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

A physics-constrained machine learning method for mapping gapless land surface temperature

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable physical constraints are incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model. Physical constraints (PCs) are coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the PC-LGBM model, which incorporates surface energy balance (SEB) constraints underlying the data in CLM-LST modeling within a biophysical framework. Compared with a pure physical method and pure ML methods, the PC-LGBM model improves the prediction accuracy and physical interpretability of LST. It also demonstrates a good extrapolation ability for the responses to extreme weather cases, suggesting that the PC-LGBM model enables not only empirical learning from data but also rationally derived from theory. The proposed method represents an innovative way to map accurate and physically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation

Clouds and the Earth's Radiant Energy System (CERES) algorithm theoretical basis document

The theoretical bases for the Release 1 algorithms that will be used to process satellite data for investigation of the Clouds and the Earth's Radiant Energy System (CERES) are described. The architecture for software implementation of the methodologies is outlined. Volume 1 provides both summarized and detailed overviews of the CERES Release 1 data analysis system. CERES will produce global top-of-the-atmosphere shortwave and longwave radiative fluxes at the top of the atmosphere, at the surface, and within the atmosphere by using the combination of a large variety of measurements and models. The CERES processing system includes radiance observations from CERES scanning radiometers, cloud properties derived from coincident satellite imaging radiometers, temperature and humidity fields from meteorological analysis models, and high-temporal-resolution geostationary satellite radiances to account for unobserved times. CERES will provide a continuation of the ERBE record and the lowest error climatology of consistent cloud properties and radiation fields. CERES will also substantially improve our knowledge of the Earth's surface radiation budget

Clouds and the Earth's Radiant Energy System (CERES) Algorithm Theoretical Basis Document

The theoretical bases for the Release 1 algorithms that will be used to process satellite data for investigation of the Clouds and Earth's Radiant Energy System (CERES) are described. The architecture for software implementation of the methodologies is outlined. Volume 3 details the advanced CERES methods for performing scene identification and inverting each CERES scanner radiance to a top-of-the-atmosphere (TOA) flux. CERES determines cloud fraction, height, phase, effective particle size, layering, and thickness from high-resolution, multispectral imager data. CERES derives cloud properties for each pixel of the Tropical Rainfall Measuring Mission (TRMM) visible and infrared scanner and the Earth Observing System (EOS) moderate-resolution imaging spectroradiometer. Cloud properties for each imager pixel are convolved with the CERES footprint point spread function to produce average cloud properties for each CERES scanner radiance. The mean cloud properties are used to determine an angular distribution model (ADM) to convert each CERES radiance to a TOA flux. The TOA fluxes are used in simple parameterization to derive surface radiative fluxes. This state-of-the-art cloud-radiation product will be used to substantially improve our understanding of the complex relationship between clouds and the radiation budget of the Earth-atmosphere system

Wintertime land surface albedo of forested environments

Forest canopies are complex 3-D structures at the interface between the atmosphere and the land surface which greatly affect radiative processes, especially across seasonally snow-covered domains. The increase in process complexity associated with these areas continues to be a source of uncertainty in land surface modelling. Model development has been hampered by a limited amount of in-situ radiation measurements, together with contrasting spatial scales of measurements and model resolution. Here, a bespoke cable car system was used to measure incoming and outgoing shortwave and longwave radiation below an evergreen forest stand, while an uncrewed aerial vehicle (UAV) system, equipped with up- and down-looking shortwave radiation sensors, was used to measure land surface albedo (LSA) above alpine, sub alpine and boreal forest stands. These in-situ measurements were combined with point-scale simulations of the Community Land Model Version 5.0 (CLM5), enabling process level assessment of algorithms used within global climate modelling frameworks. Analysis of diurnal radiation patterns, obtained via the cable car and UAV systems, revealed canopy structural shading of the snow surface as a main control on both the sub-canopy shortwave radiation budget and overall LSA. Furthermore, diurnal patterns of measured LSA revealed a strong dependency on both solar azimuth and zenith angles. Corresponding CLM5 simulations did not adequately represent the measured spatial and temporal variability in LSA and sub-canopy incoming shortwave radiation. In sparse forested environments, CLM5 performed especially poorly, as LSA was overestimated by up to 66%. The use of effective Plant Area Index (PAI) values as a simple first-order correction for this discrepancy between measured and simulated LSA substantially improved model results (64-76% RMSE reduction). That being said, such large biases suggest the need for a more robust solution, especially as the use of effective PAI values did not improve the ability of CLM5 to replicate diurnal variability in LSA and sub-canopy shortwave radiation. Hence, a time-varying transmissivity for direct shortwave radiation was integrated into CLM5, meaning that directionality of solar irradiance could be taken into account. Results with this modified version of CLM5 showed measured variability of sub-canopy incoming shortwave radiation was replicated more accurately, suggesting this approach may help to decrease uncertainty in LSA simulations across seasonally snow-covered forested environments. This has far reaching implications for simulations of the snow albedo feedback strength over the entire Northern Hemisphere Extratropics

