Thermal remote sensing of sea surface temperature

Sea surface temperature has been an important application of remote sensing from space for three decades. This chapter first describes well-established methods that have delivered valuable routine observations of sea surface temperature for meteorology and oceanography. Increasingly demanding requirements, often related to climate science, have highlighted some limitations of these ap-proaches. Practitioners have had to revisit techniques of estimation, of characterising uncertainty, and of validating observations—and even to reconsider the meaning(s) of “sea surface temperature”. The current understanding of these issues is reviewed, drawing attention to ongoing questions. Lastly, the prospect for thermal remote sens-ing of sea surface temperature over coming years is discussed

An algorithm to retrieve Land Surface Temperature using Landsat-8 Dataset

Soil moisture, surface temperature, and vegetation are variables that play an important role in our environment which in turn increases the demand for accurate estimation of certain geophysical parameters such as weather, flooding, and land classification. However, for accurate Land Surface Temperature (LST) estimation, remotely sensed data of key environmental forms were considered and applied in this research. The goal of this study was to apply a suitable algorithm for LST estimation from the Landsat-8 dataset that gives a great accuracy when compared with in-situ observations.Spatial and temporal Landsat-8 data were acquired which provided the analytical structure for linking specific data successfully due to fine resolutions. The data were then applied to determine brightness temperatures, vegetation cover, and surface emissivity which demonstrated the effectiveness of the Split-Window Algorithm as an optimum method for LST retrieval from satellite.The results show temperature variation over a long period of time can be used in observing varying temperature values based on terrain i.e. High temperatures in fully built up areas and low temperatures in the well-vegetated regions. Finally, accurate LST estimation is important for land classification, energy budget estimations as well as agricultural production.Keywords: Emissivity, Landsat, Land Surface Temperature, Split-Window, Vegetatio

Harmonization of space-borne infra-red sensors measuring sea surface temperature

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

In this study, the split window (SW) method was applied for land surface temperature (LST) retrieval using Landsat 8 in two apple orchards (Glindow, Altlandsberg). Four images were acquired during high demand of irrigation water from July to August 2018. After pre-processing images, the normalized difference vegetation index (NDVI) and LST were calculated by red, NIR, and thermal bands. The results were validated by interpolated infrared thermometer (IRT) measurements using the inverse distance weighting (IDW) method. In the next step, the temperature vegetation index (TVDI) was calculated based on the trapezoidal NDVI/LST space to determine the water status of apple trees in the case studies. Results show good agreement between interpolated LST using IRT measurements and remotely sensed LST calculation using SW in all satellite overpasses, where the absolute mean error was between 0.08 to 4.00 K and root mean square error (RMSE) values ranged between 0.71 and 4.23 K. The TVDI spatial distribution indicated that the trees suffered from water stress on 7 and 23 July and 8 August 2018 in Glindow apple orchard with the mean value of 0.69, 0.57, and 0.73, whereas in the Altlandsberg orchard on 17 August, the irrigation system compensated the water deficit as indicated by the TVDI value of 0.34. Moreover, a negative correlation between TVDI and vegetation water content (VWC) with correlation coefficient (r) of −0.81 was observed. The corresponding r for LST and VWC was equal to −0.89, which shows the inverse relation between water status and temperature-based indices. The results indicate that the LST and/or TVDI calculation using the proposed methods can be effectively applied for monitoring tree water status and support irrigation management in orchards using Landsat 8 satellite images without requiring ground measurements

Surface albedo and temperature models for surface energy balance fluxes and evapotranspiration using SEBAL and Landsat 8 over Cerrado-Pantanal, Brazil

