Landsat 8, Sentinel 2, AWiFS and LISS III. The results indicate that while SREM performs quite well for Sentinel 2 over most land surface types, the performance is good for Landsat 8 as well except over water and in coastal aerosol band. SREM retrievals also show high correlation for AWiFS and LISS III but the estimated values are much lower than level 2 SR due to smaller ToA reflectance values. Furthermore, while aerosol load does impact the performance adversely, it is least impactful for Sentinel 2 and LISS III datasets and most over Landsat 8, particularly over water. Additionally, while AWiFS and LISS III SR retrievals are underestimated at all spectral bands, retrieval is slightly underestimated from green to SWIR band in case of Sentinel 2 and SWIR band in case of Landsat 8. Overall, the findings demonstrate that while SREM will need to be modified to adapt to LISS III and AWiFS retrievals, it can, nevertheless, be readily used as a simplified method for quick and approximate estimation of SR over the Indian region using data from Landsat 8 and Sentinel 2. [ABSTRACT FROM AUTHOR]

Accurate estimation of crop parameters, such as Leaf Area Index (LAI) and biomass over large areas using remote sensing techniques, is crucial for monitoring crop growth and yield prediction. In this study, a Gaussian Process Regression (GPR) method was developed to estimate LAI and biomass values of sugarcane during growth season using optical and synthetic-Aperture Radar (SAR) data fusion. Predicting LAI on an independent test data set using the GPR and the combined optical and SAR indices provided better prediction accuracies of LAI; with the GPR based on radial basis function (Root Mean Square Error [RMSE] = 0.34, Mean Absolute Error [MAE] = 0.28 and Mean Absolute Percentage Error [MAPE] = 10.5%) and polynomial function (RMSE = 0.42, MAE = 0.31 and MAPE = 12.58%), respectively. The test results of sugarcane biomass also showed that the GPR (poly) produced the highest statistical results (RMSE = 2.45 kg/m2, MAE = 1.72 kg/m2, MAPE = 8.1%) using the combined indices. The results suggest that the crop biophysical retrieval based on optical and SAR data fusion and GPR proposed in this study could improve LAI and biomass estimation that could help for effective crop growth monitoring and mapping applications. [ABSTRACT FROM AUTHOR]

Periodic observations of vegetation index, such as the normalized difference vegetation index (NDVI), can be used for data assimilation in heterogenous ecosystems. Indeed, the new Sentinel 2 Multispectral instrument and Landsat 8 Operational Land Imager sensor data are available at such high temporal and spatial resolutions that can be used to detect the patches of the main vegetation components (grass and trees) of heterogenous ecosystems, and capture their dynamics. We demonstrate the possibility to merge grass and tree NDVI observations and models, to optimally provide robust predictions of grass and tree leaf area index. The proposed assimilation approach assimilates NDVI data through the Ensemble Kalman filter (EnKF) and dynamically calibrates a key vegetation dynamic model parameter, the maintenance respiration coefficient (ma). In the presence of large bias of the grass and tree ma base values, only the use of the proposed assimilation approach removes the large bias in the biomass balance, dynamically calibrating maintenance respiration coefficients, and corrects the model. The performance of a land surface – vegetation model was improved by assimilating observations of NDVI. The effective impact of the proposed assimilation approach on the evapotranspiration and CO2 uptake predictions in the heterogenous ecosystem is also demonstrated. [ABSTRACT FROM AUTHOR]

Blue colour-coated steel roofs (BCCSRs) offer a lightweight and economical option to concrete and other cladding in buildings, but they are also controversial for altering the surface energy budget and water cycle. Obtaining spatial information about BCCSRs is crucial for exploring the environmental impacts of man-made landscapes. However, existing methods are not always effective due to the variety of BCCSR types and background conditions. To overcome these limitations, we proposed a new index (called BCCSI) based on Sentinel-2 multispectral images to map the commonly used BCCSRs. Five typical study areas were chosen worldwide to develop and validate the BCCSI. Based on spectral analysis, we constructed the BCCSI using the blue, red, green, and shortwave infrared 2 (SWIR2) bands to highlight the BCCSR while suppressing the background condition. Compared with five existing indices, the BCCSI was effective in the visual evaluation, separability analysis and BCCSR mapping. Moreover, the BCCSI achieved similar accuracy to the supervised classifier while avoiding the time-consuming and laborious effort of sample collection. Furthermore, the BCCSI showed its applicability in medium-resolution satellite data, such as Landsat-8 imagery. Thus, the proposed BCCSI provides a viable scheme for global BCCSR mapping and analysis. [ABSTRACT FROM AUTHOR]

