Landsat Bands Research Articles

A fast and robust cirrus removal method for Landsat 8/9 images

High-quality cirrus removal plays a crucial role in remote sensing data analysis. Cirrus parallaxes are commonly observed within the vicinity of cirrus clouds in the visible and near-infrared (VNIR) bands of Landsat 8/9 images. Cirrus parallaxes have a nonnegligible effect on cirrus removal, but the existing methods do not account for the correction of parallaxes. Meanwhile, large-scale image processing involves intensive computation that requires extensive computing time. To address the effect of cirrus parallaxes and the low processing efficiency, we propose a fast and robust cirrus removal (FRCR) method. FRCR has achieved the first realization of the statistics law of cirrus parallax between the VNIR and cirrus bands, thus realizing the cirrus parallax correction. In addition, FRCR introduces an automatic sampling method to obtain the regression samples for practicality. Then, a Compute Unified Device Architecture (CUDA) based Newton method with constraints is introduced to parallelize the computation, to improve the computational performance. Experiment results of various scenarios demonstrate that the FRCR method can achieve high-quality cirrus removal by eliminating cirrus parallaxes, and significantly improving computational performance.

Cross-comparison of Landsat-8 and Landsat-9 data: a three-level approach based on underfly images

ABSTRACT The recently launched Landsat-9 has an important mission of working together with Landsat-8 to reduce the revisit period of Landsat Earth observations to eight days. This requires the data of Landsat-9 to be highly consistent with that of Landsat-8 to avoid bias caused by data inconsistency when the two satellites are simultaneously used. Therefore, this study evaluated the consistency of the surface reflectance (SR) and land surface temperature (LST) data between Landsat-8 and Landsat-9 based on five test sites from different parts of the world using synchronized underfly image pairs of both satellites. Previous cross-comparisons have demonstrated high consistency between the spectral bands of Landsat-8 and Landsat-9, with differences of around 1%. However, it is unclear whether this low deviation will be amplified in subsequent multiband calculations. It is also necessary to determine whether the difference is consistent across different land cover types. Therefore, this study used a three-level cross-comparison approach to specifically examine these concerns. Besides the commonly used band-by-band comparison, which served as the first-level comparison in this study, this approach included a second-level comparison based on the calculations of several indicators and a third-level comparison based on a composite index calculated from the indicators obtained in the second-level comparison. This three-level approach will examine whether the difference found in the first-level per-band comparison would change after the subsequent calculations in the second- and third-level comparisons. The Remote Sensing based Ecological Index (RSEI) was used for this approach because it is a composite index integrating four indicators. The results of this three-level comparison show that the first-level per-band comparison exhibited high consistency between the two satellites’ SR data, with an average absolute percent change (PC) of 1.88% and an average R 2 of 0.957 across six bands in the five test sites. This deviation increased to 2.21% in the third-level composite index-based comparison, with R 2 decreasing to 0.956. This indicates that after complex calculations, the deviation between the bands of the two satellites was amplified to some extent. However, when analyzing specific land cover types, notable differences emerged between the two satellites for the water category, with an average absolute PC ranging from 18% to 35% and an R 2 of lower than 0.6. Additionally, there were also nearly 5% differences for the built-up land category, with an average R 2 value of lower than 0.7. The comparison of LST data between both satellites also reveals that the Landsat-9 LST is on average 0.24°C lower than Landsat-8 LST across the five test areas but can be 0.58°C lower in built-up land-dominated areas and 0.42°C higher in desert environments. Overall, the SR and LST data between Landsat-8 and Landsat-9 are consistent. However, their performance varies depending on different land cover types. Caution is needed particularly for water-related research when utilizing both satellites simultaneously. Significant discrepancies may also arise in the areas characterized by deserts and built-up lands.

