Accuracy assessment for Landsat 8 thermal bands in measuring sea surface temperature over Kuwait and North West Arabian Gulf

Studying physical oceanography is one of the important fields of remote sensing applications. Previously, the thermal mapping of seas and oceans relied on primitive methods, such as the use of sensors installed on buoys, extracting contour lines, and deriving the values from the confluence of contour lines. Today's remote sensing provides more advanced methods for extracting sea surface temperature (SST) values for all bodies of water as a continuous raster model, through thermal sensors installed on satellites designated to monitor and observe the Earth. The Landsat program has facilitated a quantum leap by providing its data free for the public. What has become increasingly important is the inclusion, in Landsat 8, of a thermal band on the TIRS sensor through which SST can be extracted with a spatial resolution of 100 m2. In this article, the accuracy of the two thermal bands (band 10 and 11) of Landsat 8 was validated in estimating the SST of Kuwaiti and Northwest Arabian Gulf waters, through the use of 62 thermal images and 66 ground-truthing points (GTPs) taken from the field in the period from July 2013 to March 2020. This was achieved through a function provided by the ENVI 5.3 software - “brightness temperature” - to derive the surface temperature. The accuracy of Landsat 8 to monitor the SST of Kuwait and north-west Arabian Gulf waters was validated by calculating the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The accuracy of the thermal band 10 was ± 2.03 degrees (7.9%), while the accuracy of the thermal band 11 was ± 3.13 degrees (13.7%). Therefore, this study demonstrated that the thermal band 10 of Landsat 8 is more accurate than the thermal band 11 in monitoring the SST of Kuwaiti and north-west Arabian Gulf waters, with a difference of ± 1.1 degrees (5.8%).

Estimation and Validation of Land Surface Temperature by using Remote Sensing & GIS for Chittoor District, Andhra Pradesh

Land Surface Temperature (LST) quantification is needed in various applications like temporal analysis, identification of global warming, land use or land cover, water management, soil moisture estimation and natural disasters. The objective of this study is estimation as well as validation of temperature data at 14 Automatic Weather Stations (AWS) in Chittoor District of Andhra Pradesh with LST extracted by using remote sensing as well as Geographic Information System (GIS). Satellite data considered for estimation purpose is LANDSAT 8. Sensor data used for assessment of LST are OLI (Operational Land Imager) and TIR (Thermal Infrared). Thermal band contains spectral bands of 10 and 11 were considered for evaluating LST independently by using algorithm called Mono Window Algorithm (MWA). Land Surface Emissivity (LSE) is the vital parameter for calculating LST. The LSE estimation requires NDVI (Normalized Difference Vegetation Index) which is computed by using Band 4 (visible Red band) and band 5 (Near-Infra Red band) spectral radiance bands. Thermal band images having wavelength 11.2 µm and 12.5 µm of 30th May, 2015 and 21st October, 2015 were processed for the analysis of LST. Later on validation of estimated LST through in-suite temperature data obtained from 14 AWS stations in Chittoor district was carried out. The end results showed that, the LST retrieved by using proposed method achieved 5 per cent greater correlation coefficient (r) compared to LST retrieved by using existing method which is based on band 10.