High-resolution aerosol products offer advantages over lower spatial-resolution products for monitoring pollution in urban areas and industrial zones. Therefore, in this study we develop a Landsat 8 algorithm to retrieve aerosol optical depth (AOD) based on the MODIS Dark Target (DT) algorithm. This algorithm uses the direct surface reflectance of Landsat 8 to build a surface reflectance relationship between the visible (BLUE, RED) and short-wave infrared bands (SWIR2). AOD data from AERONET stations in the study area and AOD data from MODIS 3 km were used for validation of the estimated Landsat 8 AOD. The estimated AOD was highly correlated at most AERONET AOD, with R 2 =0.98, (RMSE = 0.3), except Bac Lieu, but only had a moderate correlation with MODIS AOD which was attributed to the difference in data retrieval time and associated changes in climate conditions. This is the first high-resolution AOD retrieval in Vietnam, leading to more practical applications for air quality monitoring. [ABSTRACT FROM AUTHOR]

Fall armyworm (FAW) Spodoptera frugiperda (J.E. Smith), damage was monitored at a regional scale using time series data in Western and Southern African countries. The study employed the normalized difference vegetation index (NDVI) computed from Landsat 8 imagery using the Google Earth Engine (GEE) using image composites for the years 2013 to 2020 for the study areas. The index was then reclassified based on the NDVI threshold values into low, sparse, moderate, and dense classes. FAW prevalence data were then utilized to validate the correlation between the FAW infestation and NDVI values. FAW was associated with a decrease in vegetation productivity between the years 2016, 2017, and 2018 when the pest infestation was reported in the study areas. The validation results showed that there is a correlation between FAW infestation and NDVI (R20.83). Our study highlighted that NDVI can be used as a proxy to quantify pest damage to vegetation productivity. [ABSTRACT FROM AUTHOR]

This study aims to assess the performance of the Sentinel-2 and Landsat 8 sensors to map forest fires. We choose two fire events, the Haddad fire and the Sidi Ferdjani fire, in northwestern Tunisia in 2017. Several spectral indices were derived from each sensor and the performance of each spectral index was assessed. A validation exercise was undertaken for each fire to compare the spatial matching between the burned area retrieved from each spectral index and its homologue obtained from the Emergency Management Service (EMS). Our results indicate that ΔNBR and its relativized version RBR derived from both sensors exhibit the highest discrimination power (M-statistic values >2.5). The Sentinel sensor is slightly more efficient than the Landsat 8 in mapping burned scars, but both sensors produce acceptable results. We conclude that both sensors could be a good alternative to EFFIS data, particularly when there is a need to detect details inside the burned areas. [ABSTRACT FROM AUTHOR]

High concentrations of suspended solids can cause water quality deterioration, leading to ecological degradation of the aquatic environment. In highly eutrophic waters, they are important carriers of pollutants and nutrients. Therefore, quantifying total suspended solids concentration ( C T S S ) is critical for any water quality monitoring program. This study proposes two band ratio algorithms for estimating C T S S in highly eutrophic lakes based on a data set of 180 in situ samples of remote-sensing reflectance, R r s λ , and C T S S measured simultaneously across 10 dates in four lakes and reservoirs in Hanoi (Vietnam). The in situ C T S S ranges from 2.7 to 73.3 mg l−1, averaging at 24.9 mg l−1, and is highly correlated to water transparency (the correlation coefficient, r = 0.73), chl-a concentration ( r = 0.78), and the trophic state index ( r = 0.89). Our analyses demonstrate the appropriateness of the near-infrared/red spectral ratio, R r s 815 / R r s 655 , model (N = 124) for estimating C T S S in the study lakes (the determination coefficient, R 2 = 0.84). The model was then verified with a small error (the root mean square error, RMSE = 4.1 mg l−1) using independent multi-date-obtained data sets (N = 56). The model is further optimized to establish a regional C T S S estimation model for Landsat 8/OLI and Sentinel 2/MSI bands using the 865 and 655 (or 665) nm bands. The band ratios showed reasonable estimations when applying to the respective spectral band ratios, i.e. the OLI's band 5/band 4 ( R 2 = 0.58; RMSE = 7.7) and the MSI band 8/band 4 ( R 2 = 0.73; RMSE = 6.1). [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for estimating wheat yields based on estimated abundances of endmembers attributed to photosynthetic pigments, using Landsat 8 images acquired during maximum greenness. Endmembers within the wheat pixels are found using an unmixing algorithm then they are further optimized to maximize the predictive power of the abundances for the yields. Similarity of the endmembers to photosynthetic pigment's spectral signatures and their predictive power suggests their relevance to the pigments. Although the initial unmixing of the intimate mixture of photosynthetic pigments is linear, interactions of abundances are used in the optimization to handle the non-linearity using bilinear model. Wheat yields are estimated with abundances, their relevant interactions, agrometeorological parameters and vegetation indices using three machine learning algorithms. Harvester records from 142 fields are used as ground truth for performance assessment. The yields are estimated with 82% accuracy (RMSE = 22.51) when Random Forest algorithm is used with important parameters. [ABSTRACT FROM AUTHOR]