Effects of landscape pattern on land surface temperature in Nanchang, China

The composition and configuration of landscapes are critical important to design effective approaches to mitigate urban thermal environment in the urbanization process. In this research, land use maps and land surface temperature (LST) retrieval were derived in Nanchang city of central China based on product datasets and the thermal infrared band of Landsat. The results showed that the thermal environment of Nanchang had become worse over the past two decades, that is, the proportion of area of the extremely low temperature zone (ELTZ) decreased from 4.39 to 0.77% from 2001 to 2020, and that of medium temperature zone (MTZ) reduced by 20%, whereas those of the high temperature zone (HTZ) and the extremely high temperature zone (EHTZ) increased sharply after 2001, and by 2020, the area ratio increased by 11% and 7.16%, respectively. The agricultural land (AL) area decreased from 68.44 to 49.69%, was gradually replaced by construction land (CL). The CL occupied the largest proportion in EHTZ, HTZ and slight high temperature zone (SHTZ); water landscape (WL) and green land (GL) occupied the largest proportion in ELTZ, low temperature zone (LTZ); and AL occupied the largest proportion in SHTZ, MTZ, and slight low temperature zone (SLTZ). Landscape configuration also obviously impacted on LST. The model fitting was well (R = 0.87) between land use area and LST by multiple regression analysis. The significant correlation between LST and six landscape pattern indices of CL (p < 0.01) indicated that the larger percent (PLANT, R = 0.78) and the more concentrate (LPI, R = 0.73) of CL implied the higher LST, while the more fragment (NP, R = − 0.45), dispersed and complex shape (R = − 0.35) were benefit to relieve LST. Contrastively, the larger percent and the more concentrated and complex shape distribution of AL, GL and WL, the lower LST (p < 0.01). In addition, LST had closely correlation with landscape level indices such as aggregation degree (AI, R = 0.44) and diversity (SHDI, R = − 0.60) (p < 0.01).

The Spatially Adaptable Filter for Error Reduction (SAFER) Process: Remote Sensing-Based LANDFIRE Disturbance Mapping Updates

LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data.

Elevation-Wise and Direction-Wise Distribution of Land Surface Temperature in Jeju Island using Landsat 7 Data

In the present research, the Land Surface Temperature (LST) has been calculated from Landsat 7 data of Jeju Island to understand the spatial distribution of temperature on the island which would be strongly related to the ecological characteristics of the island. The dates were selected based on seasons (winter and summer) March 12 and September 19, 2020, respectively. Results calculated from a thermal band of Landsat 7 revealed the temperature variations on the island. Winter temperature varies from -3.7 to 31 oC while the summer season temperature varies from 20.6 to 43.1 oC. The surface temperature was also calculated at elevations of 100m and 500m for both seasons. It was found that on 100 m elevation, in winter the North and South directions have the same temperature, comparatively, in summer the North direction has a 0.97 oC higher temperature than the South. The East direction showed a 0.55 oC higher temperature as compared to the West in winter but in the summer season the case was opposite and the West had a 1.46 oC higher surface temperature than the East. Interestingly at 100 m height in summer, the North, and West has also the same temperature (34.25 oC). In the second case, on 500 m height for all four directions, the LST was compared and results showed that the South has 0.58 and 1.52 oC higher temperatures in winter and summer seasons respectively from the North. On the contrary, the West has 3.33 and 0.5 oC higher temperatures than the East in winter and summer seasons respectively. It revealed that seasons and altitude have a great impact on the LST in different cardinal directions of the island. From the results, it can be assumed that the island's ecological diversity would be strongly related to the geographical distribution of temperature.

Enhancing suspended sediment concentration retrieval by integrating thermal infrared and optical bands of Landsat-8 and machine learning algorithms

ABSTRACT Suspended Sediment Concentration (SSC) variabilities sizably affect the bio-optical conditions in water bodies. Remote sensing plays a key role in spatiotemporal monitoring of the SSC in inland and coastal waters. However, existing studies on remote sensing of SSC focus on optical domains, i.e. visible and near-infrared bands. Here, we assume thermal infrared bands can potentially contribute to retrieving SSC, inspired by sediments’ heat-trapping effect demonstrated in aquatic research. Thus, we propose to incorporate the thermal infrared bands of Landsat-8 along with the optical bands for the estimation of SSC using machine learning models. In this study, 85 Landsat-8 images collected from the Google Earth Engine (GEE), coupled with in-situ observations from the United States Geological Survey (USGS) gauging stations, were employed to estimate SSC variabilities in Missouri-Mississippi Rivers. The relationship between in-situ SSC and water temperature revealed a promising correlation (R = 0.7), motivating our assumption of incorporating thermal infrared bands. Therefore, we employed both optical and thermal infrared bands of Landsat-8 imagery as predictors for the first time to estimate SSC. Three machine learning algorithms of Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) were considered. The results demonstrated that incorporating thermal infrared bands improved the performance of the models (average improvements in R2 for ~ 9.6% and in RMSE for ~ 18.9%) with respect to the use of optical bands only.

