Operational Land Imager Data Research Articles

Volcán de Fuego (Guatemala) monitoring with the Normalized Hotspot Indices (NHI) tool

Abstract. Volcán de Fuego is a stratovolcano located in Guatemala among the most active in the world. In this work, we investigate its eruptive activity from space by means of the Normalized Hotspot Indices (NHI) algorithm. The latter runs operationally under the Google Earth Engine (GEE) platform providing information on active volcanoes at global scale by means of daytime Sentinel 2 MSI (Multispectral Instrument) and Landsat 8/9 OLI/OLI2 (Operational Land Imager) data. In study, we present the results of a time series analysis performed by investigating 40 years of Landsat observations through the NHI algorithm. Results show that during the major periods of thermal activity (e.g., 2000−2003; 2012−2013; 2015−2018) the Volcán de Fuego generated extended lava flows, which were well identified and mapped by satellite. In addition, periods of reduced thermal activity (e.g., 2022−May 2024) marked by NHI were in good agreement with information provided by independent sources. The accurate localization and mapping of high-temperature features, and the characterization of different eruptive phases, demonstrate that the NHI algorithm, through the GEE-App available online (https://sites.google.com/view/nhi-tool/nhi-tool-for-volcanoes), may support scientists also in the monitoring of frequently active volcanoes such as the Volcán de Fuego, providing information about changes of thermal volcanic activity that could precede future and more dangerous eruptions.

The first global catalogue of gas flaring sources derived from a multi-temporal time series of OLI and MSI daytime data: the DAFI v2 algorithm

Abstract Gas flaring (GF) is an industrial phenomenon requiring a special attention for the serious impacts on environment, climate and human health. To analyses and map GF sites (GFs), the Daytime Approach for GF Investigation (DAFI), which is based on daytime data from the Operational Land Imager (OLI) aboard the Landsat-8 satellite, has recently been developed. The GFs catalogue from the DAFI system, spanning over the years 2013–2021, represents the first worldwide GF product generated from mid-high spatial resolution data (30 m). The DAFI version 2 (v2), also including information from the Multispectral Instrument (MSI) aboard Sentinel-2 satellites, already showed an improved capacity in the mapping of GFs over Iran and Iraq. In this work, the new catalogue arising from the porting of DAFI v2 at global scale is presented and discussed. By using a parallel workflow, based on a loop tiling scheme running in Google Earth Engine, DAFI v2 allowed us to extend the GF inventory globally up to 2023, by analyzing ∼4 million of OLI/OLI-2 (Collection 2) and MSI imagery. Results of this study show: (i) an increased DAFI v2 capacity, of about 41%, in detecting GFs compared to the previous DAFI version; (ii) a downward trend (∼4.6%) in terms of GF sites detected from 2021 to 2023. These findings demonstrated that DAFI v2, which will also include the estimates of radiative power at different spatial scales, may contribute in assessing and monitoring the GFs activities and in evaluating the effectiveness of the greenhouse gas-emission reduction strategies at global, continental, national and local scales.

Assessing the effects of Water hyacinth proliferation on biochemical oxygen demand with operational land imager data

The rapid proliferation of water hyacinth (Eichhornia crassipes) in water bodies poses a significant threat to ecosystems and communities that rely on these resources. While this plant can offer certain benefits, its excessive growth can lead to detrimental ecological and social impacts. This study aimed to assess the relationship between water hyacinth coverage and Biochemical Oxygen Demand (BOD) levels in the Sonso Lagoon, Colombia, using Landsat 8 satellite imagery and in-situ measurements. Water hyacinth coverage was classified and quantified from Landsat 8 images. These data were then correlated with BOD levels obtained through periodic monitoring of the lagoon. The analysis revealed a strong negative correlation (ρ=−0.83; p−value<0.05**) between water hyacinth coverage and BOD, demonstrating the plant's impact on water quality. A predictive model was developed to estimate BOD levels based on satellite-derived water hyacinth data. The integration of remote sensing and in-situ measurements offers an effective strategy for monitoring water hyacinth proliferation and its ecological consequences. The proposed approach enables rapid assessment of water quality parameters, facilitating timely implementation of mitigation and control measures. This methodology can be extended to quantify other invasive aquatic plant species and their impacts on aquatic ecosystems.