Wetland, an important carbon pool on the earth, is of great significance for human beings and the environment. In this study, we modeled the wetland NPP using Carnegie–Ames–Stanford Approach (CASA) and time series with high spatial and temporal resolution generated by Landsat 8 and Sentinel-2 images. Firstly, the downscaled Landsat 8 data (10 m) combined with Sentinel-2 data were utilized to produce time series with high spatial and temporal resolution. Subsequently, all Sentinel-1 and Sentinel-2 data within each stage (five stages.) are employed to obtain monthly wetland maps in these stages by an adaptive Stacking algorithm, respectively. Then, monthly fraction cover maps (mainly sedge, reed and poplar) were derived from the time series reflectance product using fully constrained least squares (FCLS) in these five Stages, respectively. Finally, monthly normalized difference vegetation index (NDVI), land surface water index (LSWI), temperature, solar radiation, as well as wetland maps and were combined to estimate monthly and total NPP in the Dongting Lake wetland by CASA model. The high correlation (R2 = 0.8445) and low (RMSE = 20.30 g C/m2) between the estimated NPP using proposed method and measured NPP demonstrated a significant linear relationship and the estimated NPP based on Sentinel-2 data using the CASA model with the above-described input parameters is creditable. The NPP estimation method in this paper is expected to provide scientific data support for quantitative research of regional wetland carbon reserves and sustainable development. [ABSTRACT FROM AUTHOR]

The Dunhuang radiometric calibration site is an important component of the China Radiometric Calibration Sites (CRCS), which has served for in-flight radiometric calibration of Chinese satellites sensors since the 1990s, whereby the homogeneity has been verified since its construction. However, due to the increasing demand for radiometric calibration of the in-flight satellite sensors, and the impacts of a changing natural environment and the destruction of human activities of the site, it is essential to reassess the homogeneity of Dunhuang site and redefine the suitable region for calibration. Accordingly, the homogeneity of the Dunhuang site is reevaluated based on the satellite images of OLI/Landsat 8 (with resolutions of 30 m) and MSI/Sentinel 2 (with resolutions of 10 m) observed from 2015 to 2021. An ROI of 50 × 75 km is selected covering three historical definitions of the Dunhuang site with an area of 25 × 25 km, 20 × 20 km, and 10 × 10 km, respectively. Firstly, the spatial-temporal coefficients of variation ( C V S T ) of the ROI is calculated. The homogeneity of the three existing sites is analyzed, and the results show that the 20 km site (40.06° N−40.25° N, 94.25° E−94.42° E) has the best homogeneity among the existing sites in Dunhuang. Then, by using a hierarchical region statistical method, three optimal locations and scopes of the Dunhuang site (10 × 10 km: 40.044° N−40.144° N, 94.284° E−94.384° E; 20 × 20 km: 40.002° N−40. 202° N, 94.216° E−94.416° E; 25 × 25 km: 40.021° N−40.247° N, 94.071° E−94.364° E) are defined. The average C V S T of the three OGT sites decrease from about 0.94%～2% to 0.7%～1.54% relative to the existing sites, and the standard deviation of C V S T decrease from about 0.45%～2% to 0.24%～1%. The Getis statistics also indicate that they are more homogeneous than the existing definitions of the site. Additionally, the optimal Dunhuang sites are compared with the north Africa Pseudo-Invariant Calibration Sites (PICS). The homogeneity of optimal Dunhuang sites is proved to be better than most sites hence that could be the complement for each other. These three optimal Dunhuang sites are new candidate sites served for the satellite in-flight radiometric calibration. [ABSTRACT FROM AUTHOR]