The determination of the surface energy balance fluxes (SEBFs) and evapotranspiration (ET) is fundamental in environmental studies involving the effects of land use change on the water requirement of crops. SEBFs and ET have been estimated by remote sensing techniques, but with the operation of new sensors, some variables need to be parameterized to improve their accuracy. Thus, the objective of this study is to evaluate the performance of algorithms used to calculate surface albedo and surface temperature on the estimation of SEBFs and ET in the Cerrado-Pantanal transition region of Mato Grosso, Brazil. Surface reflectance images of the Operational Land Imager (OLI) and brightness temperature (Tb) of the Thermal Infrared Sensor (TIRS) of the Landsat 8, and surface reflectance images of the MODIS MOD09A1 product from 2013 to 2016 were combined to estimate SEBF and ET by the surface energy balance algorithm for land (SEBAL), which were validated with measurements from two flux towers. The surface temperature (Ts) was recovered by different models from the Tb and by parameters calculated in the atmospheric correction parameter calculator (ATMCORR). A model of surface albedo (asup) with surface reflectance OLI Landsat 8 developed in this study performed better than the conventional model (acon) SEBFs and ET in the Cerrado-Pantanal transition region estimated with asup combined with Ts and Tb performed better than estimates with acon. Among all the evaluated combinations, SEBAL performed better when combining asup with the model developed in this study and the surface temperature recovered by the Barsi model (Tsbarsi ). This demonstrates the importance of an asup model based on surface reflectance and atmospheric surface temperature correction in estimating SEBFs and ET by SEBAL

Comparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS imagery : case study of a large shallow subtropical lake in southern Brazil

Water temperature regulates many processes in lakes; therefore, evaluating it is essential to understand its ecological status and functioning, and to comprehend the impact of climate change. Although few studies assessed the accuracy of individual sensors in estimating lake-surface-water temperature (LSWT), comparative analysis considering different sensors is still needed. This study evaluated the performance of two thermal sensors, MODIS and Landsat 7 ETM+, and used Landsat methods to estimate the SWT of a large subtropical lake. MODIS products MOD11 LST and MOD28 SST were used for comparison. For the Landsat images, the radiative transfer equation (RTE), using NASA’s Atmospheric Correction Parameter Calculator (AtmCorr) parameters, was compared with the single-channel algorithm in different approaches. Our results showed that MOD11 obtained the highest accuracy (RMSE of 1.05 C), and is the recommended product for LSWT studies. For Landsat-derived SWT, AtmCorr obtained the highest accuracy (RMSE of 1.07 C) and is the recommended method for small lakes. Sensitivity analysis showed that Landsat-derived LSWT using the RTE is very sensitive to atmospheric parameters and emissivity. A discussion of the main error sources was conducted. We recommend that similar tests be applied for Landsat imagery on different lakes, further studies on algorithms to correct the cool-skin effect in inland waters, and tests of different emissivity values to verify if it can compensate for this effect, in an effort to improve the accuracy of these estimates

Assessment of Landsat 8 TIRS data capability for the preliminary study of geothermal energy resources in West Sumatra

West Sumatra is one of has big geothermal energy resources potential. Remote sensing technology can have a role in geothermal exploration activity to measure the distribution of land surface temperatures (LST) and predict the geothermal potential area. Main study to obtain the assessment of Landsat 8 TIRS (Landsat`s Thermal Infrared Sensor) data capability for geothermal energy resources estimation. Mono-window algorithms were used to generate the LST maps. Data set was combined with a digital elevation model (DEM) to identify the potential geothermal energy based on the variation in surface temperature. The result that were derived from LST map of West Sumatra shows that ranged from -8.6 C0 to 32.59 C0 and the different temperatures are represented by a graduated pink to brown shading. A calculated result clearly identifies the hot areas in the dataset, which are brown in colour images. Lima Puluh Kota, Tanah Datar, Solok, and South Solok areas showed the high-temperature value (Brown) in the range of 28.1 C0 to 32.59 C0 color in images which means that they possess high potential for generating thermal energy. In contrast, the temperatures were lower (Pink) in the north-eastern areas and the range distribution was from-8.5 C0 to 5 C0

Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE