Search Results (32)

Today, open-source Cloud Computing platforms are valuable for geospatial image analysis while the combination of the Google Earth Engine (GEE) platform and new satellite launches greatly facilitate the monitoring of national-scale lake Water Quality (WQ). The main aim of this research is to assess the transferability and performance of published general, natural-only and artificial-only lake WQ models (Chl-a, Secchi Disk Depth-SDD- and Total Phosphorus-TP) across Greece’s WFD (Water Framework Directive) lake sampling network. We utilized Landsat (7 ETM +/8 OLI) and Sentinel 2 surface reflectance (SR) data embedded in GEE, while subjected to different atmospheric correction (AC) methods. Subsequently, Carlson’s Trophic State Index (TSI) was calculated based on both in situ and modelled WQ values. Initially, WQ models employed both DOS1-corrected (Dark Object Subtraction 1; manually applied) and GEE-retrieved respective SR data from the year 2018. Double WQ values per lake station were inserted in a linear regression analysis to harmonize the AC differences, separately for Landsat and Sentinel 2 data. Yielded linear equations were accompanied by strong associations (R² ranging from 0.68 to 0.98) while modelled and GEE-modelled TSI values were further validated based on reference in situ WQ datasets from the years 2019 and 2020. The values of the basic statistical error metrics indicated firstly the increased assessment’s accuracy of GEE-modelled over modelled TSIs and then the superiority of Landsat over Sentinel 2 data. In this way, the hereby adopted methodology was evolved into an efficient lake management tool by providing managers the means for integrated sustainable water resources management while contributing to saving valuable image pre-processing time. Full article

Landsat 8 Level 2 Collection 2 (L2C2) surface reflectance (SR) products are widely used in various scientific applications by the remote sensing community, where their accuracy is vital for reliable analysis. However, discrepancies have been observed at shorter wavelength bands, which can affect certain applications. This study investigates the root cause of these differences by analyzing the assumptions made in the Land Surface Reflectance Code (LaSRC), the atmospheric correction algorithm of Landsat 8, as currently implemented at United States Geological Survey Earth Resources Observation and Science (USGS EROS), and proposes a correction method. To quantify these discrepancies, ground truth SR measurements from the Radiometric Calibration Network (RadCalNet) and Arable Mark 2 sensors were compared with the Landsat 8 SR. Additionally, the surface pressure measurements from RadCalNet and the National Centers for Environmental Information (NCEI) were evaluated against the LaSRC-calculated surface pressure values. The findings reveal that the discrepancies arose from using a single scene center surface pressure for the entire Landsat 8 scene pixels. The pressure-related discrepancies were most pronounced in the coastal aerosol and blue bands, with greater deviations observed in regions where the elevation of the study area differed substantially from the scene center, such as Railroad Valley Playa (RVUS) and Baotao Sand (BSCN). To address this issue, an exponential correction model was developed, reducing the mean error in the coastal aerosol band for RVUS from 0.0226 to 0.0029 (about two units of reflectance), which can be substantial for dark vegetative and water targets. In the blue band, there is a smaller improvement in the mean error, from 0.0095 to −0.0032 (about half a unit of reflectance). For the green band, the reduction in error was much less due to the significantly lesser impact of aerosol on this band. Overall, this study underscores the need for a more precise estimation of surface pressure in LaSRC to enhance the reliability of Landsat 8 SR products in remote sensing applications. Full article

Mangrove ecosystems are increasingly threatened by climate change and coastal development, making precise ecological stress modeling essential for informing conservation strategies. This study employs AI-based classification techniques to classify mangroves using Landsat 8-SR OLI/TIRS sensors (2023) along the Jazan Coast, identifying a total mangrove area of 19.4 km². The ensemble classifier achieved an F1 score of 95%, an overall accuracy of 93%, and a kappa coefficient of 0.86. Ecological stress was modeled via a generalized additive model (GAM) with key predictors, including trends in the NDVI, NDWIveg (vegetation water content), NDWIow (open water), and LST from 1991 to 2023, which were derived using surface reflectance (SR) products from Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS sensors. The model exhibited strong performance, with an R² of 0.89. Model diagnostics using linear regression (R² = 0.86), a high F-statistic, minimal intercept, and 10-fold cross-validation confirmed the model’s robustness, with a consistent MSE (0.12) and cross-validated R² of 0.86. Moran’s I analysis also indicated significant spatial clustering. Findings indicate that mangroves in non-ravine, mainland coastal areas experience more ecological stress from disruptions in freshwater and sediment supply due to recent developments. In contrast, island coastal areas exhibit low stress levels due to minimal human activity, except in dense canopy regions where significant stress, likely linked to climate change, was observed. These results underscore the need for further investigation into the drivers of this ecological pressure. Full article

Accurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropagation neural network (MLBPN) and random forest (RF), have been employed to analyze the spatiotemporal distributions of the primary air pollutant from 2019 to 2022 in Guanzhong Region, China. In the conducted experiments, the RF-based model, using the MODIS AOD data, has generally demonstrated the “optimal” estimation performance for the ground-surface concentrations of the primary air-pollutants. Then, the “optimal” estimation model has been employed to analyze the spatiotemporal distribution of the various air pollutants—in terms of temporal distribution, the annual average concentrations of PM_2.5, PM₁₀, NO₂, and SO₂ in the research area showed a decreasing trend from 2019 to 2022, while the annual average concentration of CO remained relatively stable and the annual average concentration of O₃ slightly increased; in terms of the spatial distribution, the air pollution presents a gradual increase from west to east in the research area, with the distribution of higher concentrations in the center of the built-up areas and lower in the surrounding rural areas. The proposed estimation model and spatiotemporal analysis can provide reliable methodologies and data support for the further study of the air pollution characteristics in the research area. Full article

Digital soil maps are paramount for supporting environmental process analysis, planning for the conservation of ecosystems, and sustainable agriculture. The availability of dense time series of surface reflectance data provides valuable information for digital soil mapping (DSM). A detailed soil survey, along with a stack of Landsat 8 SR data and a rainfall time series, were analyzed to evaluate the influence of soil on the temporal patterns of vegetation greenness, assessed using the normalized difference vegetation index (NDVI). Based on these relationships, imagery depicting land surface phenology (LSP) metrics and other soil-forming factors proxies were evaluated as environmental covariates for DSM. The random forest algorithm was applied as a predictive model to relate soils and environmental covariates. The study focused on four soils typical of tropical conditions under pasture cover. Soil parent material and topography covariates were found to be similarly important to LSP metrics, especially those LSP images related to the seasonal availability of water to plants, registering significant contributions to the random forest model. Stronger effects of rainfall seasonality on LSP were observed for the Red Latosol (Ferralsol). The results of this study demonstrate that the addition of temporal variability of vegetation greenness can be used to assess soil subsurface processes and assist in DSM. Full article

The assessment of changes in forest coverage is crucial for managing protected forest areas, particularly in the face of climate change. This study monitored forest cover dynamics in a 6535 ha mountain area located in north-west Romania as part of the Apuseni Natural Park from 2003 to 2019. Two approaches were used: vectorization from orthophotos and Google Earth images (in 2003, 2005, 2009, 2012, 2014, 2016, 2017, and 2019) and satellite imagery (Landsat 5 TM, 7 ETM, and 8 OLI) pre-processed to Surface Reflectance (SR) format from the same years. We employed four standard classifiers: Support Vector Machine (SVM), Random Forest (RF), Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM), and three combined methods: Linear Spectral Unmixing (LSU) with Natural Breaks (NB), Otsu Method (OM) and SVM, to extract and classify forest areas. Our study had two objectives: 1) to accurately assess changes in forest cover over a 17-year period and 2) to determine the most efficient methods for extracting and classifying forest areas. We validated the results using performance metrics that quantify both thematic and spatial accuracy. Our results indicate a 9% loss of forest cover in the study area, representing 577 ha with an average decrease ratio of 33.9 ha/year⁻¹. Of all the methods used, SVM produced the best results (with an average score of 88% for Overall Quality (OQ)), followed by RF (with a mean value of 86% for OQ). Full article

