Landsat Bands Research Articles

Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors

ABSTRACT Mapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six countries, but a recent fine-scale LULC map of the area is lacking. Using Landsat-8 time series, a 30-m resolution LULC map was produced for the Tigris-Euphrates basin. In total, 1184 Landsat scenes were processed within the Google Earth Engine (GEE). For the collection of ground truth data, differential manifestations of green cover were considered by dividing the study area into five climatic regions and the training samples were taken from each sub-region. To account for the temporal variation of LULC types, six two-month interval composite layers, including the spectral and thermal bands of Landsat-8, texture and spectral indices, as well as topographic factors were created for the target year 2019. Image segmentation and classification were performed using the simple non-iterative clustering (SNIC) and Random Forest (RF) algorithms, respectively. A computationally effective parallel processing approach was developed, which created a number of tiles and sub-tiles and a bulk command was converted into smaller parallel commands. The generated LULC map showed a satisfactory overall accuracy of 91.7%, with the highest User’s accuracy in water and wetland, and the lowest in rainfed crop and rangeland and the highest Producer’s accuracy in water and barren areas, and the lowest in garden and rangeland. This study highlights the necessity of using multi-temporal data for LULC mapping, in particular, multi-temporal NDVI, for the separation of different green cover types in arid and semi-arid environment.

Multi-sensor remote sensing analysis of coal fire induced land subsidence in Jharia Coalfields, Jharkhand, India

The subsidence in coal mines induced by surface and subsurface fires leading to roof collapse, infrastructure loss, and loss of lives is a prominent concern. In the study, satellite imagery from thermal and microwave remote sensing data is used to deduce the effect of coal fires on subsidence in the Jharia Coalfields, India. The Thermal Infrared data acquired from the Landsat-8 (band 10) is used to derive the temperature anomaly maps. Persistent Scatterer Interferometry analysis was performed on sixty Sentinel-1, C-band images, the results are corrected for atmospheric error using Generic Atmospheric Correction Online Service for InSAR (GACOS) atmospheric modelling data and decomposed into vertical displacement values to quantify subsidence. A zone-wise analysis of the hazard patterns in the coalfields was carried out. Coal fire maps, subsidence velocity maps, and land cover maps were integrated to investigate the impact of the hazards on the mines and their surroundings. Maximum subsidence of approximately 20 cm/yr. and temperature anomaly of up to 25 °C has been observed. The findings exhibit a strong positive correlation between the subsidence velocity and temperature anomaly in the study area. Kusunda, Keshalpur, and Bararee collieries are identified as the most critically affected zones. The subsidence phenomenon in some collieries is extending towards the settlements and transportation networks and needs urgent intervention.

Geohazard characterization using remote sensing to model flash floods of the southeast Sinai, Egypt

Flash floods have led to disruptions of human activities and the destruction of properties particularly in the Nuweiba region of southeast Sinai. Despite the arid nature of Sinai, flash floods still pose a great hazard to the region. Using remote sensing characterization, geohazard models were developed to identify flash flood areas; delineate and discriminate morphological features, active channels areas, and soil physiography with a view to categorize risk areas exposed to flash floods hazards in a hazard map; and proffer measures for mitigation. The characterization was achieved using a workflow model developed from spatial data sets of the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) and the red-green-blue (RGB) composites from Landsat bands 7 Enhanced Thematic Mapper (ETM+). The data were used to investigate and assess flash flood areas for morphological attributes, watershed basin characterization, and generation of false color composite from the Landsat 7 ETM+ 7, 4, and 2 RGB bands, and the principal component analysis band from a composite of 742 RGB. Multivariate analysis of generated raster layer attributes provided enhancements and attribute discrimination for delineating areas of active flood channels, upslope zones, and soil physiography discrimination from their spectral reflectance. The results are presented in an integrated approach of remote sensing with geographic information systems (GIS) and indicate that the soil types and geologic units contribute greatly toward activation of these flash floods, which is triggered by intense rainstorms. Components of the generated map attributes of SRTM DEM, gradient of DEM, and Landsat 7 ETM+ composite of 742 bands in GIS were used to generate a hazard map using spatial analysis to depict the nature and scale of the issues identified. Due to the flash flood vulnerabilities, the study area was classified into stable/low-, moderate-, and high-risk areas. Mitigation measures to control flash floods were proposed to enable adequate preparations to mitigate impending flash flood disasters.

