Landsat Sensors Research Articles

Variability in land surface temperature concerning escalating urban development using thermal data of Landsat sensor: A case study of Lower Kharun Catchment, Chhattisgarh, India

Over the past few years, there has been a revitalized emphasis on comprehending the shifts in land cover and their implications for a range of environmental factors. This investigation seeks to analyze how changes in land surface temperatures (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and alterations in land cover intersect within the lower Kharun catchment area. The primary dataset utilized in this study is for 2001 and 2021 Landsat 7 and 8, part of the Landsat program managed by the United States Geological Survey (USGS), offer essential Earth observation data using their multispectral and thermal sensors which are designed to detect thermal radiation emitted from the Earth's surface. When these bands are properly processed, they enable accurate temperature measurements. Visual interpretation was conducted on these images, categorizing them into five specific classes of land cover these were vegetation, open land, settlement, waterbodies, and cultivation. Following this, spectral indices like NDVI and NDBI were calculated, and LST was derived using a single-channel algorithm. Subsequently, correlation analysis was utilized to explore the interconnectedness or mutual relationship among the spatial distribution of these parameters. Over the period from 2001 to 2021, the most significant changes in land use were observed in the settlement area and cultivation, which increased by 6.92 and 6.23 sq. km, respectively. Conversely, open land, vegetation, and waterbodies experienced decreases of 7.13, 5.56, and 0.46 sq. km, respectively. The patterns in which LST, NDBI, and NDVI are distributed, exhibited corresponding variations following changes in land cover. The observed alterations in LST, NDBI, and NDVI are believed to be primarily influenced by the expansion of built-up areas. A noticeable association suggests that as built-up areas increase, both NDBI and LST values typically rise.Furthermore, a correlation observed between LST with NDVI was negative, suggesting an inverse relationship between these parameters. On the other hand, the correlation of LST with NDBI observed was positive, indicating that these parameters exhibit a direct relationship. Overall, these findings seem to be complex and highlight the interactions between changing land cover and environmental parameters, underscoring the importance of understanding these relationships for effective land management and environmental monitoring.

Spatiotemporal lakes surface area changes over 35 years and potential causes in the Central Rift Valley, Ethiopia

Study RegionCentral Rift Valley (CRV) region, Ethiopia. Study FocusThe study aims to investigate the spatiotemporal change in lake water surface extents over 35 years (1986–2020) and identify potential causes. Three alternative multispectral water indices—Normalized Difference Water Index (NDWI), Automatic Water Extraction Index (AWEI), and Modified Normalized Difference Water Index (MNDWI)—derived from Landsat sensors (TM, ETM+, OLI) were utilized to extract and analyze the lake surface area. The Mann-Kendall correlation was applied to examine whether hydro-meteorological and anthropogenic variables are possible causes for changes. New Hydrological Insights for the RegionMNDWI outperforms other indices with over 96 % accuracy in mapping lake surface area. Lake Abiyata experienced the most significant reduction in surface area from 1986 to 2020 (>50 %), followed by Lake Ziway (<2 %) while Lake Langano remained relatively stable. This significant reduction positively correlates to alterations in lake water depth (R2=0.92). Factors such as increased temperature, evaporation, and water abstraction for soda ash production negatively correlated with lake surface area, while inflow from Bulbula River and mean annual rainfall showed positive correlations. Abiyata Lake's decrease in surface area is primarily attributed to water abstraction for soda-ash manufacturing and incoming river flow. If the current trends in water abstraction from the Lake and income water diversion continue, there is a high possibility that Lake Abiyata will change into a terminal lake shortly.

PATTERN OF SPRAWL DEVELOPMENT ALONG THE ABUJA – KEFFI HIGHWAY CORRIDOR IN THE NORTH CENTRAL NIGERIA

The rapid expansion of urban areas worldwide is widely attributed to the growth of urban populations. The objective of this study is to examine the expansion of urban sprawl in the North Central area of Nigeria, specifically along the Abuja-Keffi highway corridor, over 29 years, from 1991 to 2020. The study utilized satellite imagery from three distinct spatiotemporal intervals (1991–2003, 2003–2015, and 2015–2020) and incorporated three different types of Landsat sensors, namely Enhanced Thematic Mapper (ETM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI). The study employed image processing and land use classification analysis techniques to generate land use and land cover (LULC). The analysis of the data reveals that there has been a significant increase in sprawling development along the Abuja-Keffi highway corridor. The built-up area of the region has undergone significant expansion over the past three decades, with the urbanized land area increasing from 1081.98 hectares (4.96%) in 1991 to 10263.15 hectares in 2020, accounting for 47.06% of the total built-up area. The research also documented two distinct forms of urban growth, specifically linear and leapfrog, throughout the corridor. The research recommends regional urban policy, urban growth boundaries as well as effective and efficient development control mechanisms in the selected settlements along the corridor. The Abuja Municipal Area Council and Karu Local Government are urged to promote urban renewal and infrastructure development through private partnerships, sustainable policies, waste management systems, and community engagement.