Methods for detecting green tide in the Yellow Sea using Google Earth Engine platform

The green tide was a blooming phenomenon consisting of outbreaks and aggregations of marine macroalgae, causing severe damage to the ecosystem of the Yellow Sea in China. Remote sensing technology was considered an effective means of green tide detection. Currently, commonly used low spatial resolution satellites cannot satisfy the green tide detection in the fine-scale range. To better improve the accuracy of green tide identification, based on the Commission Internationale del'éclairage (CIE) system, two new green tide detection algorithms (CIE xy, hue angle α) were established using Sentinel-2 MultiSpectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) data on the Google Earth Engine (GEE) platform. These algorithms were validated by introducing the quantitative index Dgw from "Interclass Distance". The CIE xy algorithm and the hue angle α algorithm based on Sentinel-2 MSI exhibited higher Dgw values of 10.29 and 5.87, respectively, surpassing the performance of the two algorithms based on the Landsat-8 OLI in green tide identification (Dgw values of 3.36 and 5.17, respectively). Meanwhile, the proposed algorithms were compared with normalized difference vegetation index (NDVI), difference vegetation index (DVI), ratio vegetation index (RVI), floating algae index (FAI), enhanced vegetation index (EVI), and algal bloom detection index (ABDI) algorithms using Sentinel-2 MSI images. The results showed that the CIE xy algorithm and the NDVI algorithm outperformed the other algorithms. Especially in turbid water, the CIE xy algorithm exhibited significant advantages. However, the hue angle α algorithm still showed some potential in identifying green tide, particularly in the presence of thin cloud interference. These findings can provide effective information support for the monitoring of green tide in the Yellow Sea.

The use of ASTER and landsat remote sensing data for exploring hydrothermal mineral in Arabian-Nubian Shield

Multispectral remote sensing data provided valuable information for hydrothermal minerals exploration. In this article, Landsat-8 Operational Land Imager (OLI) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were processed and analyzed using band ratio and principal component techniques to demarcate areas of hydrothermal mineral resources by highlighting the most influenced zone of hydrothermal alteration (high-grade) in Egypt. The area is a part of the Arabian-Nubian Shield, covering 1068 km2, and comprises wide varieties of Pre- and Pan-African rock assemblages. Preparing and integrating thematic layers consisting of the grade of hydrothermal intensity in a GIS technique supported generating a hydrothermal mineral predictive map (HMPM) using a multi-criteria decision-making technique. The plausible zone of very high mineral resources covers about 6.57 %. Analysis of ASTER data revealed that this zone is reliable with phyllic and argillic minerals and iron-oxides. The results of OLI data helped in demarcating potential areas of hydrothermal alteration areas (Al–OH) mixed with iron oxides. Field observations and investigations enabled the validation of the HMPM. The spatial distribution of hydrothermal zones throughout the study was reliable to that observed in the mine areas. Field and ore microscopic examination verified the existence of Au-sulfide minerals connected with hydrothermal alteration zones (HAZs) in different areas e.g., Abu-Marawat, Gidami, Semna, Abu-Gaharish, and Hamama mines. Overall, integrating multi-spectral data through overlay analysis techniques can provide valuable information about hydrothermal mineral resources in an accurate and cost-effective approach.

Mapping hydrothermal alterations and associated lineaments within Kaiama, north-central Nigeria, using Landsat 8 Operational Land Imager Data and Digital Elevation Model

This research focuses on the geological investigation of Kaiama region, which is characterized by a diverse range of rock formations, including mylonites, porphyritic granites, gneiss, schist, phyllites, and pink granites. The study employs remote sensing techniques, utilizing Landsat 8 OLI data and Digital Elevation Models, to systematically map the spatial distribution of hydrothermal alterations and tectonic structures associated with mineralization in the Kaiama area. Various image processing methods such as Color Composites, Band Rationing, and Principal Component Analysis (PCA) were employed to extract valuable information from the collected datasets. Utilizing Sabins band ratios (4/2, 6/7, and 6/5), we categorized alterations associated with iron oxides, clay minerals, and ferrous minerals. PCA was applied to refine the identification of alteration zones, using two distinct sets of images: H-image (comprising bands 2, 4, 5, and 7) and F-image (comprising bands 2, 5, 6, and 7), which represented iron-oxide and hydroxyl mineral deposits, respectively. The synthesis of H, F, and H+F images in RGB format provided an optimal representation of the spatial distribution of hydrothermal alterations, exhibiting a strong positive correlation with known mining regions for gold, copper, wolframite, and tantalite within the study area. Furthermore, a comprehensive analysis of regional lineaments revealed a consistent NNE-SSW to NE-SW correlation, suggesting a predominant control on mineralization trends. This study advocates for adopting remote sensing techniques, specifically Landsat 8 data and DEM, as an effective approach for mapping hydrothermal alterations and identifying key structural controls associated with mineralization.

