A global long-term (1981–2000) land surface temperature product for NOAA AVHRR

Land surface temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite a variety of operational satellite LST products. In this study, a global 0.05∘×0.05∘ historical LST product is generated from NOAA advanced very-high-resolution radiometer (AVHRR) data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST; and (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapor content of 0.04 and 1.0 g cm−2, respectively, evaluated against the simulation dataset, the RF-SWA method has a mean bias error (MBE) of less than 0.10 K and a standard deviation (SD) of 1.10 K. To compensate for the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in situ LST, the RF-SWA LST has a MBE of 0.03 K with a range of −1.59–2.71 K, and SD is 1.18 K with a range of 0.84–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 to 3.01 K and SD is 3.57 K with a range of 2.34 to 3.69 K, indicating good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a), https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c), and https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b)

Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data

Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAs, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAs were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAs were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAs had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. Validation based on in-situ LST obtained for six sites further confirmed the similar accuracies of the SWAs, with the lowest RMSE ranges of 1.57–1.62 K and 0.49−0.61 K for Gobabeb and Lake Constance, respectively. While the best two SWAs usually yielded good accuracy, the official SLSTR LST generally had lower accuracy. The SWAs identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data

Online Εvaluation of Earth Observation Derived Indicators for Urban Planning and Management

Extensive urbanization and growth of population density have acquired a paramount interest towards a sustainable urban development. Earth Observation (EO) is an important source of information required for urban planning and management. The availability of EO data provides the immense opportunity for urban environmental indicators development easily derived by remote sensors. In this study, the state of the art methods were employed to develop urban planning and management relevant indicators that can be evaluated by using EO data. The importance of this approach lies on providing alternatives for improving urban planning and management, without consuming time and resources in collecting field or archived data. The evaluated urban indicators were integrated into a Web‐based Information System that was developed for online exploitation. The results for three case studies are therefore available online and can be used by urban planners and stakeholders in supporting their planning decisions

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

Integration of Aerial Thermal Imagery, LiDAR Data and Ground Surveys for Surface Temperature Mapping in Urban Environments

A single-band surface temperature retrieval method is proposed, aiming at achieving a better accuracy by exploiting the integration of aerial thermal images with LiDAR data and ground surveys. LiDAR data allow the generation of a high resolution digital surface model and a detailed modeling of the Sky-View Factor (SVF). Ground surveys of surface temperature and emissivity, instead, are used to estimate the atmospheric parameters involved in the model (through a bounded least square adjustment) and for a first assessment of the accuracy of the results. The RMS of the difference between the surface temperatures computed from the model and measured on the check sites ranges between 0.8 \ub0C and 1.0 \ub0C, depending on the algorithm used to calculate the SVF. Results are in general better than the ones obtained without considering SVF and prove the effectiveness of the integration of different data sources. The proposed approach has the advantage of avoiding the modeling of the atmosphere conditions, which is often difficult to achieve with the desired accuracy; on the other hand, it is highly dependent on the accuracy of the data measured on the ground

Multiscale Imaging of Evapotranspiration

Evapotranspiration (ET; evaporation + transpiration) is central to a wide range of biological, chemical, and physical processes in the Earth system. Accurate remote sensing of ET is challenging due to the interrelated and generally scale dependent nature of the physical factors which contribute to the process. The evaporation of water from porous media like sands and soils is an important subset of the complete ET problem. Chapter 1 presents a laboratory investigation into this question, examining the effects of grain size and composition on the evolution of drying sands. The effects of composition are found to be 2-5x greater than the effects of grain size, indicating that differences in heating caused by differences in reflectance may dominate hydrologic differences caused by grain size variation. In order to relate the results of Chapter 1 to the satellite image archive, however, the question of information loss between hyperspectral (measurements at 100s of wavelength intervals) laboratory measurements and multispectral (≤ 12 wavelength intervals) satellite images must be addressed. Chapter 2 focuses on this question as applied to substrate materials such as sediment, soil, rock, and non-photosynthetic vegetation. The results indicate that the continuum that is resolved by multispectral sensors is sufficient to resolve the gradient between sand-rich and clay-rich soils, and that this gradient is also a dominant feature in hyperspectral mixing spaces where the actual absorptions can be resolved. Multispectral measurements can be converted to biogeophysically relevant quantities using spectral mixture analysis (SMA). However, retrospective multitemporal analysis first requires cross-sensor calibration of the mixture model. Chapter 3 presents this calibration, allowing multispectral image data to be used interchangeably throughout the Landsat 4-8 archive. In addition, a theoretical explanation is advanced for the observed superior scaling properties of SMA-derived fraction images over spectral indices. The physical quantities estimated by the spectral mixture model are then compared to simultaneously imaged surface temperature, as well as to the derived parameters of ET Fraction and Moisture Availability. SMA-derived vegetation abundance is found to produce substantially more informative ET maps, and SMA-derived substrate fraction is found to yield a surprisingly strong linear relationship with surface temperature. These results provide context for agricultural applications. Chapter 5 investigates the question of mapping and monitoring rice agricultural using optical and thermal satellite image time series. Thermal image time series are found to produce more accurate maps of rice presence/absence, but optical image time series are found to produce more accurate maps of rice crop timing. Chapter 6 takes a more global approach, investigating the spatial structure of agricultural networks for a diverse set of landscapes. Surprisingly consistent scaling relations are found. These relations are assessed in the context of a network-based approach to land cover analysis, with potential implications for the scale dependence of ET estimates. In sum, this thesis present a novel approach to improving ET estimation based on a synthesis of complementary laboratory measurements, satellite image analysis, and field observations. Alone, each of these independent sources of information provides novel insights. Viewed together, these insights form the basis of a more accurate and complete geophysical understanding of the ET phenomenon