Thermal Imagery in Plant Phenotyping: Assessing Stomatal Conductance through Energy Balance Modelling

The importance of temperature data in plant phenotyping applications is well known as is the difficulty of correlating temperature to plant behaviours. This work investigates the emission of thermal radiation from plant leaves to validate non-contact temperature measurements as well as modelling approaches to extend the use of temperature data obtained continuously from outdoor field crops. Temperature data and weather data are combined to calculate a stomatal resistance to water loss to satisfy an energy balance. Several approaches to modelling an energy balance and their results are compared and contrasted

A model to estimate daily albedo from remote sensing data : accuracy assessment of MODIS MCD43 product

L’albedo superficial és un paràmetre físic que afecta al clima de la Terra i, a més, suposa una de les majors incerteses radiatives en l’actual modelització climàtica. Aquest paràmetre és molt variable tant a nivell espacial com temporal degut als canvis en les propietats de les superfícies i als canvis en les condicions d’il•luminació. En conseqüència, es requereix una resolució temporal diària de l’albedo per a realitzar estudis climàtics. L’augment de la resolució espacial dels models climàtics fa necessari l’estudi de les seues característiques espacials a nivell global. La teledetecció proporciona l’única opció pràctica de proporcionar dades d’albedo a nivell global amb alta qualitat i alta resolució tant espacial com temporal. El sensor MODerate Resolution Imaging Spectroradiometer (MODIS) a bord dels satèl•lits Terra i Aqua presenta unes característiques adequades per a l’estimació d’aquest paràmetre. En el present treball realitzem diversos estudis buscant les possibles fonts d’incertesa del producte oficial d’albedo de MODIS (MCD43). A més, presentem un model que millora la resolució temporal d’aquest paràmetre.Surface albedo is a critical land physical parameter affecting the earth’s climate and is among the main radiative uncertainties in current climate modelling. This parameter is highly variable in space and time, both as a result of changes in surface properties and as a function of changes in the illumination conditions. Consequently, an albedo daily temporal resolution is required for climate studies. The increasing spatial resolution of modern climate models makes it necessary to examine its spatial features. Satellite remote sensing provides the only practical way of producing high-quality global albedo data sets with high spatial and temporal resolutions. The MODerate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Terra and Aqua satellites presents the required sampling characteristics in order to derive the this parameter. In this PhD we develop several studies looking for the improvement of the official MODIS albedo product (MCD43) accuracy. Moreover, we present a model that improves the temporal resolution of this parameter