Crop Growth Models (CGM) have been widely used in estimating crop yield at a local scale while Remote Sensing (RS) data has the advantage of retrieving crop parameters such as leaf Area Index (LAI) at a range of spatial scales. Data Assimilation (DA) is highly useful tool that integrates CGM and RS data derived from satellite imageries to improve the simulated crop state variables and consequently model outputs such as crop total biomass and yield. In this study, we assimilated LAI with the WOrld FOod STudies (WOFOST) model to estimate sugarcane yield using an Ensemble Kalman Filter (EnKF) algorithm. The LAI was retrieved from Landsat 8 (L8) optical and Sentinel 1A (S1A) Synthetic Aperture Radar (SAR) imageries using a Gaussian Process regression (GPR) method. The Deterministic Modeling (DM), independent assimilations of LAI retrieved from L8 and S1A, and assimilation of LAI retrieved from a combined SAR-optical data were tested and validated using field observation data in the Wonji-Shoa sugar plantation, Ethiopia. The results demonstrate that the accuracy of sugarcane yield estimated by the WOFOST model was significantly improved after DA using combined L8 and S1A data. Compared to the DM estimation, the root mean square error (RMSE) was decreased by 2.13 t/ha for the independent assimilations of LAI retrieved from L8, 3.96 t/ha for the independent assimilations of LAI retrieved from S1A and 5.94 t/ha for combined assimilation of L8 and S1A LAI. A coefficients of determination (R2) of 0.36, 0.48, 0.53, and 0.69 and Normalized Root mean square error (NRMSE) of 14.72%, 11.67%, 10.55%, and 8.44% were obtained for DM, L8 LAI assimilation alone, S1A LAI assimilation alone and combined L8 and S1A LAI assimilation, respectively. The results show that combined L8 and S1A LAI DA has better performance because SAR and optical data have complementary effects. Hence, the assimilation of LAI from combined L8 and S1A data into the WOFOST model provides a robust technique to improve crop yield estimations. [ABSTRACT FROM AUTHOR]

Evapotranspiration is one of the most important elements of the hydrological cycle. Estimation of evapotranspiration is imperative for effective forest, irrigation, rangeland and water resources management as well as to increase yields and for better crop management. This study aims to evaluate the effectiveness of the Surface Energy Balance Algorithm for Land (SEBAL) in estimating evapotranspiration and crop coefficient of corn in the Mediterranean region of Adana province, Turkey. The Landsat 8 satellite images from March to September 2018 were used to acquire the coefficients of the respective bands. Then, the net radiation flux on the earth's surface and the earth's heat flux is obtained using incoming-outgoing radiation fluxes from albedo, surface emissivity coefficients, land surface temperature, and plant indicators. Next, the sensible heat flux is calculated by determining the hot and cold pixels under consideration via the atmospheric stability conditions. Finally, evapotranspiration maps are plotted. The crop coefficient of corn is also estimated with the respected maps being plotted. To validate the outcomes from the SEBAL algorithm, experimental methods were employed to calculate the evapotranspiration values and evaluated using suitable performance metrics. The results showed that the SEBAL generated evapotranspiration values are in high agreement with the FAO Penman-Monteith method registering the highest correlation (R = 0.91) and the lowest error (RMSE = 1.14). In addition, the SEBAL method registered the highest correlation values of 0.89, 0.87 and 0.68 with Turk, Makkink and Hargreaves experimental methods, respectively. Moreover, the crop coefficients estimated using SEBAL also manifested an acceptable correlation with all methods. The highest correlation value registered was with the FAO Penman-Monteith method (R = 0.98). The outcomes show that since the performance of the SEBAL algorithm in estimating the actual evapotranspiration and crop coefficient using Landsat 8 satellite images is acceptable, the SEBAL algorithm could be a very convenient method. Moreover, it could easily be assimilated into farming management systems and precision agriculture for better decision-making and higher yield. [ABSTRACT FROM AUTHOR]

Sentinel-1 and Landsat-8 data were used to retrieve soil moisture from top soil surface (0–5 cm depth) at agricultural land (area under wheat crop). After pre-processing of satellite data and removal of vegetation influence (σ°veg) using Water Cloud Model (WCM), total backscattering coefficient (σ°total) and Normalized Difference Vegetation Index (NDVI) were used to simulate backscattering from soil (σ°soil). Modified Dubois Model (MDM) and Topp's Model were used to retrieve soil moisture using ε. Further, modelled soil moisture was evaluated using in situ soil moisture measurements and a Time Domain Reflectometer during Sentinel-1 overpass (24 January, 25 February and 13 March 2018). Statistical tests showed that an integrated approach has potential to improve soil moisture estimates over the vegetated/cropped area for agricultural and hydrological studies. [ABSTRACT FROM AUTHOR]