Validation of AVHRR Land Surface Temperature with MODIS and In Situ LST—A TIMELINE Thematic Processor

Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed

Evaluation of MODIS Land products for air temperature estimations in Colombia

El producto moderate resolution imaging spectroradiometer (MODIS) land-surface temperature/emissivity (LST ) es a menudo utilizado en estudios meteorológicos para evaluaciones en tiempo cuasireal. Colombia requiere de un manejo prospectivo de sus ecosistemas productivos, sin embargo no existe una alta densidad de mediciones para la temperatura de la superficie (temperatura del aire a 2 m). La validación clásica de los productos MODIS incluye trabajo de campo para la calibración y la medición de las diferencias entre los radiómetros y el sensor MODIS. Para esta investigación, los datos del producto LST de MODIS son comparados con estaciones climatológicas usando técnicas de regresión múltiple para aumentar la exactitud de MODIS LST en conjunto con los productos MOD09GA , MOD17A2, MOD15A2 y MOD13A2 como parámetros auxiliares (variables explicativas) dentro del modelo final. Las mediciones terrestres se realizaron en la zona Caribe, Casanare y Valle del Cauca, usando las estaciones agroclimatológicas en la primera temporada seca de 2007 y los datos MODIS en versión diaria. El índice de vegetación aumentado, la porción de radiación activa fotosintética y la fotosíntesis neta son incluidas en el modelo final como estimadores del parámetro clave para la temperatura del aire: la vegetación. Finalmente dos factores son propuestos para la estimación de la temperatura de la superficie-LST : ángulo cenit del sensor y ángulo cenit del sol, que registran los cambios en la reflectancia de la vegetación y la sensibilidad del sensor.The moderate resolution imaging spectroradiometer (MODIS) land-surface temperature/emissivity (LST) product is often used for studies in meteorology due to its ability for near realtime evaluations. Colombia, as a country requires a prospective management for its productive ecosystems, but currently does not have sufficient spatially-distributed field data for air temperature at 2-m above the ground. The traditional validation of MODIS products includes field campaigns for calibrating and measuring differences between the satellite sensor and radiometers. For this research, the LST data on the ground was compared with climatologic stations using multiple regression techniques for improving the accuracy of the LST from MODIS, using MOD09GA, MOD17A2, MOD15A2, MOD13A2 as ancillary parameters (explanatory variables) in the final model. The ground measurements were obtained in the Caribbean zone and the Casanare and Valle del Cauca departments in Colombia, using agroclimatic stations in the first dry season of 2007 and daily MODIS data. Enhanced vegetation index, fraction of photosynthetically active radiation, and net photosynthesis were included in the final model for explaining the vegetation as a key parameter for air temperature. Finally, two factors were proposed for LST estimation: sensor zenith angle and solar zenith angle due to the reflectance of the vegetation and sensitivity of the sensor.Fil: Castro Diaz, Ivan Ricardo. Universidad de Buenos Aires. Facultad de Filosofía y Letras. Instituto de Geografia "Romualdo Ardissone"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Remote Sensing Based Estimation of Evapo-Transpiration Using Selected Algorithms: The Case of Wonji Shoa Sugar Cane Estate, Ethiopia

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map on a pixel-by-pixel basis, the actual evapotranspiration (ETa) of the Wonji Shoa Sugarcane Estate using the Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0 - 6.85, 0.0 – 9.36, 0.0 – 3.61, 0.0 – 6.83 mm/day; using SSEB 0.0 - 6.78, 0.0 – 7.81, 0.0 – 3.65, 0.0 – 6.46 mm/day, and SSEBop were 0.05 - 8.25, 0.0 – 8.82, 0.2 – 4.0, 0.0 – 7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ETa over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop had higher ETa values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season, ETa is limited to water surplus areas only and in wet season, ETa was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ETa over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ETa estimated for the whole estate were 107 Mm3, 140 Mm3, and 178 Mm3 using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate. Keywords: ET; CWR; Landsat ETM+; Remote Sensing; SEBAL; SSEB; SSEBo