Empirical Estimation of Nutrient, Organic Matter and Algal Chlorophyll in a Drinking Water Reservoir Using Landsat 5 TM Data

The main objective of this study was to develop empirical models from Landsat 5 TM data to monitor nutrient (total phosphorus: TP), organic matter (biological oxygen demand: BOD), and algal chlorophyll (chlorophyll-a: CHL-a). Instead of traditional monitoring techniques, such models could be substituted for water quality assessment in aquatic systems. A set of models were generated relating surface reflectance values of four bands of Landsat 5 TM and in-situ data by multiple linear regression analysis. Radiometric and atmospheric corrections improved the satellite image quality. A total of 32 compositions of different bands of Landsat 5 TM images were considered to find the correlation coefficient (r) with in-situ measurement of TP, BOD, and CHL-a levels collected from five sampling sites in 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a correlate well with Landsat 5 TM band reflectance values. TP (r = −0.79) and CHL-a (r = −0.79) showed the strongest relations with B1 (Blue). In contrast, BOD showed the highest correlation with B1 (Blue) (r = −0.75) and B1*B3/B4 (Blue*Red/Near-infrared) (r = −0.76). Considering the r values, significant bands and their compositions were identified and used to generate linear equations. Such equations for Landsat 5 TM could detect TP, BOD, and CHL-a with accuracies of 67%, 65%, and 72%, respectively. The developed empirical models were then applied to all study sites on the Paldang Reservoir to monitor spatio-temporal distributions of TP, BOD, and CHL-a for the month of September using Landsat 5 TM images of the year 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a decreased from 2001 to 2006 and 2010. However, S3 and S4 still have water quality issues and are influenced by climatic and anthropogenic factors, which could significantly affect reservoir drinking water quality. Overall, the present study suggested that the Landsat 5 TM may be appropriate for estimating and monitoring water quality parameters in the reservoir.

Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia.

This study aims to develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images. VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). Comparison of prediction using Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn) and Multivariate Adaptive Regression Spline (MARS). Prediction results are interpolated using Inverse Distance Weight (IDW). This study was conducted in stages: (1) Image preprocessing; (2) calculating numerical data extracted from the LANDSAT band imagery using vegetation indices; (3) analyzing correlation coefficients between VI; (4) prediction using RF and CART; (5) comparing performances between RF and CART using ANN, SVM, k-nn, and MARS; (6) testing the accuracy of prediction using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE); (7) interpolating with IDW. Correlation coefficient of VI data shows a positive correlation, the lowest r (0.07) and the highest r (0.98). The experiments show that the RF and CART algorithms have efficiency and effectivity in determining the aridity areas better than the ANN, SVM, k-nn, and MARS algorithm. RF has a difference between the predicted results and 1.04% survey data MAPE and the smallest value close to zero is 0.05 MSE. CART has a difference between the predicted results and 1.05% survey data MAPE and the smallest value approaching to zero which is 0.05 MSE. The prediction results of VI show that in 2020 most of the study areas were low vegetation areas with the Normalized Difference Vegetation Index (NDVI) < 0.21, had an indication of drought with the Vegetation Health Index (VHI) < 31.10, had a Vegetation Condition Index (VCI) in some areas between 35%–50% (moderate drought) and < 35% (high drought). The Burn Area Index (dBAI) values are between −3, 971 and −2,376 that show the areas have a low fire risk, and index values are between −0, 208 and −0,412 that show the areas are starting vegetation growth. The result of this study shows that the machine learning algorithms is an accurate and stable algorithm in predicting the risks of drought and land fire based on the VI data extracted from the LANDSAT 8 OLL imagery. The VI data contain the record of vegetation condition and its environment, including humidity, temperatures, and the environmental vegetation health.