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

Development of Soil-Suppressed Impervious Surface Area Index for Automatic Urban Mapping

Expanding urban impervious surface area (ISA) mapping is crucial to sustainable development, urban planning, and environmental studies. Multispectral ISA mapping is challenging because of the mixed-pixel problems with bare soil. This study presents a novel approach using spectral and temporal information to develop a Soil-Suppressed Impervious Surface Area Index (SISAI) using the Landsat Operational Land Imager (OLI) data set, which reduces the soil but enhances the ISA signature. This study mapped the top 12 populated megacities using SISAI and achieved an over-all accuracy of 0.87 with an F1-score of 0.85. It also achieved a higher Spatial Dissimilarity Index between the ISA and bare soil. However, it is limited by bare gray soil and shadows of clouds and hills. SISAI encourages urban dynamics and inter-urban compari- son studies owing to its automatic and unsupervised methodology.

Monitoring of chlorophyll-a and suspended sediment concentrations in optically complex inland rivers using multisource remote sensing measurements

In recent decades, phytoplankton proliferation and sediment input to rivers (especially urban rivers) have become more dramatic under the compound pressure of climate change and human activities. Given the generally narrow width of rivers and current high spatial resolution satellites, which are limited by band settings, bandwidth, and the signal-to-noise ratio, UAVs with their exceptional spatiotemporal resolution can be used as a useful tool for river environmental monitoring and inversion uncertainty assessment. In this study, UAV-based hyperspectral (X20P) and multispectral (P4M) images, along with Sentinel-2 MultiSpectral Instrument (MSI), Landsat-8 Operational Land Imager (OLI) and Landsat-9 OLI2 data, were used to assess the uncertainty in retrieving chlorophyll-a (Chla) and suspended sediment (SS) concentrations in rivers. Chla and SS models based on UAV and satellite data were constructed using stepwise multiple regression and typical Chla and SS retrieval algorithms, respectively, and the performance of the models was the focus of our research. The results demonstrated that in the Chla concentration inversion, each sensor performed as follows: X20P > P4M > Landsat9 OLI2 > Sentinel-2 MSI > Landsat8 OLI, and the performance in the SS concentration inversion was as follows: X20P > Sentinel-2 MSI > P4M > Landsat9 OLI2 > Landsat8 OLI. In addition, the uncertainty of high spatial resolution satellite retrievals was analyzed with the assistance of the UAV-based model. Results showed that narrow bandwidths and finely tuned band settings are more essential for the Chla inversion. The typical Chla retrieval algorithm, NDCI, is only effective in certain bands (band 1 from 684 to 724 nm and band 2 from 660 to 680 nm). It is also noted that Landsat8 and Landsat9 lack some key band settings (e.g., the red-edge band of 700–710 nm), severely limiting practical application in relation to Chla. However, specific variances in different sensor bands have a relatively small impact on SS inversion, for example, the correlation between SS and the R/B (a typical SS retrieval algorithm) constructed by each sensor ranged from 0.68 to 0.77. Chla monitoring, on the other hand, necessitates a higher spatial resolution than SS monitoring. The accuracy decreased markedly when UAV images were resampled to 10 m and 30 m spatial resolution. However, it is not as crucial for the SS inversion, images with the original spatial resolution (RMSE<30cm = 6.28 mg/L) were resampled to 10 m resolution (RMSE10m = 5.85 mg/L) and 30 m resolution (RMSE30m = 4.08 mg/L) while using P4M for SS inversion, and the accuracy increased. Our results demonstrated and highlighted various options for future monitoring of Chla and SS, while exploiting the synergy between UAVs and satellites to achieve more precise observations at greater spatial and temporal scales, which will benefit aquatic environment management and protection.

Space based Earth observation approach for exploring hydrocarbon prospects in an intracratonic rift basin – A study in north Cambay basin, Gujarat

In the present study, we carried out a conjugate analysis of Landsat-8 Operational Land Imager (OLI), Sentinel-2B Multi-Spectral Instrument (MSI), Advanced Land Observation Satellite Phased Array L-band SAR (ALOS PALSAR), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over a study area of northern Cambay Basin to delineate prospective areas for hydrocarbon exploration. In this regard, we utilized feature-oriented principal component (PC) images of Landsat OLI data, PC images of Sentinel-2B visible and near-infrared (VNIR)-short-wave infrared (SWIR) bands, thresholded Cartosat-1 Digital Elevation Model (DEM), and polarization image composite of ALOS PALSAR data to delineate geomorphic and lineament features in the study area. Most of the geomorphic features, such as scroll plain, gullies, elevated pediment, circular drainage anomalies, etc., indicate localized structural uplift. Geomorphic anomalies and lineaments, essential for identifying structural controls of near-surface hydrocarbon accumulation, were identified using enhanced satellite data. Geophysical anomalies derived from gravity datasets supplemented the presence and down-depth continuity of geological structures. Spectral profiles of calcrete-bearing soil samples were used to identify calcrete-rich zones in the optical remote sensing image data as a proxy owing to field evidence of close association of calcrete-rich soils with known subsurface hydrocarbon accumulations. Sub-pixel mapping algorithm Constrained Energy Minimization (CEM) applied to SWIR bands of ASTER data was used to delineate surface proxies of near-surface hydrocarbon seepage. Isopach maps of shallowest reservoir formations (Mid-/ Late Eocene) were used as indicators of favorable near-surface hydrocarbon accumulation.Earth observation-based geomorphic, lineament, and spectral anomalies were integrated with supplementary datasets, such as reservoir isopach and absorbed gas anomalies, as controls using the weighted summation method for quantitative deduction of the hydrocarbon potential map of the study area. The weights for input variables or parameters (geomorphic, spectral, geochemical, etc.) were inferred based on spatial associations of these anomalous units/variables with the existing oil & gas well locations. The identified potential map is in broad synergy with the field observations on localized uplift, geo-botanical anomalies, and calcrete-rich zones within the extent of high & moderate hydrocarbon potential areas.

Mapping Land Use and Land Cover Classes in São Paulo State, Southeast of Brazil, Using Landsat-8 OLI Multispectral Data and the Derived Spectral Indices and Fraction Images

This work aims to develop a new method to map Land Use and Land Cover (LULC) classes in the São Paulo State, Brazil, using Landsat-8 Operational Land Imager (OLI) data. The novelty of the proposed method consists of selecting the images based on the spectral and temporal characteristics of the LULC classes. First, we defined the six classes to be mapped in the year 2020 as forest, forest plantation, water bodies, urban areas, agriculture, and pasture. Second, we visually analyzed their variability spectral characteristics over the year. Then, we pre-processed these images to highlight each LULC class. For the classification, the Random Forest algorithm available on the Google Earth Engine (GEE) platform was utilized individually for each LULC class. Afterward, we integrated the classified maps to create the final LULC map. The results revealed that forest areas are primarily concentrated in the eastern region of São Paulo, predominantly on steeper slopes, accounting for 19% of the study area. On the other hand, pasture and agriculture dominated 73% of all São Paulo’s landscape, reaching 39% and 34%, respectively. The overall accuracy of the classification achieved 89.10%, while producer and user accuracies were greater than 84.20% and 76.62%, respectively. To validate the results, we compared our findings with the MapBiomas Project classification, obtaining an overall accuracy of 85.47%. Therefore, our method demonstrates its potential to minimize classification errors and offers the advantage of facilitating post-classification editing for individual mapped classes.

Utilizing Remote Sensing and Satellite-Based Bouguer Gravity data to Predict Potential Sites of Hydrothermal Minerals and Gold Deposits in Central Saudi Arabia