Over the past decades, benefiting from the development of computing capacity and the free access to Landsat and Sentinel imagery, several fine-resolution global land cover (GLC) products (with a resolution of 10 m or 30 m) have been developed (GlobeLand30, FROM-GLC30, GLC_FCS30, FROM-GLC10, European Space Agency (ESA) WorldCover and ESRI Land Cover). However, there is still a lack of consistency analysis or comprehensive accuracy assessment using a common validation dataset for these GLC products. In this study, a novel stratified random sampling GLC validation dataset (SRS_Val) containing 79,112 validation samples was developed using a visual interpretation method, significantly increasing the number of samples of heterogeneous regions and rare land-cover types. Then, we quantitatively assessed the accuracy of these six GLC products using the developed SRS_Val dataset at global and regional scales. The results reveal that ESA WorldCover achieved the highest overall accuracy (of 70.54% ± 9%) among the global 10 m land cover products, followed by FROM-GLC10 (68.95% ± 8%) and ESRI Land Cover (58.90% ± 7%) and that GLC_FCS30 had the best overall accuracy (of 72.55% ± 9%) among the global 30 m land cover datasets, followed by GlobeLand30 (69.96% ± 9%) and FROM-GLC30 (66.30% ± 8%). The mapping accuracy of the GLC products decreased significantly with the increased heterogeneity of landscapes, and all GLC products had poor mapping accuracies in countries with heterogeneous landscapes, such as some countries in Central and Southern Africa. Finally, we investigated the consistency of six GLC products from the perspective of area distributions and spatial patterns. It was found that the area consistencies between the five GLC products (except ESRI Land Cover) were greater than 85% and that the six GLC products showed large discrepancies in area consistency for grassland, shrubland, wetlands and bare land. In terms of spatial patterns, the totally inconsistent pixel proportions of the 10 m and 30 m GLC products were 23.58% and 14.12%, respectively, and these inconsistent pixels were mainly distributed in transition zones, complex terrains regions, heterogeneous landscapes, or mixed land-cover types. Therefore, the SRS_Val dataset well supports the quantitative evaluation of fine-resolution GLC products, and the assessment results provide users with quantitative metrics to select GLC products suitable for their needs. Full article

Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed of 240 km/h. In this regard, Earth Observation (EO) Satellite Remote Sensing (SRS) images can effectively highlight oil spills in marine areas as a “fast and no-cost” technique. However, clouds and the sea surface spectral signature complicate the interpretation of oil spill areas in the optical images. In this study, Principal Component Analysis (PCA) has been applied of Landsat-8 and Sentinel-2 SRS images to improve information from the optical sensor bands. The PCA produces an output unrelated to the main bands, making it easier to distinguish oil spills from clouds and seawater due to the spectral diversity between oil, clouds, and the seawater surface. Then, an additional step has been applied to highlight the oil spill area using PCAs with different band combinations. Furthermore, Sentinel-1 (SAR), Sentinel-2 (optical), and Landsat-8 (optical) SRS images have been analyzed with cross-sections to suppress the “look-alike” effect of marine oil spill areas. Finally, mean and high-pass filters were used for Land Surface Temperature (LST) SRS images estimated from the Landsat thermal band. The results show that the seawater value is about −17.5 db and the oil spill area shows a value between −22.5 db and −25 db; the Landsat 8 satellites thermal band 10, depicting contrast at some areas for oil spill, can be determined by the 3 × 3 and 5 × 5 Kernel High pass and the 3 × 3 Mean filter. The results demonstrate that the SRS images should be used together to improve oil spill detection studies results. Full article

The leaf area index (LAI) is a key crop biophysical variable influencing many vegetation processes. Spatial LAI estimates are essential to develop and improve spatial modeling tools to monitor vegetation conditions at large regional scales. Numerous optical remote sensing methods have been explored to retrieve crop-specific LAI at a regional scale using satellite observations. However, a major challenge is selecting a method that performance well under various conditions without local scale calibration. As such, we assessed the performance of existing statistical and physical approaches, developed based on parametric, non-parametric and radiative transfer model (RTM)-look-up-table based inversion, using field observations from two geographically distant locations and Landsat 5, 7, and 8 satellite observations. These methods were implemented for corn and soybeans cultivated at two locations in the U.S (i.e., Mead, Nebraska, and Bushland, Texas). The evaluation metrics (i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²)) were used to study the performance of each method, and then the methods were ranked based on these metrics. Our study showed that overall parametric methods outperformed other methods. The RMSE (MAE) for the top five methods was less than 1.3 (0.95) for corn and 1.0 (0.8) for soybeans, irrespective of location. Even though they outperformed, parametric methods exhibited inconsistency in their performance. For instance, the SR_CA_cross method ranked 1 for corn, however, it performed poorly for soybean (ranked 15). The non-parametric methods showed moderate accuracy partly due to the availability of a smaller number of observations for training. The RTM-LUT inversion physical-based approach was found to perform reasonably well RMSE (MAE) less than 1.5 (1.0) consistently irrespective of location and crop, implying that this approach is more suitable for regional-scale LAI estimation. The results of this study highlighted the drawbacks and advantages of available optical remote sensing approaches to estimate LAI for corn and soybean crops using Landsat imagery. These results are of interest for remote sensing and modeling communities developing spatial-scale approaches to model and monitor agricultural vegetation. Full article