Geoecological analysis of the state of forest park plantations in the Dnipro city using Landsat satellite images

&#x0D; &#x0D; &#x0D; The importance of the number and viability of green areas for creating a com- fortable and safe life for residents of urbanized areas is increasing with the growth of cities, which has not stopped since the first half of the 20th century. Dnipro is a city with progressive air pollution, therefore the effective use of its green areas in this aspect – the selection of tree species, regular assessment of their condition and timely response to changes in vegetative activity – becomes especially relevant. To solve the research task, a combination of on-site and remote methods of assessing the vital state of green areas of the city was chosen. First, spatial and statistical information about the city’s green areas was defined and summarized, and a cartographic and statistical base of raw data was created, potential anthropogenic factors of influence were determined, and reference areas were selected. At the second stage, a systematic survey and assessment of the vital state of the trees of selected areas was carried out for seven years: morphometric indicators of trees of certain species and assessment of the state of their morphological elements (roots, trunk, crown, leaves). According to the degree of damage, trees were classified as: healthy, weakened (damaged), severely weakened; dying and dead. At the third stage of the study, the correlation of the vital state indicator (Ln) and the normalized vegetation index (NDVI) was determined, based on images from Landsat sensors 8 and 9 of the L1 processing level, pre-processing of the images was performed using the SCP (Semi-automatic Classification Plugin) module integrated in QGIS. According to the results of on-site research, it was found that for the period 2015-2021, most parks and squares of the control group belong to the second category (weakened), three to the first (healthy) and one to the third (severely weakened). The main types of damage have been identified as well as species of trees that are most susceptible to damage. The dynamics of vegetation activity indicators reflect the general tendency to increase the vegetation cover of the city for six years. According to the results of the correlation analysis of NDVI and Ln values, a stable dependence between the index of vital state and the normalized vegetation index was revealed. The correlation between the specified indicators for each particular year is direct and varies from 0.5 to 0.9. The vital state of forest park plantations in Dnipro city is at a satisfactory level. The average Ln for the entire city is 65.6%. It was concluded that the exploitation of the gas-absorbing capacity of plants must be carried out in combination with technical measures in order to activate the processes of improving the environment.&#x0D; &#x0D; &#x0D;

A comparative spectral assessment approach of SEBAL and SEBS for actual evaporation estimation in Ardabil Province

Evapotranspiration is a crucial process in the Earth's water and climate cycle, responsible for transforming water from liquid to water vapor. This transformation plays a vital role in the global water cycle and has a significant impact on the climate, weather patterns, and precipitation in various regions. In this study, actual evapotranspiration in the Mughan plain of Ardabil province has been estimated using spectral data from the OLI and TIRS sensors of the Landsat 8 satellite. The SEBAL (Surface Energy Balance Algorithm for Land) and SEBS (Surface Energy Balance System) methods have been employed to calculate these phenomena. SEBAL is an energy balance algorithm designed for land surfaces, utilizing satellite data to estimate actual evapotranspiration accurately. Similarly, SEBS is a surface energy balance system that provides a more precise estimation of evapotranspiration and transpiration rates. Data from the OLI and TIRS sensors of Landsat 8 were collected from the study area for this research. By applying the SEBAL and SEBS methods to these data, actual evapotranspiration values in the Mughan plain were obtained. The results indicated that SEBAL showed a broader range of actual evapotranspiration values (0.74 to 5.8 mm) compared to SEBS (1.25 to 8.85 mm), highlighting its ability to distinguish different regions with varying evapotranspiration rates. However, SEBAL's implementation is more computationally demanding than SEBS, making the calculation process time-consuming. Overall, both algorithms demonstrated relatively high capabilities in calculating instantaneous evapotranspiration using spectral data. The findings of this study can be valuable for water resources and agricultural management in the research area, as well as for water resource planning and environmental studies.

Towards global long-term water transparency products from the Landsat archive

Secchi Disk Depth (Zsd) is one of the most fundamental and widely used water-quality indicators quantifiable via optical remote sensing. Despite decades of research, development, and demonstrations, currently, there is no operational model that enables the retrieval of Zsd from the rich archive of Landsat, the long-standing civilian Earth-observation program (1972 – present). Devising a robust Zsd model requires a comprehensive in situ dataset for testing and validation, enabling consistent mapping across optically varying global aquatic ecosystems. This study utilizes Mixture Density Networks (MDNs) trained with a large in situ dataset (N = 5689) from 300+ water bodies to formulate and implement a global Zsd algorithm for Landsat sensors, including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) aboard Landsat-5, -7, -8, and -9, respectively. Through an extensive Monte Carlo cross-validation with in situ data, we showed that MDNs improved Zsd retrieval when compared to other commonly used machine-learning (ML) models and recently developed semi-analytical algorithms, achieving a median symmetric accuracy (ε) of ∼29% and median bias (β) of ∼3%). A fully trained MDN model was then applied to atmospherically corrected Landsat data (i.e., remote sensing reflectance; Rrs) to both further validate our MDN-estimated Zsd products using an independent global satellite-to-in situ matchup dataset (N = 3534) and to demonstrate their utility in time-series analyses (1984 – present) via selected lakes and coastal estuaries. The quality of Rrs products rigorously assessed for the Landsat sensors indicated sensor-/band-dependent ε ranging from 8% to 37%. For our Zsd products, we found ε ∼ 39% and β ∼ 8% for the Landsat-8/OLI matchups. We observed higher errors and biases for TM and ETM+, which are explained by uncertainties in Rrs products induced by uncertainties in atmospheric correction and instrument calibration. Once these sources of uncertainty are, to the extent possible, characterized and accounted for, our developed model can then be employed to evaluate long-term trends in water transparency across unprecedented spatiotemporal scales, particularly in poorly studied regions of the world in a consistent manner.

A novel method for identification of disturbance from surface coal mining using all available Landsat data in the GEE platform

Coal mining has contributed to a solid and stable global economy. Surface mining and underground mining are the principal methods for extraction of coal worldwide and given the developments in mining technology and equipment, these methods are increasingly generating more and more coal. Surface mining is also a subject that cannot be overlooked in the context of sustainable development because amongst human activities it causes comparatively strong land disturbance. Much of the damage and assessment of land and ecosystems from surface mining occurs at the site, and at the regional scale; hence access to the mining data is limited. Moreover, it is difficult for remote sensing to extract information on larger-scale areas with high precision. As a consequence, a frequency index of open coal mining can be created by merging the vast high-resolution images acquired by Landsat sensors with the spectral properties of open coal scraped from the overburden layer. Such indices and morphological principles can be used to develop strategies for quickly identifying the open coal boundaries in a vast area without mine-related information. In addition, LandTrendr may be used to reconstruct the spatial and temporal processes of surface disturbance and remediation caused by regional surface coal mining to identify surface mining disturbance and reclamation. Seven typical mining locations worldwide were selected to test and confirm the proposed method's practicality and robustness, including delivering accurate findings based on randomly testing sample points and comparing the mature data outputs. The results showed that: (1) the overall accuracy of the open coal areas identified by the OCFI (Open Coal Frequency Index) was over 85 % (range 0.85–0.96). In all study areas, the accuracy of recognizing disturbance and reclamation events was greater than 76 %. (2) The surface mining disturbed 3046.42 km2 of land and 1487.39 km2 was reclaimed in the seven mining areas investigated between 1986 and 2018, resulting in a reclamation rate of 48.82 %. The proposed method is more accurate and faster in identifying open coal locations than the current commonly used land cover datasets based on comparisons with other data products. The approach can extract surface coal mining in the absence of mining data and offer precise boundary data for mining research, which can be extremely useful for extending mining operations.

Blending of Landsat and Sentinel images using Multi-sensor fusion

Global surveillance based on remote sensing always has a trade-off between spectral, temporal, and spatial resolutions of the sensor data. Since unique land cover information could be estimated at a good spatial scale than the temporal resolution, the Sentinel sensor, for example, has a revisit of 5 days and gathers images at a 10-20m spatial resolution. The Landsat sensor can only be revisited every 16 days with a spatial scale of 30 m. Fine temporal and spatial resolution data are essential for seasonable observation of dynamic agricultural, environmental, or ecological phenomena. This trade-off problem can be solved by using fusion methods based on the linear differences between images with fine (Sentinel) and coarse (Landsat) spatial resolution. As a result, an image with high spatial resolution can be predicted than acquired from a single Landsat-like sensor. The proposed work will combine Landsat and Sentinel data to bridge the gap between the Landsat image (spatial resolution,30m) and the Sentinel image (temporal resolution,10m or 20m). As a result, detailed investigations of the Earth’s surface and dynamics will be possible. Evaluation metrics – RMSE, SRE, and SSIM are used to compare the predicted image with the Sentinel image of the predicted date. The metrics analysis found that the predicted image is like the reference image with optimal values for all three-evaluation metrics.