This article aims to aid in exploring and forecasting hydrothermal minerals and gold deposits in Central Saudi Arabia (SA), with a focus on structural contexts. Remote sensing (RS) and satellite-based Bouguer gravity (SBG) data were integrated in order to create a mineral prediction map for the researched location. Data from the Landsat Operational Land Imager (OLI) and Shuttle Radar Topography Mission (SRTM) were transformed and enhanced using a variety of approaches. The delineation of hydrothermal alteration zones (HAZs) and highlighting of structural discontinuities in the OLI data were made possible using band ratios and oriented principal component analysis (PCA). Additionally, the underlying structural features were successfully exposed by processing the SBG using a variety of edge detection techniques, like the analytical signal (AS), total horizontal derivative (THD), tilt angle (TA), horizontal tilt angle (TDX), theta map (TM), horizontal derivative of the tilt derivative (HD_TDR), horizontal gradient of the tilt angle (HGTA), tilt angle of the analytical signal (TAAS), and soft sign function (SF). As a result, more prominent lineaments were found in the NW–SE, NNW–SSE, NE–SW, and NNE–SSW directions than in the N–S and E–W directions. The GIS incorporated surface/subsurface geological structure density maps with zones of hydrothermal alteration. It was found that the lineaments derived from the analysis of the RS and SBG data were more in line with the HAZs, which demonstrated the common connection between alteration zones and deep lineaments. The findings revealed a mineral prediction map with extremely low to extremely high probabilities. Overall, combining RS and SBG data effectively identified probable mineralization sites associated with hydrothermal processes and made it easier to create this study’s final predictive mineralization map.

Investigating the effectiveness of Landsat-8 OLI and Sentinel-2 MSI satellite data in monitoring the effects of drought on surface water resources in the Western Cape Province, South Africa

Drought frequency and magnitude are increasing, posing significant risks to surface water bodies, particularly in arid and semi-arid regions. Leveraging on moderate resolution satellite data, such as Landsat-8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI), offers unique opportunities for monitoring surface water availability and drought impacts. In this study, we assessed the extent to which Landsat-8 OLI and Sentinel-2 MSI satellite data can be used to characterise and monitor the impacts of drought on water resources in the Western Cape, South Africa. Multispectral indices including Normalised Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI), and the Land Surface Water Index (LSWI+5) were computed to determine the most suitable method for surface water detection and drought monitoring. Sentinel-2-derived NDVI emerged as the most suitable index for mapping surface waterbodies, demonstrating an overall accuracy of 77.27%. However, LSWI+5 exhibited limitations, misclassifying built-up and mountainous areas as surface waterbodies. SPI, VCI, and WRSI effectively identified drought periods and correlated well with climate data, revealing low rainfall and high evapotranspiration rates during the 2016–2018 drought period. These findings provide valuable insights into surface water variability and the impacts of drought on water resources. Overall, our study underscores the feasibility of utilizing moderate resolution datasets to assess drought impacts on surface water resources, enabling improved water resource management, drought detection, and preparedness efforts in resource limited environments.

Novel Threshold Self-Regulating Water Extraction Method

Water resources are crucial for human activities and sustainable socioeconomic development. Understanding surface water information can play a key role in water resource management, which affects the global water cycle and ecological environments. Considering the Hailar River water body as an example, this study proposes a new threshold self-learning water body extraction method (TSLWEM) based on modified normalized difference water index (MNDWI) data. The optimal water extraction thresholds determined by the TSLWEM algorithm for four test images were −0.0030, 0, 0.1990, and −0.0800. The TSLWEM algorithm effectively identified the target water body with recognition accuracies of 98.08%, 99.93%, 93.39%, and 93.20% for the four test images. Moreover, it can accurately identify small tributaries, such as lakes and rivers. The TSLWEM algorithm is suitable for Landsat 8 Operational Land Imager (OLI) data, which can effectively monitor and map complex surface water in temperate and semiarid regions while improving the accuracy of water body identification. The study’s findings provide technical support for the protection of water resources as well as their rational utilization and monitoring.

Predicting Dynamics of Soil Salinity and Sodicity Using Remote Sensing Techniques: A Landscape-Scale Assessment in the Northeastern Egypt