STUDY INFLUENCE OF LAND COVER CHANGE IN WETLAND AND VEGETATION ON LAND SURFACE TEMPERATURE

Wetlands are a vital component of land cover in reducing impacts caused by urban heat effects and climate change. Remote sensing technology provides historical data that can study the impact of development on the environment and local climate. The studies of wetland in reducing Land Surface Temperature (LST) in a tropical climate are still lacking. The objective of the study is to examine the influence of land cover change wetland and vegetation on land surface temperature between the years 1988 and 2019. First of all, step, pre-processing, namely geometric correction, atmosphere correction, and radiometric correction, were performed before retrieval of the LST dataset from thermal band Landsat 5 and 8. Then, Iso Cluster, unsupervised was chosen to produce the land cover map for 1988 and 2019. Geographical Information System (GIS) technology was utilized to determine changes to land cover and LST change between the years 1988 and 2019. With GIS technology, a study of the impact of wetland deforestation on local temperatures at a local scale was carried out. Next to that, correlations between LST and the wetland were analyzed. The results indicated the different land cover between the years 1988 and 2019. The areas of land cover for wetland and vegetation decrease and while area of urban increased. The land cover changed the influences of LST significantly in the study area. The LST increased with the decreasing in areas wetland areas for every 5-kilometer square (km²) wetland lost an increase in 1-degree Celsius of LS was estimated. The size of wetland influence on LST was significant. Wetland and vegetation function in reducing the urban heat island effect was vital in providing a comfortable environment to the Kuching population and indirectly reduce the demand for power energy.

Crop specific algorithms trained over ground measurements provide the best performance for GAI and fAPAR estimates from Landsat-8 observations

Estimation of Green Area Index (GAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from decametric satellites was investigated in this study using a large database of ground measurements over croplands. It covers six main crop types including rice, corn, wheat and barley, sunflower, soybean and other types of crops. Ground measurements were completed using either digital hemispherical cameras, LAI-2000 or AccuPAR devices over sites representative of a decametric pixel. Sites were spread over the globe and the data collected at several growth stages concurrently to the acquisition of Landsat-8 images. Several machine learning techniques were investigated to retrieve GAI and fAPAR from the Landsat-8 top of canopy reflectance values, either using empirical or simulated calibration databases and generic or crop-type specific algorithms.Results show that using the six Landsat-8 bands together provided the best estimates of GAI and fAPAR. Machine learning techniques trained over a dataset simulated by the PROSAIL model provided less accurate estimates of GAI and fAPAR as compared to machine learning techniques trained over the ground data collected and applied over a similar dataset (best-case scenario). All machine learning techniques performed similarly when calibrated on PROSAIL simulations. The Gaussian process regression (GPR) was the best performing machine learning technique as compared to artificial neural networks (ANN), support vector machine regression (SVM) and as compared to NDVI simple model when calibrated over the ground dataset in the best-case scenario. However, the performance of the GPR trained over ground data is notably degraded when applied to crop types excluded from the calibration (worst-case scenario), and models based on simulations performs similarly of even better. Furthermore, training the GPR over specific crop types was performing slightly better than training over all the crop types together if at least 100 well distributed data points were available in the training dataset. Similar conclusions were obtained for the other machine learning techniques, with crop specific empirical models providing slightly better performances with a few exceptions. Finally, GAI was estimated by GPR with a RMSE varying between 0.45 and 1.19 and fAPAR with RMSE varying between 0.07 and 0.15 depending on the crop type.