A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level

Information on crop phenology is essential when aiming to better understand the impacts of climate and climate change, management practices, and environmental conditions on agricultural production. Today's novel optical and radar satellite data with increasing spatial and temporal resolution provide great opportunities to derive such information. However, so far, we largely lack methods that leverage this data to provide detailed information on crop phenology at the field level. We here propose a method based on dense time series from Sentinel-1, Sentinel-2, and Landsat 8 to detect the start of seven phenological stages of winter wheat from seeding to harvest. We built different feature sets from these input data and compared their performance for training a one-dimensional temporal U-Net. The model was evaluated using a comprehensive reference data set from a national phenology network covering 16,000 field observations from 2017 to 2020 for winter wheat in Germany and compared against a baseline set by a Random Forest model.Our results show that optical and radar data are differently well suited for the detection of the different stages due to their unique characteristics in signal processing. The combination of both data types showed the best results with 50.1% to 65.5% of phenological stages being predicted with an absolute error of less than six days. Especially late stages can be predicted well with, e.g., a coefficient of determination (R2) between 0.51 and 0.62 for harvest, while earlier stages like stem elongation remain a challenge (R2 between 0.06 and 0.28). Moreover, our results indicate that meteorological data have comparatively low explanatory potential for fine-scale phenological developments of winter wheat.Overall, our results demonstrate the potential of dense satellite image time series from Sentinel and Landsat sensor constellations in combination with the versatility of deep learning models for determining phenological timing.

Greening rate in North Korea doubles South Korea

South and North Korea have experienced contrasting economic developments since the 1950s while sharing similar climates. Previous studies revealed an overall long-term greening trend across the Korean Peninsula based on greenness data from coarse-resolution satellite images. However, there has been no comprehensive comparison of the greenness patterns and the driving mechanisms between two countries due to the limitations of coarse-resolution satellite data. Here, we performed cross-calibration among Landsat sensors and adopted a phenology-based approach to generate Landsat annual maximum Normalized Difference Vegetation Index (NDVImax) time series for each pixel from 1986 to 2017. We found that over 1986–2017, the greening rate in North Korea was almost twice that of South Korea. Cropland in South Korea is the main source of the greening discrepancy. The expansion of agricultural facilities in the stable cropland area and urbanization in the cropland loss area of South Korea contributed 57% to the significant negative NDVImax trend, which was dominant over the forest NDVImax increase resulting from rising temperatures, CO2 fertilization effects and afforestation projects. However, in North Korea, CO2 fertilization effects in the stable cropland area and transition from grassland to cropland promoted an increase in NDVImax, despite decreasing NDVImax in forest areas due to deforestation. Our results highlight the need for delineating fine-scale land-use changes to advance our understanding of regional vegetation dynamics.

Remote estimates of suspended particulate matter in global lakes using machine learning models

Suspended particulate matter (SPM) in lakes exerts strong impact on light propagation, aquatic ecosystem productivity, which co-varies with nutrients, heavy metal and micro-pollutant in waters. In lakes, SPM exerts strong absorption and backscattering, ultimately affects water leaving signals that can be detected by satellite sensors. Simple regression models based on specific band or hand ratios have been widely used for SPM estimate in the past with moderate accuracy. There are still rooms for model accuracy improvements, and machine learning models may solve the non-linear relationships between spectral variable and SPM in waters. We assembled more than 16,400 in situ measured SPM in lakes from six continents (excluding the Antarctica continent), of which 9640 samples were matched with Landsat overpasses within ±7 days. Seven machine learning algorithms and two simple regression methods (linear and partial least squares models) were used to estimate SPM in lakes and the performance were compared. To overcome the problem of imbalance datasets in regression, a Synthetic Minority Over-Sampling technique for regression with Gaussian Noise (SMOGN) was adopted in this study. Through comparison, we found that gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) models demonstrated good spatiotemporal transferability with SMOGN processed dataset, and has potential to map SPM at different year with good quality of Landsat land surface reflectance images. In all the tested modeling approaches, the GBDT model has accurate calibration (n = 6428, R2 = 0.95, MAPE = 29.8%) from SPM collected in 2235 lakes across the world, and the validation (n = 3214, R2 = 0.84, MAPE = 38.8%) also exhibited stable performance. Further, the good performances were also exhibited by RF model with calibration (R2 = 0.93) and validation (R2 = 0.86, MAPE = 24.2%) datasets. We applied GBDT and RF models to map SPM of typical lakes, and satisfactory result was obtained. In addition, the GBDT model was evaluated by historical SPM measurements coincident with different Landsat sensors (L5-TM, L7-ETM+, and L8-OLI), thus the model has the potential to map SPM of lakes for monitoring temporal variations, and tracks lake water SPM dynamics in approximately the past four decades (1984–2021) since Landsat-5/TM was launched in 1984.