Traditional mapping of salt affected soils (SAS) is very costly and cannot precisely depict the space–time dynamics of soil salts over landscapes. Therefore, we tested the capacity of Landsat 8 Operational Land Imager (OLI) data to retrieve soil salinity and sodicity during the wet and dry seasons in an arid landscape. Seventy geo-referenced soil samples (0–30 cm) were collected during March (wet period) and September to be analyzed for pH, electrical conductivity (EC), and exchangeable sodium percentage (ESP). Using 70% of soil and band reflectance data, stepwise linear regression models were constructed to estimate soil pH, EC, and ESP. The models were validated using the remaining 30% in terms of the determination coefficient (R2) and residual prediction deviation (RPD). Results revealed the weak variability of soil pH, while EC and ESP had large variabilities. The three indicators (pH, EC, and ESP) increased from the wet to dry period. During the two seasons, the OLI bands had weak associations with soil pH, while the near-infrared (NIR) band could effectively discriminate soil salinity and sodicity levels. The EC and ESP predictive models in the wet period were developed with the NIR band, achieving adequate outcomes (an R2 of 0.65 and 0.61 and an RPD of 1.44 and 1.43, respectively). In the dry period, the best-fitted models were constructed with deep blue and NIR bands, yielding an R2 of 0.59 and 0.60 and an RPD of 1.49 and 1.50, respectively. The SAS covered 50% of the study area during the wet period, of which 14 and 36% were saline and saline-sodic soils, respectively. The extent increased up to 59% during the dry period, including saline soils (12%) and saline-sodic soils (47%). Our findings would facilitate precise, rapid, and cost-effective monitoring of soil salinity and sodicity over large areas.

Landsat 8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China’s Diverse Latitudes

Crop growth and development exhibit high temporal heterogeneity. It is crucial to capture the dynamic characteristics of crop growth using intensive time-series data. However, single satellites are limited by revisit cycles and weather conditions to provide dense time-series data for earth observations. However, up until now, there has been no proposed remote sensing fusion product that offers high spatial-temporal resolution specifically for farmland monitoring. Therefore, focusing on the demands of farmland remote sensing monitoring, identifying quantitative conversion relationships between multiple sensors, and providing high spatial-temporal resolution products is the first step that needs to be addressed. In this study, a fused Landsat 8 (L8) Operational Land Imager (OLI) and Sentinel-2 (S-2) multi-spectral instruments (MSI) data product for regional monitoring of farmland at high, mid, and low latitudes in China is proposed. Two image pairs for each study area covering different years were acquired from simultaneous transits of L8 OLI and S-2 MSI sensors. Then, the isolation forest (iForest) algorithm was employed to remove the anomalous pixels of image pairs and eliminate the influence of anomalous data on the conversion relationships. Subsequently, the adjustment coefficients for multi-source sensors at mixed latitudes with high spatial resolution were obtained using an ordinary least squares regression method. Finally, the L8-S-2 fused dataset based on the adjustment coefficients is proposed, which is suitable for different latitude farming areas in China. The results showed that the iForest algorithm could effectively improve the correlation between the corresponding spectral bands of the two sensors at a spatial resolution of 10 m. After the removal of anomalous pixels, excellent correlation and consistency were obtained in three study areas, and the Pearson correlation coefficients between the corresponding spectral bands almost all exceeded 0.88. Furthermore, we mixed the six image pairs of the three latitudes to obtain the adjustment coefficients derived for integrated L8 and S-2 data with high-spatial-resolution. The significance and accuracy quantification of the adjustment coefficients were thoroughly examined from three dimensions: qualitative and quantitative analyses, and spatial heterogeneity assessment. The obtained results were highly satisfactory, affirming the validity and precision of the adjustment coefficients. Finally, we applied the adjustment coefficients to crop monitoring in three latitudes. The normalized difference vegetation index (NDVI) time-series curves drawn by the integrated dataset could accurately describe the cropping system and capture the intensity changes of crop growth within the high, middle, and low latitudes of China. This study provides valuable insights into enhancing the application of multi-source remote sensing satellite data for long-term, continuous quantitative inversion of surface parameters and is of great significance for crop remote sensing monitoring.

Assessments of the impacts of environmental factors on vegetation cover at gas flaring sites in the Niger Delta, Nigeria

This study aims at the assessment of the effects of environmental factors on vegetation cover at the flaring sites in Rivers State, Nigeria. Twenty one Landsat 7 Enhanced Thematic Mapper Plus (ETM) data, and four Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) data dated from 21/04/2000 to 05/02/2022 with 3 cloud cover were employed to study eleven gas flaring sites. Matrix Laboratory MATLAB code was developed for data processing and analysis. Normalized Differential Vegetation Index (NDVI) was computed from the atmospherically corrected multispectral bands (1-4) for Landsat 7, and bands (2-5) for Landsat 8. Change in NDVI was computed as NDVI450-60)m in the N, E, S and W directions which is the difference between NDVI at 450 m and 60 m distance from the flare stack. The available environmental factors (Facility size, flare stack height and time i.e. year, month and day) were applied to the (NDVI450-60m) in the 4 cardinal directions. Pairwise linear and multiple regression statistical analyses were adopted to investigate the relationships between each of the (NDVI450-60)mN, (NDVI450-60)mE, (NDVI450-60)mS and (NDVI450-60)mW and facility size, stack height and time. The results show that only 12 of the variance in (NDVI450-60)mN is explained by the available data. Therefore, it can be concluded that the combination of the facility size, flare stack height and time accounted for only 12 of the results. Further researches using the rate and volume of flared gas data, and the vegetation species is required to achieve better results.