A novel technique for ship wake detection from optical images

Monitoring ships at sea is of great importance for both civilian and military purposes. Since ship wakes are more distinct than hulls, wakes are widely employed for ship detection from satellite imagery. In this paper, a novel approach was proposed to detect ships from optical imagery. Candidate wakes were first obtained by the normalized Radon transform of an image with the ship hull in the center. False wakes were then removed by pixel value verification, turbulent wake identification, included angle verification, and contrast verification. Meanwhile, bright edges of turbulent wakes were detected after turbulent wake identification. Detected wakes were verified with wakes delineated through visual inspection. By implementing the proposed technique to optical imagery, reflectance at the near-infrared (NIR) band of GF 1 and Sentinel 2 MSI and the panchromatic band of Landsat 8 OLI showed the best accuracies. Total recalls of turbulent and Kelvin wakes were 93.5% and 91.2%, respectively, while total precisions were 93.7% and 93.8%, respectively. Positions, directions, and endpoints of wakes were also obtained with good accuracy. The categories of wakes were confirmed by the wake features and the spatial relationships between wakes. Factors influencing the accuracy of the developed method and comparison with state-of-the-art wake detection methods were then discussed.

Discrimination of Neoproterozoic granitic-phases in Southern-Sinai, Egypt. Remote sensing, petrography and geochemistry

At Sharm El-Sheikh area a unique association of Neoproterozoic Younger granitic phases is found. These granitic rocks are petrographically identified as monzogranite, syenogranite, alkali feldspar granite, and riebeckite granite.The magma sources of monzogranite and syenogranite are generally of magnesian affinity while those of Alkali-feldspar granite and riebeckite granite are ferroan affinity. Tectonically, the monzogranite and syenogranite were generated during syn- collision tectonic regime and those of Alkali-feldspar granite and riebeckite granite are (within-plate granite). Geochemically they are discriminated into fractionated A-type granites and I-type granite. Alkaline granite samples have the highest ∑REE upto 497.4 ppm while monzogranite samples have the lowest ∑REE reach 153.7. The most conspicuous negative Eu anomaly (Eu/Eu* = 0.1) is recorded in the in alkali feldspar and riebeckite granitoids while monzogranite and syenogranite samples display negligible negative to slight positive Eu anomalies (Eu/Eu* = 1.8).Landsat-8 bands (7, 4, 2) and (7, 3, 5) in RGB used in discrimination and lithologic mapping of the different granitoid rocks. Band rationing technique was successful in discrimination of the two main granitic phases of the study area.

Application of remote sensing data in gold exploration: targeting hydrothermal alteration using Landsat 8 imagery in northern Portugal

Mapping hydrothermal alteration minerals and structural lineaments using Landsat 8 multispectral imagery provides valuable information for mineral exploration. In northern Portugal, there are several known gold occurrences, but there is the potential to identify new anomalous areas. Gold mineralization occurs in the form of quartz veins and veinlets associated with hydrothermal alteration halos. Fractures are interpreted as conduits for mineralizing fluids, where the interaction between the wall rock and hydrothermal fluids induces compositional variations. Identifying these features is one of the key indicators for targeting new prospective zones of orogenic gold mineralization in the Boticas–Chaves region. Remote sensing image processing methods such as band combinations, band ratios, and principal component analysis (PCA) were implemented to the visible, near-infrared, and shortwave infrared bands of Landsat 8. The results of this investigation demonstrate the capability of the applied imagery enhancement methods in distinguishing different features and identifying hydrothermally altered rocks. Selective PCA proved to be the most effective and reliable method to identify iron oxides and hydroxyl-bearing minerals, compared to other methods, where a simple imagery analysis has a strong influence of noise and is more challenging to interpret. Enhanced imagery allowed the identification of physiographic characteristics and extracted structural features. The combination of mapped hydrothermal alteration minerals and extracted structural features allowed us to predict potential areas for the mineralization occurrence. This investigation proves that remote sensing can be a cost-efficient and time-saving technique for mineral exploration, and its application in new areas can accurately map hydrothermal alteration and outline potential new exploration targets.