Improving the remote sensing process of evapotranspiration in the SEBAL algorithm using meta-heuristic models

Abstract This paper investigated how the meta-heuristic models can be used to facilitate the estimation of evapotranspiration (ET) images. Focusing on estimating daily ET directly from received images of the electromagnetic bands of Landsat 8 satellite utilizing metaheuristic models, authors used daily ET images estimated by the SEBAL algorithm to calibrate and verify these models. The results of this research showed that the ANN model with DC and RMSE of 0.98 and 0.09025 mm/day, respectively, is more accurate compared to the ACO (with DC = 0.65 and RMSE = 1.45 mm/day) and PSO (with DC = 0.23 and RMSE = 1.60 mm/day) models in the verification stage in estimating daily ET images. The ACO model compared to the PSO model is more accurate in estimating ET images with DC of 0.65 and 0.23 in the verification step, respectively. While removing half of the training data, the accuracy of the PSO model surpasses the ACO model with DC of 0.85 and 0.80, respectively. Also, the ANN model is more accurate than the other two models in estimating ET, both when considering all the data and half of the training data (with DC = 0.98 and RMSE = 0.09 mm/day).

Utilizing Spectral Indices to Estimate Total Dissolved Solids in Water Body Northwest Arabian Gulf

Remote sensing techniques have made it possible to monitor an important parameter of water quality, total dissolved solids (TDS), more appropriately and regularly. The research aims to assess Landsat 8 OLI images' ability to expose TDS on the sea surface in Iraqi marine waters. six band combinations were employed in the correlation analysis between band values and six dissolved solids samples collected during the fieldwork in three sampling stations to determine the amount of total dissolved solids (TDS): st1, st2, and st3 in December 2014 (26,38,36.9) g.L-1 and mid-January 2022 (27.4,37.9,37) g.L-1, respectively. Furthermore, two images of the Landsat-8 OLI satellite were captured concurrently with the gathering of samples for TDS detection at the measuring stations. After atmospheric correction and the add the remote sensing indicators, the reflectance of water was extracted from satellite images and utilized to express the spectral characteristics of various TDS concentrations, since the values were (22.64, 38, 32.46) g.L-1 in 2014, and (27.5, 36.68, 38) g.L-1 in 2022. Correlation coefficient (R2) values of 0.81, 0.96, and Pearson correlation (r) values of 0.90, 0.97 were found among field measurements and spectral data of TDS indicator 2 (SI-2) derived from green, blue, and red bands of Landsat -8 at 2014, 2022 respectively.

Satellite-Based Identification and Characterization of Extreme Ice Features: Hummocks and Ice Islands

The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) and other EIFs, such as fragments of MYHF and large, newly formed hummock fields. The main objectives for the paper included demonstration of various satellite capabilities over specific regions in the Canadian Arctic to assess their utility to detect and characterize EIFs. Stereo pairs of very-high-resolution (VHR) imagery provided detailed measurements of sea ice topography and were used as validation information for evaluation of the applied techniques. Single-pass interferometric SAR (InSAR) data were used to extract ice topography including hummocks and ice islands. Shape from shading and height from shadow techniques enable us to extract ice topography relying on a single image. A new method for identification of EIFs in sea ice based on the thermal infrared band of Landsat 8 was introduced. The performance of the methods for ice feature height estimation was evaluated by comparing with a stereo or InSAR digital elevation models (DEMs). Full polarimetric RADARSAT-2 data were demonstrated to be useful for identification of ice islands.

Validity of the Landsat surface reflectance archive for aquatic science: Implications for cloud‐based analysis

AbstractOriginally developed for terrestrial science and applications, the US Geological Survey Landsat surface reflectance (SR) archive spanning ~ 40 yr of observations has been increasingly utilized in large‐scale water‐quality studies. These products, however, have not been rigorously validated using in situ measured reflectance. This letter quantifies and demonstrates the quality of the SR products by harnessing a sizeable global dataset (N = 1100). We found that the Landsat 8/9 SR in the green and red bands marginally meet the targeted accuracy requirements (30%), whereas the uncertainties in the blue and coastal‐aerosol bands ranged from 48% to 110%. We further observed &gt; +25% biases in the visible bands of Landsat 5/7 SR, which can introduce an apparent downward trend when applied in time‐series analyses combined with Landsat 8/9. Users must exercise caution when using this archive for trend analyses, and progress in atmospheric correction is required to foster advanced applications of the Landsat archive for aquatic science.