‘LandsatTS': an R package to facilitate retrieval, cleaning, cross‐calibration, and phenological modeling of Landsat time series data

The Landsat satellites provide decades of near‐global surface reflectance measurements that are increasingly used to assess interannual changes in terrestrial ecosystem function. These assessments often rely on spectral indices related to vegetation greenness and productivity (e.g. Normalized Difference Vegetation Index, NDVI). Nevertheless, multiple factors impede multi‐decadal assessments of spectral indices using Landsat satellite data, including ease of data access and cleaning, as well as lingering issues with cross‐sensor calibration and challenges with irregular timing of cloud‐free acquisitions. To help address these problems, we developed the ‘LandsatTS' package for R. This software package facilitates sample‐based time series analysis of surface reflectance and spectral indices derived from Landsat sensors. The package includes functions that enable the extraction of the full Landsat 5, 7, and 8 records from Collection 2 for point sample locations or small study regions using Google Earth Engine accessed directly from R. Moreover, the package includes functions for 1) rigorous data cleaning, 2) cross‐sensor calibration, 3) phenological modeling, and 4) time series analysis. For an example application, we show how ‘LandsatTS' can be used to assess changes in annual maximum vegetation greenness from 2000 to 2022 across the Noatak National Preserve in northern Alaska, USA. Overall, this software provides a suite of functions to enable broader use of Landsat satellite data for assessing and monitoring terrestrial ecosystem function during recent decades across local to global geographic extents.

Determining fire frequency and its relationship with rangeland aboveground grass biomass using MODIS and Landsat imagery

ABSTRACT High frequency burned area (BA) images provide an opportunity to monitor rangeland fire frequency (FF). This study related grass aboveground biomass (AGB) to FF in a 4800 ha biodiversity conservation savannah-grassland rangeland. Archived (2000–2021), 500 m resolution MODIS burned area monthly images were used. Following Boolean coding (fire event pixel = 1, no-fire pixel = 0), addition GIS overlay analysis yielded total fires per pixel location. Fire detection accuracy was assessed using 2018–2021 management fire event records. Sample grass AGB data were obtained at the end of the 2020 and 2021 growing seasons from widely dispersed sampling sites with wide grass cover uniformity, where a 1 m quadrat was tossed randomly in a 20 m × 20 m plot up to three times. The quadrat-enclosed grass was harvested to soil level, air-dried, and weighed to generate site average AGB values, which were correlated with vegetation index (VI) values from sampling near-concurrent surface reflectance (L2SP) Landsat-8 OLI images. Four biomass-sensitive VIs utilising Landsat sensor spectral ranges were tested. The Enhanced Vegetation Index (EVI) yielded the strongest relationship (r = 0.410, p < 0.01). A linear model predicting grass AGB from EVI values was developed using 58% of sample data for training (R 2 = 0.3062, p < 0.01) and 42% for validation (R 2 = 0.5225, p < 0.001). Using the model, sampling site historical grass AGB values were predicted on same season, L2SP Landsat (TM, ETM+, OLI) images from 2000, 2002, 2006, 2009, 2013 and 2016, whose dates were selected by comparing rainfall. The MODIS images detected 73% of fires larger than 25 ha (one pixel). Most sites experienced long-term AGB gains, at faster rates in high FF (4–5 fires), low grazing sites. Most fires occurred as ecologically undesired late burns, indicating the utility of archived high-frequency BA images for rangeland management.