Identification of the impact of vegetation cover changes and the development of urban areas on Urban Heat Island using GIS and remote sensing: A case studies of Sleman regency, Province of Yogyakarta

The phenomenon of urban area development both physically and socially as the center of government in Sleman can indirectly cause an impact on the atmosphere. One of the impacts of the atmospheric change, the urban heat island, which is influenced by the physical and social development activities, is expanding. The research focuses on the study of surface urban heat island (SUHI) aimed at knowing the spatial distribution of the UHI intensity in Sleman Regency. The methods used in this research are the integration of remote sensing techniques and geographic information systems (GIS) using thermal bands of Landsat 8 OLI/TIRS and image statistical analysis using Buffer Boundary Analysis to identify the potential occurrence of UHI in Sleman. The result of the processing shows the potential of high-intensity urban heat island in Depok subdistrict, Ngemplak subdistrict, Kalasan subdistrict, and part of Mlati subdistrict with the varying intensity of UHI. The highest UHI intensity is around the district of Depok and Mlati with an intensity of 2,001 - 6,000 °c during the period from 2014 to 2019.

Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

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

Identifikasi Pertumbuhan Urban Heat Island secara Spasial-Temporal di Kota Palangka Raya Menggunakan Penginderaan Jauh dan Sistem Informasi Geografis

Developments in the city of Palangka Raya y giving different temperature between urban and sub-urban. Phenomenon that cities have warmer temperatures than sub-urban and rural areas called Urban Heat Island (UHI). This study aims to find out about the development of the UHI phenomenon in the city of Palangka Raya from 2000 to 2018 using remote sensing and geographical system. Based on the analysis of the TIR band landsat 7, in Kota Palangka Raya since 2000 has been UHI phenomenon, where high Land Surface Temperature (LST) was found dominantly in urban areas compared to sub-urban areas . In 2018, as Palangka Raya city had developed, based on the result of TIR band Landsat 8, the distribution of high LST not only found in the urban area, but in the sub-urban zone, especially at Menteng Urban Village and Panarung Urban Village. The development of UHI in Palangka Raya city over eighteen years (2000-2018) show in the sub-urban area experienced changes of UHIindex’s area more dynamic than the urban area. Urban development causing to conversion of vegetated land into impervious land, which greatly affects the energy balance. The increase in impervious areas causes more solar radiation that reaches the surfaces of the earth more absorbing and it is converted into sensible thermal energy which increases the surface temperature.

MODELAGEM ESPAÇO-TEMPORAL DE ILHAS DE CALOR URBANA E CORRELAÇÃO COM VARIÁVEIS- MONTES CLAROS, MG, BRASIL

As ilhas de calor urbana (ICU) é um aumento da temperatura superficial (TS) e atmosférica, atingindo metrópoles e cidades médias do mundo. Alterações de uso da terra, clima e topografia modulam o fenômeno, compreender relação desses fatores possibilita identificar as causas de ICU. A pesquisa tem foco nesse âmbito, com análise TS, formação de ICU e relação com outras variáveis. O estudo foi na sexta maior cidade do estado de Minas Gerais (Montes Claros), em zona de clima AW (Köppen). A modelagem de TS foi a partir da banda termal Landsat 5 e 8 durante meses agosto de 1991, 2001, 2011 e 2018. Análise estatística multivariada foi aplicada entre informações de TS, variáveis de uso da terra e geomorfométricas. Entre 1991 a 2018 a TS aumentou, convertendo faixas de 22 a 26 °C para &gt; 28 °C, com formação e aumento de 333% de ICU (ICU= &gt; 4 °C média/período). Fatores naturais de radiação solar, altitude, índice de circulação do vento, e face de exposição de vertente influenciam na dinâmica de TS. Porém, o aumento de TS tem melhor relação com densidade urbana e com baixa cobertura vegetal. A redução do problema de ICU deve focar no planejamento de uso da terra e aumento de áreas verdes.