Ice velocity constitutes a key parameter for quantifying ice-sheet discharge rates and is thus crucial for improving the coupled models of the Antarctic ice sheet towards accurately predict its contribution to future global sea-level rise. However, in Antarctica, high-resolution and continuous ice velocity estimates remain elusive, which is key to unravel Antarctica's present-day ice mass balance processes. Here, we present a suite of newly estimated Antarctic-wide, annually-sampled ice velocity products at 105-m grid-spacing observed by Landsat 8 optical images data. We first describe a procedure that can automatically calibrate and integrate ice displacement maps to generate Antarctic-wide seamless ice velocity products. The annual ice velocity mosaics are assembled using a total of 250,000 displacement maps inferred from more than 80,000 Landsat 8 images acquired between December 2013 and April 2019. The new annual Antarctic ice velocity data product exhibits an improved quantification of near-decadal Antarctic-wide ice flow, and an opportunity to investigate ice dynamics at a higher spatial resolution and annual sampling, as compared to existing data products. Validation studies confirmed improved accuracy and consistency of this new data product, when compared with independently estimated optical and radar ice velocity data products, as well as in situ data. [ABSTRACT FROM AUTHOR]

Atmospheric correction is used to remove atmospheric effects on remotely sensed imagery, so it could change its properties and, in the end, all the derived results from it. The objective of this research was to analyse the effect of different atmospheric corrections on radiometric indices using Landsat 8 and Sentinel-2 satellite. Radiometric indices used in this research are Normalized difference vegetation index (NDVI), Normalized difference water index (NDWI), Soil-adjusted vegetation index (SAVI) and Modified soil-adjusted vegetation index (MSAVI). The analysis was done using five pairs of contemporary images from Landsat 8 and Sentinel-2. On the basis of those images, our results indicate that NDVI values between those two datasets did not have a statistically significant difference (p > 0.05) and have the strongest correlation for all used atmospheric corrections (r1 = 0.989, r2 = 0.967, r3 = 0.983). We concluded that it is possible to combine radiometric indices values from Landsat 8 and Sentinel-2 data. [ABSTRACT FROM AUTHOR]

In the current study, a combination of automated lineament extraction from Landsat 8 satellite imagery and 3D interactive visual interpretation (using DEM) alongside image processing techniques (Gram-Schmidt pan-sharpening, convolution directional filter) was carried out to investigate expression and influence of tectonic activity on drainage morphometry within Qorveh-Dehgolan basin. The watersheds, derived from burned DEM using SWAT, were categorized into high and low-altitude watersheds depending on the associated relief. Analysis of spatial relationship of lineament density with drainage density and basin slope revealed that structural lineaments have a decisive control over drainage density distribution particularly at higher basin slopes of certain watersheds. These high-altitude watersheds, associated with more-resistant lithology, were also found to cover higher percentage of total lineaments compared to the lower-altitude watersheds associated with less-resistant lithology. The analysis presented significant insights into the influence of tectonic activity on drainage network in a relatively unexplored area and provided vital baseline information for future investigations. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is needed in a range of applications in hydrology, climatology, ecology, and agriculture. Remote sensing-based estimation is the only viable and economical method for ET estimation over large areas. The current Landsat satellites provide images every 16 days limiting the ability to capture biophysical changes affecting ET. Thus, we explored the potential integration of Landsat 8 and Sentinel-2 data for estimating ET using a surface energy balance model. The results indicate the proposed Landsat-Sentinel data fusion approach substantially reduced relative errors from 48% to 10% on area-wide and from 49% to 17% on pixel-wide compared to linear interpolation between two Landsat images. The proposed approach had a better agreement with expected actual ET maps across high-vegetation conditions than in low-vegetation conditions. The finer temporal resolution and better accuracy of ET maps based on Landsat-Sentinel integration is of great importance in managing limited water resources. [ABSTRACT FROM AUTHOR]

Background: Traditional field-based methods of measurement of biomass and carbon storage face difficulty in collecting time-consuming and expensive, suggests the use of remote sensing-based techniques. It estimates the economic value of the aboveground biomass (AGB) using satellite remote sensing across the Hyrcanian forests of Iran. Methods: The Landsat-8 OLI sensor data were combined with field-based allometric information of 186 circular sample plots. The AGB was calculated at the plot level using the collected data and specific volumetric mass for species in the studied area. It was followed by calculating the carbon storage using a 50% carbon coefficient and the photosynthesis equation at the forest parcel level. Model results using the random forest and support vector machines. The carbon sequestration value was calculated with USD 25.3 as a shadow value of carbon in 2014 and using the replacement cost approach. Results: The highest performances achieved by RF for biomass, carbon storage and the carbon storage value (Iranian Rials of 0.67% and 16%, respectively). The value was derived once at the plot level of 12.22 million IRR (370.43 USD) per ha. In addition, at the parcel level, which resulted in an estimated value of 12.87 million IRR (390.24 USD) per ha. [ABSTRACT FROM AUTHOR]