Harvesting the Landsat archive for land cover land use classification using deep neural networks: Comparison with traditional classifiers and multi-sensor benefits

The Landsat archive, with a multi-decadal global coverage is a prime candidate for deep learning classification methods due to the large data volume. Local studies have evaluated deep learning methods on Landsat observations. However, these models often saturate at high accuracies due to limited reference dataset size thus do not fully explore the potential of deep classifiers. Furthermore, no provisions are taken to investigate algorithmic performance of challenging classification areas. To address these shortcomings in this research, Landsat 5, 7 and 8 observations were combined within the continental United States to create one of the largest to date reference dataset containing about 21 million labeled annual temporal sequences. Difficult to classify reference samples were isolated by examining labelsin the immediate vicinity. Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) deep learners were integrated to capture temporal and spatial relationships, respectively. Classification mapping accuracy was contrasted with a commonly implemented large-scale mapping method, the Random Forest (RF).Results indicate substantial classification improvements of deep learning methods (DLMs) over the RF. These improvements are more pronounced on challenging to classify pixels in heterogenous areas. RF classification accuracy reaches about 70% on average, while DLMs are at 86%-95% range, depending on model architecture. Grass and bare land classes show the highest accuracy improvements, from 65.5% and 63.5%, respectively for the RF to the 79.4%-96.3% range for the DLMs. Our work also examined the practical value of having two, instead of one, Landsat sensors. Results indicate substantial classification increases (7%-10% in average F1 accuracy) suggesting that having two concurrent Landsat sensors is important not only for redundancy but also for improved mapping capabilities.

Method of Validating Satellite Surface Reflectance Product Using Empirical Line Method

Atmospherically corrected surface reflectance (SR) products are used for reliable monitoring of land surfaces and are the standard products of Landsat sensors. Due to increased demand for SR products, a need exists to verify that the L2C2 (Level-2 Collection-2) SR products are precise and accurate. The Level-2 Collection 2 (L2C2) SR Product has processed satellite imagery data that correct for atmospheric effects such as absorption and scattering, providing a more accurate representation of Earth’s surface. The validation of SR products using ground truth measurement is essential. This study aims to develop and evaluate a validation methodology for satellite SR products. Thus, the Empirical Line Method (ELM) is used here for atmospheric validation of remotely sensed data. Validation is performed using the SR derived from ELM tied to ground truth measurement. Absolute surface reflectance models of Algodones Dunes and the Salton Sea located in North America Sonoran Desert are developed to extend the temporally limited ground truth measurements. This model can give ground truth reflectance in any time frame independent of time constraints. The result of the absolute surface reflectance model of Algodones Dunes indicates that the model predicts the response of Algodones Dunes with an average accuracy of 0.0041 and precision of 0.0063 and gives ground measurements across all multispectral between 350 and 2500 nm. For the Salton Sea, the model predicts the response of the Salton Sea with mean absolute error (MAE) of 0.0035 and gives ground measurements across all multispectral between 350 and 2500 nm. The ELM generates atmospheric coefficients (gain and bias), which are applied to an image to obtain SR. Validation results indicated that for L9-OLI-2, L8-OLI, and L5-TM-SR products, the RMSE range is 0.0019 to 0.0106, 0.0019 to 0.0148 and 0.0026 to 0.0045 reflectance unit, respectively, and accuracy is within 0.0038, 0.0022, and 0.0055 reflectance unit across all spectral bands of L9, L8, and L5, respectively. On average, the validation result showed a strong linear relation between the L2C2 SR products and ELM SR within 0.5 to 1 reflectance units. These results demonstrate the high accuracy and reliability of the L2C2 SR product, providing valuable information for a wide range of remote sensing applications, including land cover and land use mapping, vegetation monitoring, and climate change studies.

Remote‐Sensing Method for Monitoring Suspended‐Sediment Concentration on the Middle‐Mississippi and Lower‐Missouri Rivers

AbstractSediment transport, erosion, and deposition are primary drivers of river geomorphic processes and ecological services. Suspended‐sediment concentration (SSC) is an important parameter for evaluating these processes and is accordingly of significant interest to engineers, scientists, and water resource managers. The United States Geological Survey (USGS) previously operated nine daily SSC gauging stations along the Mississippi River, with operating dates ranging from 1974 to 2018. Currently, there are no USGS gauging stations reporting daily SSC values along the Mississippi River. For this study, regression models were developed to compute the SSC along the Middle‐Mississippi River (MMR) and Lower‐Missouri River (LMOR) using publicly and freely available Landsat imagery. Surface reflectance data from Landsat satellites were used with USGS‐measured SSC to develop regression models for three different Landsat sensors (Landsat 8 OLI/TIRS, Landsat 7 ETM+, and Landsat 4‐5 TM). Previous models published for predicting SSC in the MMR and LMOR from Landsat images have a linear‐regression form and have provided invalid negative values when extrapolated outside of the dataset used for development. The objectives of this study were to develop reflectance‐SSC regression models using a power‐function form and demonstrate their extrapolation performance using multiple novel applications in the MMR basin. The reflectance‐SSC regression models were applied to the following conditions: 1) mixing at the Mississippi and Missouri River confluence, 2) point‐source pollution, and 3) SSC changes along the entire MMR reach for a range of discharges. The regression models were also used to develop sediment rating curves for the four largest tributaries of the MMR.