Using forestry inventories and satellite imagery to assess floristic variation in bamboo‐dominated forests in Peruvian Amazonia

AbstractQuestionsDoes the floristic composition of trees differ between bamboo forests and adjacent non‐bamboo forests? Can the degree of compositional differences be predicted from differences in canopy reflectance as measured by Landsat satellites? Are the results sensitive to different taxonomical data cleaning strategies, or to which tree‐size class is considered? Are some tree taxa associated with either bamboo or non‐bamboo forests?LocationPeruvian Amazonia.MethodsWe used national forestry inventory data to characterise floristic composition of trees at 25 sites. Bamboo and non‐bamboo forests were identified with a pixel‐based time series analysis using the entire Landsat TM/ETM+ archive. To visualise floristic similarity among the plots, we used non‐metric multidimensional scaling. Floristic differences between bamboo and non‐bamboo forests were tested using an analysis of similarity (ANOSIM). Mantel tests were used to assess correlation between floristic turnover and differences in canopy reflectance. We tested the impact of applying three different taxonomic data cleaning strategies and of analysing different tree‐size classes. Finally, we ran an indicator species analysis to identify taxa that were associated with either bamboo or non‐bamboo forests.ResultsIn floristic ordinations, bamboo‐dominated forests appeared floristically distinct from non‐bamboo forests regardless of the taxonomic cleaning strategy and tree‐size class. This floristic separation was also confirmed through analysis of floristic similarity (ANOSIM). Turnover in floristic composition was strongly correlated with differences in the reflectance values of Landsat bands, especially when using genus‐level rather than species‐level data. Different palm taxa were associated with bamboo (Socratea) and non‐bamboo forests (Iriartea).ConclusionsFloristic differences of trees between bamboo‐dominated and non‐bamboo forests are consistent enough to be observable with different tree‐size classes and taxonomic cleaning strategies. Although coarsening the taxonomic resolution to the genus level may hide ecological detail from the results, the fact that it allows keeping incompletely identified stems in the analyses seems to compensate for this.

Estimation of Total Dissolved Solids in Water Bodies by Spectral Indices Case Study: Shatt al-Arab River

In recent years, the problem of rising salinity levels in the Shatt al-Arab river in southern Iraq has been repeated, which has directly affected the living and health situation and the agricultural activity of these areas. Six sampling stations were selected along Shatt al-Arab to estimate the concentration of total dissolved solids (TDS) in the river; these stations included the following: Qurna, Labani, City Centre, Kateban, Corniche, and Sihan. In addition, three Landsat-8 satellite images which were taken at the same time as collected samples also used for detecting the salinity in the river. After processing of atmospheric correction and inserted remote sensing indices, the reflectance of water extracted from satellite images was used to express the spectral characteristics of different TDS concentrations. Correlation and regression were used to obtain accurate models for detecting the salinity depending on the spectral reflectance of Landsat 8 operational land image OLI. The results presented Pearson correlation (r) value of 0.70, 0.97, and 0.71, and correlation coefficient (R2) of 0.56, 0.94, and 0.85 between field data with spectral data of salinity index 2 (SI-2) derived from the green and blue bands of Landsat obtained in 2015, 2017, and 2018 respectively. In conclusion, remote sensing and GIS technologies coupled with spectral modeling are useful tools for providing a solution of future water resources planning and management, and also offer great undertaking as a means to improve knowledge of water quality and support water decision making.