Estimating the Chlorophyll-a in the Nha Trang Bay using Landsat-8 OLI data

The pigment chlorophyll-a (Chl-a) is used to evaluate aquatic ecological health. Using remote sensing techniques to estimate this pigment and spatially mapping its distribution becomes essential for measuring and assessing water quality in coastal areas. Nha Trang Bay is famous not only for its scenery but also for its biodiversity values, especially the existence of coral reefs. In this study, Landsat-8 OLI was taken on June 3, 2015, and field measurements of Chl-a at 13 survey sites from June 6-8, 2015, were used to build a local algorithm to monitor the spatio-temporal distribution of Chl-a content in Nha Trang Bay. The ACOLITE processor was employed for the atmospheric modification of Landsat-8 OLI images to obtain atmospherically corrected surface reflectance products. Four types of simple regression models, including linear, exponential, logarithmic, and power models, were used to describe the relationship between the in-situ measurement of Chl-a and remote sensing reflectance of Landsat-8 OLI data. The results of correlation analysis have shown a statistically significant relationship between field measurement of Chl-a concentration and the remote sensing reflectance ratio of Landsat-8 band 3 versus band 2 using an exponential model with a coefficient of determination of 0.88 (p < 0.001) and root mean square error (RMSE) of 0.40 mg/m3. This empirical relationship was applied to map the spatial distribution of Chl-a concentration from 27 cloudless Landsat-8 OLI images from 2013 to 2021. The spatio-temporal distribution of Chl-a indicated that the Chl-a concentration in the Bay has a low value (less than 1 mg/m3). However, this concentration becomes high in the coastal areas (greater than 1 mg/m3) and the Cai and Tac rivers (greater than 2 mg/m3). It is also noted that the content of Chl-a in the rainy season is fairly higher than in the dry season, by an average of 0.68 mg/m3 and 0.53 mg/m3, respectively. This research highlights that Landsat-8 OLI data can be an effective and valuable tool in monitoring Chl-a for coastal areas.

Quantifying biomass and canopy cover of submerged aquatic vegetation on an offshore island

The coverage and biomass of submerged aquatic vegetation (SAV) are vital characteristics of marine resource management and development. However, using in situ measurements to assess the distribution and biomass of SAV is challenging because SAV distribution varies with biomass, substrate type, and depth, making it difficult to monitor. Satellite remote sensing is an advanced tool that can tackle this problem. In this study, several techniques, such as atmospheric correction, water column correction, and supervised classification, were integrated with Landsat-8 images to map SAV distribution on an offshore island in Viet Nam, Ly Son island. The substrate-classifying result had high accuracy (OA = 92.6%, Ҡ = 0.9 ). The coastal, blue, and green bands of Landsat-8 were used to estimate the SAV biomass using a polynomial regression (RMSE =±1440.6 g fresh m−2, R2=0.71). The results showed that the total fresh biomass of SAV in Ly Son island is 14551.04 tonnes, with a corresponding area of 332.1 hectares, equivalent to an average biomass of 4381.5 ± 1440.6 g fresh m−2. The distribution and biomass maps of SAV produced in this study could be used as input to measure the attributes of SAV biodiversity, contributing more effectively to monitoring marine ecosystems in Viet Nam.

Assessment of image ratio technique: Targeting structural features and mineralization characteristics in the southwestern part of the Sokoto Basin in Nigeria using Landsat 8 imagery