We demonstrated an innovative learning method of convolutional neural network (CNN) to identify landuse and land cover (LULC) patterns and extract features to disaggregate socio-economic factors by using remote sensing imageries at 30 m spatial resolution. The training labels were extracted from the historical LULC map to reduce the huge cost of labelling work, and to provide an inaccuracy but sufficient training dataset. The fully connected layer of the trained CNN was extracted as disaggregating features to map socio-economic factors of population and gross domestic product (GDP). Results indicate that current method can attain 92% overall agreement of LULC identification with the cross-validation of other products. The determination coefficient of disaggregating socio-economic factors can reach 0.945 for population density, and 0.876 for GDP density with the cross-validation at county level. [ABSTRACT FROM AUTHOR]

Extracting built-up areas from remote sensing data like Landsat 8 satellite is a challenge. We have investigated it by proposing a new index referred as built-up land features extraction index (BLFEI). The BLFEI index takes advantage of its simplicity and good separability between the four major component of urban system, namely built-up, barren, vegetation and water. The histogram overlap method and the spectral discrimination index (SDI) are used to study separability. BLFEI index uses the two bands of infrared shortwaves, the red and green bands of the visible spectrum. OLI imagery of Algiers, Algeria, was used to extract built-up areas through BLFEI and some new previously developed built-up indices used for comparison. The water areas are masked out leading to Otsu's thresholding algorithm to automatically find the optimal value for extracting built-up land from waterless regions. BLFEI, the new index improved the separability by 25% and the accuracy by 5%. [ABSTRACT FROM AUTHOR]

Water hyacinth, Eichhornia crassipes (C. Mart) Solms (Pontederiaceae), is an invasive aquatic macrophyte with major negative economic and ecological impacts in Rwanda and other East African countries since its establishment in the region in the 1960s. Reliable estimates of water hyacinth distribution are required to determine the severity of the problem and identify waterbodies requiring management. Remote sensing techniques, based on the Landsat 8 sensor, offer promising alternatives to accurately detect, map and monitor the extent of the water hyacinth invasion in Rwandan waterbodies. The aim of the current study was to investigate the utility of multispectral remote sensed imagery using Random Forest and Support Vector Machine algorithms to detect and map water hyacinth in Rwandan waterbodies. Random Forest had a high overall accuracy of 85%, compared with Support Vector Machine (65%). These algorithms confirmed different levels of water hyacinth infestations in three main Rwandan rivers. Many of the wetlands along these riparian systems and most of the lakes, particularly those from the Eastern Province of the country were found to be invaded by water hyacinth. These findings would, therefore, assist government partners and policy makers to put in place sustainable methods, such as biological control, along with integrated pest management, to control the of water hyacinth invasion in Rwanda. [ABSTRACT FROM AUTHOR]

Developing techniques are required to generate agricultural land cover maps to monitor agricultural fields. Landsat 8 Operational Land Imager (OLI) offers reflectance data over the visible to shortwave-infrared range. OLI offers several advantages, such as adequate spatial and spectral resolution, and 16 day repeat coverage, furthermore, spectral indices derived from Landsat 8 OLI possess great potential for evaluating the status of vegetation. Additionally, classification algorithms are essential for generating accurate maps. Recently, multi-Grained Cascade Forest, which is also called deep forest, was proposed, and it was shown to give highly competitive performance for classification. However, the ability of this algorithm to generate crop maps with satellite data had not yet been evaluated. In this study, the reflectance at 7 bands and 57 spectral indices calculated from Landsat 8 OLI data were evaluated for its potential for crop type identification. [ABSTRACT FROM AUTHOR]

Differential Interferometric Synthetic Aperture Radar (DInSAR) methodology has been successfully employed to detect water level changes and produce corresponding water level variation maps. In this study, Agia and Kournas lakes, located in Western Crete, Greece, were used as pilot areas to monitor water level change with means of SAR interferometry and auxiliary Earth Observation (EO) data. The water level variation was monitored for the period 2015–2016, using Sentinel-1A imageries and corresponding stage water level data. Landsat 8 data were additionally used to study vegetation regime and surface water extent and how these parameters affect interferograms performance. The results highlighted the fact that the combination of SAR backscattering intensity and unwrapped phase can provide additional insight into hydrological studies. The overall analysis of both interferometric characteristics and backscattering mechanism denoted their potential in enhancing the reliability of the water-level retrieval scheme and optimizing the capture of hydrological patterns spatial distribution. [ABSTRACT FROM AUTHOR]