An Empirical Radiometric Intercomparison Methodology Based on Global Simultaneous Nadir Overpasses Applied to Landsat 8 and Sentinel-2

The Simultaneous Nadir Overpass (SNO) method was developed by the NOAA/NESDIS to improve the consistency and quality of climate data acquired by different meteorological satellites. Taking advantage of the reduced impact induced by the Bidirectional Reflectance Distribution Function (BRDF), atmospheric effects, illumination and viewing geometries during an SNO, we created a sensor comparison methodology for all spectral targets. The method is illustrated by applying it to the assessment of data acquired by the Landsat 8 (L8), Sentinel-2A (S2A), and Sentinel-2B (S2B) optical sensors. Multiple SNOs were identified and selected without the need for orbit propagators. Then, by locating spatially homogeneous areas, it was possible to assess, for a wide range of Top-of-Atmosphere reflectance values, the relationship between the L8 bands and the corresponding ones of S2A and S2B. The results yield high coefficients of determination for S2 A/B with respect to L8. All are higher than 0.980 for S2A and 0.984 for S2B. If the S2 band 8 (wide near-infrared, NIR) is excluded then the lowest coefficients of determination become 0.997 and 0.999 from S2A and S2B, respectively. This methodology can be complementary to those based on Pseudo-Invariant Calibration Sites (PICS) due to its simplicity, highly correlated results and the wide range of compared reflectances and spectral targets.

Remote Sensing Analysis of Surface Temperature from Heterogeneous Data in a Maize Field and Related Water Stress

Precision agriculture aims at optimizing crop production by adapting management actions to real needs and requires that a reliable and extensive description of soil and crop conditions is available, that multispectral satellite images can provide. The purpose of the present study, based on activities carried out in 2019 on an agricultural area north of Ravenna (Italy) within the project LIFE AGROWETLANDS II, is to evaluate the potentials and limitations of freely available satellite thermal images for the identification of water stress conditions and the optimization of irrigation management practices, especially in agricultural areas and wetlands affected by saline soils and salt water capillary rise. Point field surveys and a very-high resolution thermal survey (5 cm) by an unmanned aerial vehicle (UAV) supported thermal camera were performed on a maize field tentatively at every Landsat-8 passage to check land surface temperature (LST) and canopy cover (CC) estimated from satellite. Temperature measured in the soil near ground surface and from UAV flying at 100 m altitude is compared with LST estimated from satellite measurements using three conversion methods: the top of atmosphere brightness temperature based on Landsat-8 band 10 (SB) corrected to account only for surface emissivity, the radiative transfer equation (RTE) for atmosphere effects correction, and the original split window method (SW) using both Thermal Infrared Sensor (TIRS) bands. The comparison shows discrepancies, due to extreme difference in resolution, the systematic hour of satellite passage (11 am solar time), and systematic differences between methods beside the unavoidable inaccuracy of UAV measurements. Satellite derived temperatures result usually lower than UAV measurements; SB produced the lowest values, SW the best (difference = −1.7 ± 1.7), and RTE the median (difference = −2.7 ± 1.6). The correlation between contemporary 30 m resolution temperature values of near pixels and corresponding tile-average temperatures was not significant, due to the purely numerical interpolation from the 100 m resolution TIRS images, whereas the time pattern along the season is consistent among methods, being correlation coefficient always greater than 0.85. Correlation coefficients among temperatures obtained from Landsat-8 by different methods are almost 1, showing that values are almost strictly related by a linear transformation. All the methods are useful to estimate water stress, since its associated Crop Water Stress Index (CWSI) is, from its definition, insensitive to linear transformation of temperatures. Actual evapotranspiration (ETa) maps are evaluated with the Surface Energy Balance Algorithm for Land (SEBAL) based on the three Landsat-8 derived LSTs; the higher is LST, the lower is ETa. Resulting ETa estimates are related with LST but not strictly, due to variation in vegetation cover and soil, therefore patterns result similar but not equivalent, whereas values are dependent on the atmosphere correction method. RTE and SW result in the best methods among the tested ones and the derived ETa values result reliable and appropriate to user needs. For real time application the Normalized Difference Moisture Index (NDMI), which can also be derived from more frequent Sentinel-2 passages, can be profitably used in combination or as a substitute of the CWSI.