Landsat 8 used multispectral images to map surface structures and validate the existence of suspected mineral alteration zones in the southwestern part of the Sokoto Basin in Nigeria. The area falls within contrasting sedimentary, metasediment, metamorphic, and older granitoid lithologies. The processing and analysis of the Landsat 8 imagery involving false color composite (FCC), band ratio, and principal component analysis (PCA) were implemented in the visible near-infrared and short-wave infrared bands of Landsat 8. PCA highlighted areas with high concentrations of clay minerals and iron oxides using RGB composite bands 7, 5, and 3. Moreover, PCA enhanced iron oxide, hydroxyl-bearing minerals, and likely prospective zones mainly constrained in amphibolite, phyllite, quartzite, and alluvial deposits that have undergone metamorphism and metasedimentation within Yegiwa and Kurege. It identified iron ore and clay mineral alteration, suggesting the occurrence of manganese, goethite, and carbonate minerals around Maburo and Derrena. The observed alteration zones and their associated quartz veins are usually consistent with the main NE–SW provincial structural trend dipping to the northwest zone. However, field verification validates rock units typically consisting of muscovite, hornblende, manganite, tourmaline, quartz vein, and granitic rocks. Accordingly, remote-sensing and field verification served as a guide for future subsurface structural investigation and in-depth geophysical exploration.

Predicting Water Quality Distribution of Lakes through Linking Remote Sensing–Based Monitoring and Machine Learning Simulation

The present study links monitoring and simulation models to predict water quality distribution in lakes using an optimized neural network and remote sensing data processing. Two data driven models were developed. First, a monitoring model was established that is able to convert spectral images to TDS distribution. Moreover, a simulation model was developed to generate a TDS distribution map for unseen scenarios for which no spectral images are available. Outputs of the monitoring model were applied as the observations for training the simulation model. The Nash–Sutcliffe model efficiency coefficient (NSE) was utilized in the system performance measurement of the models. Based on the results in the case study, the monitoring model was sufficiently robust to convert the operational land imager spectral bands of Landsat 8 to the TDS distribution map. The NSE was more than 0.6 for the monitoring model, which confirms the predictive skills of the model. Furthermore, the simulation model was highly reliable in generating the TDS distribution map of the lakes. Three tests were carried out to demonstrate the reliability of the model. When comparing the results of the monitoring model and simulation model, an NSE of more than 0.6 was found for all the tests. It is recommendable to apply the proposed method instead of conventional hydrodynamic models that might be highly time consuming for simulating water quality parameters distribution in lakes. Low computational complexity is the main advantage of the proposed method.

Introducing a map of soil base cation concentration, an ecologically relevant GIS-layer for Amazonian forests

Soil maps are crucial for habitat and species distribution modeling under present and future conditions, thereby providing relevant background information for conservation planning. In Amazonia, soil conditions are highly heterogeneous, which has important implications for the distribution and dynamics of the area's exceptional biodiversity. Unfortunately, available soil maps for this region suffer from inaccuracies and lack of ecologically relevant variables. Here, we develop a map of the sum of exchangeable base cation concentration (SB) in the surface soil by applying machine learning to a comprehensive set of over 10,000 field data points of SB values directly measured from soil samples or inferred using indicator plant species occurrences. As predictors, we used rasters of soil type probabilities, elevation, biomass and reflectance values from Landsat satellite images. Random Forest (RF) models were trained and tested using two different cross-validation strategies. We also assessed in which areas the map was more reliable using the area of applicability approach and compared the results with two other soil layers. The best predictors of SB variation were Landsat bands 7, 4 and 3, elevation, and probability of Histosols. The regional patterns observed across Amazonia were consistent with current geological understanding; lower SB values tended to occur in central Amazonian soils and higher values in western Amazonian soils, with considerable variation within each region. The model was found applicable over most of the Amazonian biome, especially in non-inundated (terra-firme) forest, but not over coastal areas, floodplains of major rivers and wetlands, which were poorly represented in the training data. Our new SB map over performed previous SB map and represent an accurate and ecologically meaningful variable. It is available as a digital GIS layer and can be used in habitat mapping and in modeling the current or future distributions of biological communities and species. This will advance general understanding of Amazonian biogeography and help in conservation planning.