Efficient implementation of remote sensing image classification can facilitate the extraction of spatiotemporal information for land use and land cover (LULC) classification. Mapping LULC change can pave the way to investigate the impacts of different socioeconomic and environmental factors on the Earth’s surface. This study presents an algorithm that uses Landsat time-series data to analyze LULC change. We applied the Random Forest (RF) classifier, a robust classification method, in the Google Earth Engine (GEE) using imagery from Landsat 5, 7, and 8 as inputs for the 1985 to 2019 period. We also explored the performance of the pan-sharpening algorithm on Landsat bands besides the impact of different image compositions to produce a high-quality LULC map. We used a statistical pan-sharpening algorithm to increase multispectral Landsat bands’ (Landsat 7–9) spatial resolution from 30 m to 15 m. In addition, we checked the impact of different image compositions based on several spectral indices and other auxiliary data such as digital elevation model (DEM) and land surface temperature (LST) on final classification accuracy based on several spectral indices and other auxiliary data on final classification accuracy. We compared the classification result of our proposed method and the Copernicus Global Land Cover Layers (CGLCL) map to verify the algorithm. The results show that: (1) Using pan-sharpened top-of-atmosphere (TOA) Landsat products can produce more accurate results for classification instead of using surface reflectance (SR) alone; (2) LST and DEM are essential features in classification, and using them can increase final accuracy; (3) the proposed algorithm produced higher accuracy (94.438% overall accuracy (OA), 0.93 for Kappa, and 0.93 for F1-score) than CGLCL map (84.4% OA, 0.79 for Kappa, and 0.50 for F1-score) in 2019; (4) the total agreement between the classification results and the test data exceeds 90% (93.37–97.6%), 0.9 (0.91–0.96), and 0.85 (0.86–0.95) for OA, Kappa values, and F1-score, respectively, which is acceptable in both overall and Kappa accuracy. Moreover, we provide a code repository that allows classifying Landsat 4, 5, 7, and 8 within GEE. This method can be quickly and easily applied to other regions of interest for LULC mapping. Full article

Optical classification for water bodies was carried out based on satellite remote sensing data, which avoided the limitation of having a limited amount of in situ measured spectral data. Unsupervised cluster analysis was performed on 53,815 reflectance spectra extracted at 500-m intervals based on the same season or quasi-same season Landsat 8 SR data using the algorithm of fuzzy c-means. Lakes and reservoirs in the study area were comprehensively identified as three optical types representing different limnological features. The shape and amplitude characteristics of the reflectance spectra for the three optical water types indicated that one corresponds to the clearest water, one corresponds to turbid water, and the other is moderate clear water. The novelty detection technique was further used to label the match-ups of the in situ data set collected during 2006 to 2019 in 12 field surveys based on mathematical rules of the three optical water types. The results confirmed that each optical water type was associated with different bio-optical properties, and the total suspended matter of the clearest, moderate clear and turbid water types were 14.99 mg/L, 41.06 mg/L and 83.81 mg/L, respectively. Overall, the clearest, moderate clear and turbid waters in the study area accounted for 49.3%, 36.7% and 14.0%, respectively. The spatial distribution of optical water types in the study area was seamlessly mapped. Results showed that the bio-optical conditions of the water distributed across the southeast region were roughly homogeneous, but in most of other regions and within some water bodies, they showed a patchy distribution and heterogeneity. This study is useful for monitoring water quality and provides a useful foundation to develop or tuning algorithms to retrieve water quality parameters. Full article

Crop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R²) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R² was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields. Full article

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion. Full article

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R²) values of 0.0020 sr⁻¹ and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects. Full article

The major lockdown due to the COVID-19 pandemic has affected the socio-economic development of the world. On the other hand, there are also reports of reduced pollution levels. In this study, an indicative analysis is adopted to understand the effect of lockdown on the changes in the water quality parameters for Lake Hussain Sagar using two remote sensing techniques: (i) spectral reflectance (SR) and (ii) chromaticity analysis (Forel-Ule color Index (FUI) and Excitation Purity). The empirical relationships from earlier studies imply that (i) increase in SR values (band B2) indicates a reduction in Chlorophyll-a (Chl-a) and Colored Dissolved Organic Matter (CDOM) concentrations, and (ii) increase in FUI indicates an increase in Total Suspended Solids (TSS). The Landsat 8 OLI satellite images are adopted for comparison between (i) January to May of year 2020: the effect of lockdown on water quality, and (ii) March and April for years 2015 to 2020: historical variations in water quality. The results show notable changes in SR values and FUI due to lockdown compared to before lockdown and after unlock suggesting a significant reduction in lake water pollution. In addition, the historical variations within April suggest that the pollution levels are least in the year 2020. Full article