Comparison of multi-source satellite data for quantifying water quality parameters in a mining environment

Population growth and associated anthropogenic activities such as mining are posing a major threat to water resources. Remote sensing provides valuable information for assessing and monitoring water quality and quantity, and thus supports sustainable water management. Several studies have demonstrated the utility of various remote sensing techniques for quantifying water quality parameters, however, more needs to be done to exploit the growing number of satellite sensors available to the public. The main objective of this study was to evaluate the performances of individual and combined bands of Landsat 8 OLI, Sentinel-2 MSI, ASTER and SPOT 6 data as predictors of water quality parameters of open water bodies in a mining area. We applied the all-subsets regression approach to explore all possible candidate models from which selection was made based on Akaike’s Information Criterion (AIC), coefficient of determination (adjR2) and Root-Mean-Squared-Error (RMSE). Two secondary objectives that make use of the products of the all-subsets regression approach were added in the study. This included comparison of a parsimonious model containing few predictors with the model that was ranked the best but containing more predictors; and comparison of models consisting of similar bands across sensors per water quality parameter. In general, all sensors yielded promising estimation accuracies, particularly for SAR, permanent hardness and cations (RMSE < 50% of observed means). Sentinel-2 bands produced the best estimations followed by Landsat 8 bands for most water quality parameters, chiefly due to the greater number of bands these sensors possess compared to ASTER and SPOT 6 sensors. All sensors provided alternative models that used fewer predicting bands than the best-ranked models (lowest AIC) and still showed comparable accuracies with the lowest AIC models for most variables (Pearson’s correlation, r up to 0.9). Sentinel-2, in particular, had the most accurate, parsimonious models compared to the other sensors. Pairwise cross-sensor comparison of estimation models using similar bands revealed the best matches when a model consisted of blue band only (r > 0.95) for most parameters while the similarity decreased as the number of predicting bands increased. Furthermore, the best similarities were obtained between Sentinel-2 and Landsat 8 sensors compared to the other pairs, indicating the potential of replacing one in the absence of the other. The findings of this study showed the advantage of commonly used remotely-sensed data for estimating, and by extension monitoring, water quality parameters in open water bodies. In addition, the results revealed that few, specific bands carry information relevant to characterise most of the water quality parameters considered in the study. We recommend similar studies for different seasons, considering the need to monitor water quality throughout the year.

An Approach for the Retrieval of Land Surface Temperature from the Industrial Area Using Landsat-8 Thermal Infrared Sensors

The thermal imagine provides data with synoptic coverage for investigating thermal information from hot sources for detecting, mapping energy loss from the industrial area. This study attempts to retrieved heat loss from the industrial area using Landsat-8 TIRS experimented at an industrial area of Pasir Gudang, Peninsular Malaysia, the main objective is to investigate the sensitivity of Landsat-8 TIR for detecting industrial thermal energy within the various range of targets of different temperatures. An estimated heat map with absolute surface temperature values is the final output. Apart of the pre-processing of Landsat-8 TIRS data, data are processed for the retrieval of land surface temperature, then subjected to a downscaling process to final 30 x 30 m pixels, hence enable to merge with all Landsat-8 bands for visualization and validation of results. The split window algorithm (SWA) is used for the temperature retrieval from band 10 and 11, with other driven parameters. The Multiple Adaptive Regression Splines (MARS) model for spatial downscaling was adopted in this study. The generated thermal energy map was then validated over selected targets in the field and compared to corresponding downscaled MODIS LST product (MODIS11A2). TIR bands applied with SWA generated 13.7°C temperature dynamic range from 22.35˜51.36° C in comparison with MODIS LST product values range from 27.17 ˜ 37.65°C). Results indicated good agreement between the generated thermal energy map with the in-situ validations (RMSE=0.43 °C). It is therefore concluded that derived Land surface temperature map derived is suitable for study industrial thermal environment at 1:50,000 ˜ 100,000 scales, adequately to be used for environmental impact assessment.