Lake surface temperature retrieved from Landsat satellite series (1984 to 2021) for the North Slave Region

Abstract. Lake surface temperature (LST) is an important attribute that highlights regional weather and climate variability and trends. The spatial resolution and thermal sensors on Landsat platforms provide the capability of monitoring the temporal and spatial distribution of lake surface temperature on small- to medium-sized lakes. In this study, a retrieval algorithm was applied to the thermal bands of Landsat archives to generate a LST dataset (North Slave LST dataset) for 535 lakes in the North Slave Region (NSR) of the Northwest Territories (NWT), Canada, for the period of 1984 to 2021. North Slave LST was retrieved from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS); however, most of the dataset was created from the thermal bands of Landsat 5 (43 %) due to its longevity (1984–2013). Cloud masks were applied to Landsat images to eliminate cloud cover. In addition, a 100 m inward buffer was applied to lakes to prevent pixel mixing with shorelines. To evaluate the algorithm applied, retrieved LST was compared with in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) LST observations. A good agreement was observed between in situ observations and North Slave LST, with a mean bias of 0.12 ∘C and a root mean squared deviation (RMSD) of 1.7 ∘C. The North Slave LST dataset contains more available data for warmer months (May to September; 57.3 %) compared to colder months (October to April). The average number of images per year for each lake across the NSR ranged from 20 to 45. The North Slave LST dataset, available at https://doi.org/10.5683/SP3/J4GMC2 (Attiah et al., 2022), will provide communities, scientists, and stakeholders with spatial and temporal changing temperature trends on lakes for the past 38 years.

Spatial resolution enhancement of the land surface thermal field imagery based on multiple regression models on multispectral data from various space systems

The methodology has been developed for enhancing the spatial resolution of the land surface thermal field satellite imagery based on the following steps: coupling images in the visible, thermal, and radar ranges into the single multispectral data product; constructing regression models of the images’ relationship; performing the linear regression of the pseudo-thermal product with enhanced spatial resolution from the visible and radar ranges data. The methodology is implemented on the Google Earth Engine open cloud platform using the Earth Engine API and the software scripts created in the JavaScript language with the processing of multispectral image collections of various space systems at specified time intervals. The possibility of practical synthesis of the pseudo-thermal image with an enhanced spatial resolution of 10 m based on the thermal image with the resolution of 100 m and the multispectral composite with the layers’ resolution of 10 m and 30 m is shown. The technology has been developed for synthesis and calibration of the land surface temperature product with enhanced spatial resolution and daily data providing rate based on the brightness temperature product in the B10 band of Landsat 8 and linear regression on the MODIS, ASTER, and Sentinel 1 products with daily to moderate data providing rates. The software in JavaScript has been developed, and technology has been implemented in the interactive web service form with open access on the Google Earth Engine Apps cloud platform. The final data product provides the satisfactory relative root mean square error of the brightness temperature recovery of not more than 6 % according to the reference cross-calibration data of the B10 Landsat 8 band in the moderate thermal field (up to 100° C). The relative root mean square errors of the synthesized data according to the reference data on high-temperature sites (fire, hot lava) up to 28 % are due to the fact that the synthesized product contains information from high-temperature spectral bands (B07-B09 from ASTER), while the reference product (B10 from Landsat 8) does not contain such information. Technology implementation examples show that cross-calibration of the synthesized product can be performed during the year from March to October according to reference thermal images of natural or artificial objects. Objects selected for calibration must have stable thermal characteristics at the time of the satellite flight during the data acquisition period. Keywords: land surface temperature, brightness temperature, space resolution of imagery, multiply linear regression, heterogeneous multispectral data coupling, data providing rate, product cross-calibration, Google Earth Engine.

Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan region

Land Surface Temperature (LST) affects exchange of energy between earth surface and atmosphere which is important for studying environmental changes. However, research on the relationship between LST, Land Use Land Cover (LULC), and Normalized Difference Vegetation Index (NDVI) with topographic elements in the lower Himalayan region has not been done. Therefore, the present study explored the relationship between LST and NDVI, and LULC types with topographic elements in the lower Himalayan region of Pakistan. The study area was divided into North-South, West-East, North-West to South-East and North-East to South-East directions using ArcMap 3D analysis. The current study used Landsat 8 (OLI/TIRS) data from May 2021 for LULC and LST analysis in the study area. The LST data was obtained from the thermal band of Landsat 8 (TIRS), while the LULC of the study areas was classified using the Maximum Likelihood Classification (MLC) method utilizing Landsat 8 (OLI) data. TIRS collects data for two narrow spectral bands (B10 and B11) with spectral wavelength of 10.6 μm–12.51 μm in the thermal region formerly covered by one wide spectral band (B6) on Landsat 4–7. With 12-bit data products, TIRS data is available in radiometric, geometric, and terrain-corrected file format. The effect of elevation on LST was assessed using LST and elevation data obtained from the USGS website. The LST across LULC types with sunny and shady slopes was analyzed to assess the influence of slope directions. The relationship of LST with elevation and NDVI was examined using correlation analysis. The results indicated that LST decreased from North-South and South-East, while increasing from North-East and South-West directions. The correlation coefficient between LST and elevation was negative, with an R-value of −0.51. The NDVI findings with elevation showed that NDVI increases with an increase in elevation. Zonal analysis of LST for different LULC types showed that built-up and bare soil had the highest mean LST, which was 35.76 °C and 28.08 °C, respectively, followed by agriculture, vegetation, and water bodies. The mean LST difference between sunny and shady slopes was 1.02 °C. The correlation between NDVI and LST was negative for all LULC types except the water body. This study findings can be used to ensure sustainable urban development and minimize urban heat island effects by providing effective guidelines for urban planners, policymakers, and respective authorities in the Lower Himalayan region. The current thermal remote sensing findings can be used to model energy fluxes and surface processes in the study area.