Enhanced Estimate of Chromophoric Dissolved Organic Matter Using Machine Learning Algorithms from Landsat-8 OLI Data in the Pearl River Estuary

Chromophoric Dissolved Organic Matter (CDOM) plays a critical role in the carbon and biogeochemical cycles within aquatic ecosystems. Satellite imagery can be employed to determine aquatic CDOM concentrations, highlighting the need for effective and precise algorithms for this task. In this study, a cruise survey dataset containing CDOM absorption coefficients and water-leaving radiances in the Pearl River estuary (PRE) was utilized to develop machine learning algorithms for CDOM retrieval from Landsat-8 Operational Land Imager (OLI) observations. Based on OLI wavelength bands, five bands and six band-ratios were chosen as input parameters for the machine learning models. Six machine learning models were trained to develop CDOM algorithms, including Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The results indicated that, among the six machine learning models, the XGBoost algorithm performed best, with the highest R2 value of 0.9 and the lowest CDOM root mean square error (RMSE) of 0.37 m−1, outperforming empirical algorithms. The XGBoost algorithm identified B4/B1 as the most critical input parameter, contributing 71%, followed by B3/B2 with a 16% contribution, where B1, B2, B3, and B4 are the wavelength bands of the OLI. These two band-ratios accounted for most of the contributions, suggesting their significant role in CDOM retrieval from Landsat OLI images. By employing the developed XGBoost algorithm, CDOM spatial patterns at six instances were derived from Landsat-8 OLI image reflectance, illustrating CDOM variations in the PRE influenced by various factors. Further analysis revealed that, in the PRE, tides and winds are the primary driving forces behind the spatial and temporal variability of CDOM. At present, the exploration of employing machine learning algorithms to infer CDOM concentrations in this region remains relatively limited; therefore, with a higher R2 value, the machine learning model we established unveils fresh and novel results.

Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data

Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 109 m3 from July 2002 to December 2022, with a growth rate of 5.3766 × 108 m3/a. The growth rate from January 2005 to January 2015 is 4.4850 × 108 m3/a, and from January 2015 to December 2022, the growth rate is 8.9206 × 108 m3/a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years.

Estimating Effects of Natural and Anthropogenic Activities on Trophic Level of Inland Water: Analysis of Poyang Lake Basin, China, with Landsat-8 Observations

The intensification of anthropogenic activities has led to the infiltration of enormous quantities of pollutants into rivers and lakes, resulting in significant deterioration in water quality and a more prominent occurrence of eutrophication. Poyang Lake, the largest freshwater lake in China, is facing a severe challenge related to eutrophication, which seriously threatens the delivery of the ecosystem service and the safety of drinking water. To address this challenge, Landsat-8 Operational Land Imager (OLI) data for the Poyang Lake Basin (PLB) from May 2013 to December 2020 were used. Since inland water bodies with complex optical characteristics, we developed a semi-analytical algorithm to assess the trophic state of the water based on two cruise field measurements in 2016 and 2019. Combining the semi-analytical trophic level index (TLI) with an atmospheric correction model is the most suitable model for OLI images of the PLB, this model was then applied to Landsat-8 time series observations. The trends of the trophic state of water bodies in PLB were revealed, and the annual, quarterly and monthly percentages of eutrophic water bodies were calculated. Natural and anthropogenic factors were then used to explain the changes in the trophic state of the PLB waters. The main findings are as follows: (1) From the 8-year observation results, it can be seen that the variation of trophic level of water in PLB showed obviously spatial and temporal variations, characterized by higher in the north than in the south and higher in winter than in summer. (2) Temperature promoted the growth of harmful algae and plays an essential role in affecting changes in the trophic level of the water. (3) Changes in the trophic level of water bodies in PLB were mainly affected by human activities. The results of spatial and temporal variation of the trophic level of water and the driving factors in PLB can extend our knowledge of water quality degradation and provide essential references for relevant policy-making institutions.