Variation of surface water extent in the great Sebkha of Oran (NW of Algeria), using Landsat data 1987–2019: Interaction of natural factors and anthropogenic impacts

The rapid and precise extraction of water information through satellite remote sensing has become a crucial technology in the study of water resources due to its ability to provide substantial and repeatable coverage instantly. This technology is particularly useful in monitoring wetlands, among other applications. It in this perceptive, this research aims to study the surface water extent (SWE) variation of the Great Sebkha of Oran (GSO). A wetland area classified as Ramsar, bordering Oran city. Extended over nearly 40,000 ha, the water extent is influenced by climatic, geological, and hydrological factors, which are typical of endorheic regions in semi-arid and arid climates. However, over the last decade, these natural effects have been overtaxed by anthropogenic impacts caused by the strong urban expansion of the city of Oran. This is particularly evident with the commissioning of the large El Kerma sewage wastewater treatment plant (WWTPK) in 2009. Located at NE edge of the GSO. The function of this plant is to treat a large proportion of the wastewater volume generated by the city of Oran. Nevertheless, for various reasons, a lag in the implantation of the various execution phases of a development project extended to the entire GSO basin has led to nearly a decade; the WWTPK has discharged partially treated wastewater directly into GSO. This had a major impact on the mode of surface water extent change of the sebkha and led to major pollution of the latter. To better understand the (SWE) dynamics, post and pre-discharges from the WWTPK in the sebkha were monitored monthly over 33 years (1987–2019). A consistent database of Landsat sensor images (TM, ETM+, and OLI) was collected and processed in the Google Earth Engine (GEE) platform to extract the SWE in the GSO, which was achieved using spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI1 and NDWI2), Modified Normalized Difference Water Index (MNDWI) and Dryness/Wetness index (DWI). Subsequently, the SWE, correlated with precipitation over the entire GSO watershed, was analyzed and discussed at different time steps: monthly, annual, and seasonal. Before 2009, the precipitation regime study concerning the SWE shows the complexity of the functional dynamics of the endorheic system of the GSO watershed. After 2009, the commissioning of WWTPK had disrupted the SWE variation of the GSO. The maximum average of the SWE during the 2009–2019 sub-period was 169 km2, while it was 122 km2 during the undisturbed sub-period (1987–2008). The use of remote sensing data analysis contributes effectively to this work, representing proven results to protect this sensitive natural environment. They remain necessary to help decision-makers to ensure and preserve the sustainable ecological viability of the GSO and the natural environment of wetlands of the entire Oran sublittoral furrow of Gdyel-Arzew: Daiet Morcelly, Telamine Lake, Sebkha golf Arzew.

The Hi-GLASS all-wave daily net radiation product: Algorithm and product validation

The surface net radiation (Rn) represents the balance of the radiative budget on the land surface and drives many physical and biological processes. An accurate and long-term product for global daily coverage of Rn at a high spatial resolution is needed for a variety of applications at regional and local scales. This study proposes two algorithms, called the downward shortwave radiation (DSR)-based algorithm and the top-of-atmosphere (TOA)-based algorithm, to estimate Rn by using Landsat data. The DSR-based algorithm consists of three conditional models, and was developed based on the analysis of the relationship between Rn and shortwave radiation as well as ancillary information from ground measurements and various datasets. The TOA-based algorithm was developed by linking Rn to TOA observations from Landsat sensors and ancillary information. The two algorithms were developed by using the random forest method. The results of their validation against ground measurements showed that the DSR-based algorithm outperformed the TOA-based algorithm in terms of accuracy, with a determination coefficient (R2) of 0.93, root-mean-squared error (RMSE) of 17.58 Wm−2, and bias of −4.27 Wm−2. It was stable under various conditions. We then applied the DSR-based algorithm to generate a product of the global daily Rn, called the High-resolution (Hi)- Global LAnd Surface Satellite (GLASS), from 2013 to 2018 at a spatial resolution of 30 m under a clear sky based on remotely sensed products, including the DSR from GLASS, the normalized difference vegetation index (NDVI) obtained from Landsat, surface broadband albedo from Hi-GLASS, and meteorological factors based on reanalysis data from MERRA2. Following its validation using in-situ observations from 2013 to 2018, the overall accuracy of the daily Rn acquired by Hi-GLASS under clear sky was found to be satisfactory, with a value of R2 of 0.90 and an RMSE of 25.03 Wm−2. Moreover, compared with the daily Rn obtained from the GLASS product at a spatial resolution of 5 km, that obtained by Hi-GLASS can better characterize the surface by providing more details and capturing the variations in the measurements, especially large and small values. However, due to limitations of the available datasets and the algorithm, the data on Rn for most regions lacked information on cloudy skies and areas at high latitudes. This information thus cannot be provided by Hi-GLASS yet. Moreover, the influence of the topography on values of Rn has not been thoroughly considered. Nonetheless, values of Rn under clear sky obtained from Hi-GLASS offer promise for use in a wide range of areas, and efforts are underway to improve this product.