Prediction of the concentration of cadmium in agricultural soil in the Czech Republic using legacy data, preferential sampling, Sentinel-2, Landsat-8, and ensemble models

The current study assesses and predicts cadmium (Cd) concentration in agricultural soil using two Cd datasets, namely legacy data (LD) and preferential sampling-legacy data (PS-LD), along with four streams of auxiliary datasets extracted from Sentinel-2 (S2) and Landsat-8 (L8) bands. The study was divided into two contexts: Cd prediction in agricultural soil using LD, ensemble models, 10 and 20 m spatial resolution of S2 and L8 (context 1), and Cd prediction in agricultural soil using PS-LD, ensemble models and 10 and 20 m spatial resolution of S2 and L8 (context 2). In context 1, ensemble 1, L8 with PS-LD was the cumulative optimal approach that predicted Cd in agricultural soil with a higher R2 value of 0.76, root mean square error (RMSE) of 0.66, mean absolute error (MAE) of 0.35, and median absolute error (MdAE) of 0.13. However, with R2 = 0.78, RMSE = 0.63, MAE = 0.34, and MdAE = 0.15, ensemble 1, S2 of PS-LD was the best prediction approach in predicting Cd concentration in agricultural soil in context 2. Overall, the predictions from both contexts indicated that ensemble 1 of S2 combined with PS-LD was the most appropriate and best model for Cd prediction in agricultural soil. The modeling approaches' uncertainty in both contexts was assessed using ensemble-sequential gaussian simulation (EnSGS), which revealed that the degree of uncertainty propagated in the study area was within 5% in both contexts. The combination of the PS dataset and the LD along with ensemble models and the remote sensing dataset, produced promising results. Nonetheless, the results demonstrated that the 20 m spatial resolution band dataset used in the prediction of Cd in agricultural soil outperformed the 10 m spatial resolution. When PS is combined with LD, an appropriate modeling approach, and a well-correlated remote sensing dataset are used, good results are obtained.

Deep-Learning-Based Retrieval of an Orange Band Sensitive to Cyanobacteria for Landsat-8/9 and Sentinel-2

The lack of an orange band (∼620 nm) in the imagery captured by Landsat-8/9 and Sentinel-2 restricts the detection and quantification of harmful cyanobacterial blooms in inland waters. A recent study suggested the retrieval of orange remote sensing reflectance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_rs</i> (620), by assuming green, red, and panchromatic (Pan) bands of Landsat-8 as predictors through a linear model. However, this method is not applicable to Sentinel-2 imagery lacking a Pan band. Moreover, the Pan-based method does not account for the nonlinear relationships among the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_rs</i> data at different wavelengths. We propose a deep-learning model called DOABLE-Net (Deep OrAnge Band LEarning Network) that leverages a large training set of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_rs</i> data from radiative transfer simulations and in-situ measurements. The proposed DOABLE-Net is structured as five fully-connected layers and implemented either with or without the Pan band as an input feature, which only the latter applies to Sentinel-2. DOABLE-Net provided more accurate and robust retrievals than the Pan-based method on a wide range of independent validation datasets. The performance of DOABLE-Net on Landsat-8/9 data was minimally impacted by including the Pan band. The results from Sentinel-2 data analysis also confirmed that the DOABLE-Net provides promising results without using a Pan band.