The aims of this study are to investigate the thermophysical behaviour of various urban land use/land cover (LULC) surfaces in an urban environment, and to examine the relationship between field measurements and remotely sensed data. Perth metropolitan area has been used as a test case. Two sets of surface albedo and land surface temperature (LST) measurements were taken at different locations on a variety of urban LULC surfaces using two procedures. Firstly, field measurements were made with a pyranometer and a hand-held infrared camera. Secondly, a Landsat 8 image was used to create maps of estimated surface albedo and LST. A new adjustment method was applied to the field measurements to adjust them to satellite overpass time. The results demonstrate that surface albedo and LST are influential factors in determining the thermal characteristics of LULC surfaces. Some built-up areas have a negative effect on the urban environment of Perth compared with non-built-up categories. The correlation between adjusted field work and Landsat 8 data is in strong agreement for surface albedo, and reasonably strong for land surface temperature. [ABSTRACT FROM AUTHOR]

k Nearest neighbour (kNN) is a non-parametric method to integrate satellite image and ground inventory data in order to estimate accurate information and becoming increasingly popular in natural resources and forest sciences. The main objective of this study was to evaluate the IRS and Landsat 8 satellite images data for forest attributes (volume, basal area and tree density) estimation by kNN method in the north forest of Iran. Our results suggested k ≤ 10 and Euclidean and Fuzzy distance metrics were the best for our study area. We concluded that Landsat 8 image improved the quality of predictions by reducing the error in kNN algorithm predictions. Also, the greatest amount of root mean square error estimated for tree density and the least was for basal area and volume, respectively in all images. Therefore, kNN method showed the best efficiency for basal area but have smaller efficiency for tree density and volume. We found that main original spectral bands have the best results versus normalised difference vegetation index and fusion data. The strength of kNN was the possibility to predict several variables consist of continuous and categorical variables. Therefore, we suggested studying forest cover types as a categorical variable by this method in diverse natural hardwood species in the north forest of Iran. [ABSTRACT FROM AUTHOR]

Globally, alien invasive plant species are considered a serious threat to native flora and fauna. In the eastern parts South Africa, the American bramble (Rubus cuneifolius) has been identified as one of the major threats to social and ecological systems. Optimal management and mitigation of American bramble spread requires reliable and cost effective approaches to determine invaded spatial extents. In this study, we test the value of the recently launched, freely available Sentinel-2 (S2), as opposed to conventional Landsat 8 imagery in mapping the American bramble. Using the Support Vector Machine classification algorithm, we seek to identify the optimal season for mapping the American bramble as well as the most influential bands in the classification process. Results show that Sentinel-2 out-performed Landsat 8 in all seasons, with summer providing the highest classification accuracy (77% accuracy). The study also shows that strategically placed Sentinel-2 bands of Near Infrared, Red edge and Short Wave Infrared significantly contribute to an increase in overall bramble mapping accuracy. This study demonstrates the value of freely available multispectral imagery in mapping American bramble at large spatial extents, hence valuable for cost-effective operational use. [ABSTRACT FROM AUTHOR]

This study investigated the impact of seasonal land cover changes on human thermal comfort outdoors in Harare, Zimbabwe in Southern Africa. Multi-temporal thermal infrared and in situ air temperature data were used to develop simple linear regression model for retrieving air temperature from land surface temperature (r²=0.6897). Season specific simple linear regression models for deriving relative humidity from land surface temperature were also developed (r² greater than 0.78). When tested against in situ observations, the LST-based approach retrieved DI with high accuracy for each sub-season (mean percentage error less than 20%). The findings showed that vegetation fraction was higher (0.60) in the most comfortable post-rainy season than in the most thermally uncomfortable season (0.43), hot season. Outdoor thermal discomfort was high in hot season (mean DI of 31 °C), while the post-rainy season was the most thermally comfortable (mean DI of 19.9 °C). During the hot season, thermal discomfort was higher in densely built-up areas (DI greater than 27 °C) than in the northern areas where low-density residential areas, forests and most well maintained parks are located (DI less than 27 °C). It was concluded that Landsat 8 data detects seasonal land use/cover and thermal discomfort changes with high accuracy. [ABSTRACT FROM AUTHOR]