AVALIAÇÃO DA TEMPERATURA DA SUPERFÍCIE NO PANTANAL MATO-GROSSENSE POR SENSORIAMENTO REMOTO

Devido sua grande importância, o Pantanal é objeto de estudos de trabalhos que visam conhecer e proteger sua biodiversidade. Como instrumento indispensável, o Sensoriamento Remoto surge como uma ferramenta ideal para o mapeamento, identificação de queimadas, desmatamento e estudos climáticos. Dentre as variáveis estimadas pelos sensores orbitais, a temperatura da superfície é a menos explorada. Estudos que são conduzidos para validação da temperatura da superfície por meio de sensores orbitais e de estações meteorológicas no bioma Pantanal são escassos. Diante disso, este trabalho visou analisar a dinâmica da temperatura da superfície em uma unidade de conservação no norte do Pantanal a partir de imagens adquiridas do satélite Landsat 8. O estudo foi realizado em uma torre micrometeorológica, localizada entre os municípios de Cuiabá e Santo Antônio de Leverger - MT, situada na encosta de uma unidade de conservação, denominada Monumento Natural Morro de Santo Antônio. Para validação da temperatura da superfície, foram adquiridas imagens do satélite Landsat 8 fornecidos pela USGS. Resultados encontrados nesse trabalho demonstraram o uso das bandas termais do Landsat 8 no norte do Pantanal mato-grossense na discriminação de diferentes alvos na superfície. A análise parcial de cada banda do sensor TIRS demonstrou valores mais elevados na banda 10. A análise estatística dos dados indica que as maiores correlações da temperatura medida na torre meteorológica foram constatadas com a média das bandas termais do satélite Landsat 8. A análise de variância demonstrou que houve diferença significativa entre as médias das temperaturas medida e estimada.Palavras-chaves: banda termal, sensor orbital, Pantanal. Surface temperature evaluation in the Pantanal Mato Grosso by Remote Sensing A B S T R A C TDue to its great importance, the Pantanal is the object of studies that aim to know and protect its biodiversity. As an indispensable tool, Remote Sensing emerges as an ideal tool for the mapping, identification of fires, deforestation and climatic studies. Among the variables estimated by the orbital sensors, the surface temperature is the least explored. Studies that are conducted for surface temperature validation by orbital sensors and meteorological stations in the Pantanal biome are scarce. The study was carried out in a micrometeorological tower, located between the municipalities of Cuiabá and Santo Antônio, located on the slope of a conservation unit, called Natural Monument Morro de Santo Antônio. To validate the surface temperature, images of the Landsat 8 satellite provided by the USGS were acquired. Results obtained in this work demonstrated the use of the Landsat 8 thermal bands in the northern Pantanal of Mato Grosso in the discrimination of different surface targets. The partial analysis of each TIRS sensor band showed higher values in the 10 band. Statistical analysis of the data indicates that the highest correlations of the temperature measured in the meteorological tower were verified with the average of the thermal bands of the satellite Landsat 8. The analysis of variance showed that there was a significant difference between the measured and estimated temperatures.Keywords: thermal band, orbital sensor, Pantanal.

Automated Cloud and Cloud-Shadow Masking for Landsat 8 Using Multitemporal Images in a Variety of Environments

Landsat 8 images have been widely used for many applications, but cloud and cloud-shadow cover issues remain. In this study, multitemporal cloud masking (MCM), designed to detect cloud and cloud-shadow for Landsat 8 in tropical environments, was improved for application in sub-tropical environments, with the greatest improvement in cloud masking. We added a haze optimized transformation (HOT) test and thermal band in the previous MCM algorithm to improve the algorithm in the detection of haze, thin-cirrus cloud, and thick cloud. We also improved the previous MCM in the detection of cloud-shadow by adding a blue band. In the visual assessment, the algorithm can detect a thick cloud, haze, thin-cirrus cloud, and cloud-shadow accurately. In the statistical assessment, the average user’s accuracy and producer’s accuracy of cloud masking results across the different land cover in the selected area was 98.03% and 98.98%, respectively. On the other hand, the average user’s accuracy and producer’s accuracy of cloud-shadow masking results was 97.97% and 96.66%, respectively. Compared to the Landsat 8 cloud cover assessment (L8 CCA) algorithm, MCM has better accuracies, especially in cloud-shadow masking. Our preliminary tests showed that the new MCM algorithm can detect cloud and cloud-shadow for Landsat 8 in a variety of environments.

Four-band Thermal Mosaicking: A New Method to Process Infrared Thermal Imagery of Urban Landscapes from UAV Flights

Unmanned aerial vehicles (UAVs) support a large array of technological applications and scientific studies due to their ability to collect high-resolution image data. The processing of UAV data requires the use of mosaicking technology, such as structure-from-motion, which combines multiple photos to form a single image mosaic and to construct a 3-D digital model of the measurement target. However, the mosaicking of thermal images is challenging due to low lens resolution and weak contrast in the single thermal band. In this study, a novel method, referred to as four-band thermal mosaicking (FTM), was developed in order to process thermal images. The method stacks the thermal band obtained by a thermal camera onto the RGB bands acquired on the same flight by an RGB camera and mosaics the four bands simultaneously. An object-based calibration method is then used to eliminate inter-band positional errors. A UAV flight over a natural park was carried out in order to test the method. The results demonstrated that with the assistance of the high-resolution RGB bands, the method enabled successful and efficient thermal mosaicking. Transect analysis revealed an inter-band accuracy of 0.39 m or 0.68 times the ground pixel size of the thermal camera. A cluster analysis validated that the thermal mosaic captured the expected contrast of thermal properties between different surfaces within the scene.