Vicarious CAL/VAL Approach for Orbital Hyperspectral Sensors Using Multiple Sites

The hyperspectral (HSR) sensors Earth Surface Mineral Dust Source Investigation (EMIT) of the National Aeronautics and Space Administration (NASA) and Environmental Mapping and Analysis Program (EnMAP) of the German Aerospace Center (DLR) were recently launched. These state-of-the-art sensors have joined the already operational HSR sensors DESIS (DLR), PRISMA (Italian Space Agency), and HISUI (developed by the Japanese Ministry of Economy, Trade, and Industry METI and Japan Aerospace Exploration Agency JAXA). The launching of more HSR sensors is being planned for the near future (e.g., SBG of NASA, and CHIME of the European Space Agency), and the challenge of monitoring and maintaining their calibration accuracy is becoming more relevant. We proposed two test sites: Amiaz Plain (AP) and Makhtesh Ramon (MR) for spectral, radiometric, and geometric calibration/validation (CAL/VAL). The sites are situated in the arid environment of southern Israel and are in the same overpass coverage. Both test sites have already demonstrated favorable results in assessing an HSR sensor’s performance and were chosen to participate in the EMIT and EnMAP validation stage. We first evaluated the feasibility of using AP and MR as CAL/VAL test sites with extensive datasets and sensors, such as the multispectral sensor Landsat (Landsat5 TM and Landsat8 OLI), the airborne HSR sensor AisaFENIX 1K, and the spaceborne HSR sensors DESIS and PRISMA. Field measurements were taken over time. The suggested methodology integrates reflectance and radiometric CAL/VAL test sites into one operational protocol. The method can highlight degradation in the spectral domain early on, help maintain quantitative applications, adjust the sensor’s radiometric calibration during its mission lifetime, and minimize uncertainties of calibration parameters. A PRISMA sensor case study demonstrates the complete operational protocol, i.e., performance evaluation, quality assessment, and cross-calibration between HSR sensors. These CAL/VAL sites are ready to serve as operational sites for other HSR sensors.

EFFICIENCY OF MACHINE LEARNING ALGORITHMS IN SOIL SALINITY DETECTION USING LANDSAT-8 OLI IMAGERY

Abstract. Climate change is one of the biggest problems facing today’s world. Rising temperatures and declining rainfall have had a profound effect on the planet, one of which is the destructive effects of soil salinity. Soil salinity phenomena commonly occur in arid and semi-arid regions. Maharloo Salt Lake, southeast of Shiraz, Iran, with an arid and semi-arid climate, has faced severe droughts in the past and is dealing with the soil salinity problem. One useful way to manage land and soil in such areas is regular monitoring of the soils and lands and keeping abreast of changes to prevent land degradation and erosion. With the advancement of technology, remote sensing techniques to monitor natural factors have become very popular. Landsat sensor images were used in this research, and several environmental indicators were extracted by combining satellite bands. Three machine learning algorithms, RF, GBM, and MLP, were used to evaluate methods for monitoring and mapping saline soils. The models were trained and then tested to compare the accuracy and performance of each model in predicting soil salinity. GBM algorithm showed the best performance with R2 = 0.89 and RMSE = 0.63 for testing the dataset after that RF model with R2 = 0.85 and RMSE = 0.71 and the worst performance was for MLP model with R2 = 0.75 and RMSE = 0.88. The figures mapped from the results of these algorithms for salinity distribution in this region showed that by choosing the appropriate algorithm and suitable in-situ data, it could be possible to estimate soil salinity to an excellent extent by satellite data.