Detailed information on the emperor penguin colonies is crucial for estimating total populations and analysing population migration. This study presents a new method for detecting colonies of emperor penguins by identifying areas covered with their faeces using Landsat 8 data. Top of Atmosphere (TOA) reflectance and Brightness Temperature (BT) of Landsat images are used as inputs. This method first uses normalized spectral indexes (normalized difference water index (NDWI), normalized difference faeces index (NDFI), normalized difference snow index (NDSI)), band ratios and an individual band reflectance to produce probability masks for areas covered by faeces differentiating from other land cover types. Then, after eliminating those pixels that have abnormal elevations and performing a median filter, the probability masks for those areas covered by faeces are used to derive the corresponding polygons. Subsequently, the geometric centre of the polygons of those areas covered by faeces is used as the location for a corresponding colony. For a widely distributed set of data around the Ross and Somov Seas, the overall classification accuracy is as high as 91% with a small standard deviation of 0.12. [ABSTRACT FROM PUBLISHER]

A geological map is an indispensable tool for understanding the structure of the Earth’s crust but high-quality geological maps are usually lacking in remote areas of mountainous Central Asia covered by vast deserts. The progress in remote sensing and geographical information system (GIS), as well as the advancement in analytical methods, have generated new challenges in producing modern geological maps in such regions. The presented 1:50,000 geological map along the Sagsai River summarizes new and more accurate geological data from the geologically interesting region at the contact of the supracrustal and deep crustal Tugrug and Tseel units forming the metamorphosed accretionary wedge on the S and SW slopes of the Mongolian Altai. These geological units are formed by the lower Palaeozoic volcano-sedimentary sequences affected by Devonian Barrovian metamorphism ranging from lower greenschist to granulite facies. This metamorphic basement was subsequently intruded by the post-orogenic late Carboniferous Sagsai Pluton. The presented map shows complex relationships between different crustal levels of the metamorphosed accretionary wedge and post-tectonic intrusion evaluated using a GIS, numerical processing of remote sensing data as well as field documentation and laboratory studies. [ABSTRACT FROM PUBLISHER]

Olive oil mill wastes (OOMW) constitute a major pollution factor in olive-growing regions and an important problem to be solved for the agricultural industry. Olive oil mill wastes are normally deposited in tanks, or directly into the soil or even on adjacent torrents, rivers, and lakes, posing a high risk of environmental pollution in regard to public health. This study aims to develop integrated satellite remote sensing, geographical information systems (GIS), and ground spectroscopy methodologies to detect and monitor OOMW disposal areas on the island of Crete, Greece in the Southeastern Mediterranean. More than 1000 disposal tanks were mapped through an extended global positioning system (GPS) survey that took place throughout the island. Satellite images of both high (IKONOS) and medium (Landsat 8 OLI (Operational Land Imager)) resolution were preprocessed and analysed by applying geometric, radiometric, and atmospheric corrections. A library with a spectral signature of OOMW including both different time periods and satellite sensors was developed. At the same time, ground spectroscopy campaigns were carried out and a complementary spectral signature library was developed. The narrow band reflectance of ground measurements was recalculated using the relative response filters of the corresponding satellite sensors. Both libraries were compared for their accuracy through statistical approaches and the optimum spectral range for detecting OOMW areas was estimated. Subsequently, further auxiliary image-processing techniques such as image fusion, linear spectral unmixing (LSU), false-colour composites (FCCs), image classification, and principal component analysis (PCA) were applied to satellite images to enhance OOMW patterns, and an innovative OOMW detection index for Landsat 8 was developed. In addition, several vegetation indices were applied and compared in regard to their efficiency in detecting waste ponds. Finally an integrated, semi-automatic methodology was developed in the GIS environment employing classification algorithms for the detection of waste ponds. This study highlights the potential of satellite remote sensing, GIS, and ground spectroscopy in the semi–automatic detection of OOMW disposal areas in the context of the Mediterranean landscape. [ABSTRACT FROM AUTHOR]

The successful launch of Landsat 8 provides a new data source for monitoring land cover, which has the potential to significantly improve the characterization of the earth’s surface. To assess data performance, Landsat 8 Operational Land Imager (OLI) data were first compared with Landsat 7 ETM + data using texture features as the indicators. Furthermore, the OLI data were investigated for land cover classification using the maximum likelihood and support vector machine classifiers in Beijing. The results indicated that (1) the OLI data quality was slightly better than the ETM + data quality in the visible bands, especially the near-infrared band of OLI the data, which had a clear improvement; clear improvement was not founded in the shortwave-infrared bands. Moreover, (2) OLI data had a satisfactory performance in terms of land cover classification. In summary, OLI data were a reliable data source for monitoring land cover and provided the continuity in the Landsat earth observation. [ABSTRACT FROM AUTHOR]