Anwendungen zur Abschätzung des Strahlungseinflusses von Aerosolen

The aim of this PhD research is to contribute to a better estimation of the radiation budget of the Earth and the atmosphere by delving into the further understanding of physical phenomena of the atmosphere. The studied phenomena are the atmospheric radiative transfer and aerosols. The radiative transfer code MOMO (Matrix Operator Model) has been extended from shortwave [0.2 – 4 μm] to the full spectral range [0.2 – 100 μm] in order to obtain a versatile radiative transfer code that can be used for different radiative transfer studies (e.g. inversion of remote sensing measurements, optimization and calibration of measurement instruments and methods, estimation of radiative transfer fluxes, estimation of radiative forcings and heating rates), with different exigencies of precision and rapidity and over the full spectral range. The extension of MOMO to the full range consisted of the integration of the emission of thermal infrared radiation by gases, aerosols and clouds into the matrix operator algorithm of the code. The extension of MOMO also required the development of a spectroscopy module for the modeling of the water vapor continuum of absorption in the thermal infrared. In MOMO, the gas transmission for spectral bands is modeled by means of a k-distribution method. This k-distribution algorithm has also been extended to the thermal infrared and now includes the gas emission of radiation. In a second step, MOMO has been applied in a study on the contribution of aerosols to the radiation budget. This application has been carried out in 3 steps: 1) The characterization of the aerosols by means of observations on a regional scale (measurement campaign or spaceborne measurements). 2) The development of a radiative transfer scheme with radiative transfer code MOMO in its full range version. 3) The estimation of the radiative fluxes and of instant aerosol radiative forcings and heating-rates. The results of this work demonstrate the importance of the instrumental synergy of in-situ measurements and lidar remote sensing for the characterization of aerosol microscopic properties (refractive index and size distribution). The latter method was applied to aerosols in the Mediterranean basin within the measurement campaign TRAQA. The results have revealed the differences between pollution aerosols and desert dust aerosols regarding their microscopic and radiative properties. Further case studies have shown that the presence of clouds below the aerosols has a decisive influence on the sign and on the order of magnitude of aerosol direct radiative forcing.Das Ziel dieser Doktorarbeit ist das Verständnis von physikalischen atmosphärischen Phänomenen zu vertiefen, um eine bessere Abschätzung der Strahlungsbilanz zu erhalten. Die Phänomene, die untersucht wurden sind der Strahlungstransport in der Atmosphäre und die Aerosole. Das Strahlungstransportprogramm MOMO (Matrix-Operator Method) wurde vom kurzwelligen Spektralbereich [0.2 – 4 μm] zum gesamten Spektralbereich [0.2 – 100 μm] erweitert. Dadurch erhielten wir ein Programm, das für Strahlungstransportsimulierungen unterschiedlicher Art und ohne spektrale Einschränkung verwendet werden kann. Die Erweiterung des Spektralbereiches MOMOs besteht in der Implementierung der Strahlungsemission von Gasen, Aerosolen und Wolken in den Matrix-Operator Algorithmus des Programms. Für die Erweiterung des Programms zum langwelligen Spektralbereich wurde auch ein spektroskopisches Modul entwickelt, um das Absorptionskontinuum von Wasserdampf im thermischen infraroten Spektralbereich zu modellieren. Innerhalb von MOMO wird die Transmission von Gasen für breite Spektralbände anhand einer sogenannten „k-Verteilung Methode“ modelliert. Des Weiteren wurde der k-Verteilungsalgorithmus MOMOs zum thermischen Infrarot erweitert, um die Strahlungsemission von Gasen zu berücksichtigen. In dieser Arbeit wurde MOMO verwendet, um den Beitrag der Aerosole zur Strahlungsbilanz abzuschätzen. Die Studie wurde in 3 Schritten durchgeführt: 1) Die Charakterisierung der Aerosole anhand von Beobachtungen auf der regionalen Skala (aus Messkampagnen oder Satellitendaten). 2) Die Entwicklung eines Schemas zur Strahlungssimulation mit der neuen Version MOMOs als Kern. 3) Die Abschätzung der Strahlungsflüsse und des Strahlungsantriebes und der Heizrate der Aerosole. Die Ergebnisse dieser Studie zeigen wie effizient die Synergie von in-situ Messungen und LIDAR-Messungen für die Charakterisierung der mikroskopischen Eigenschaften der Aerosole ist. Diese Methode wurde innerhalb der Messkampagne TRAQA (Aerosole in der Region des Mittelmeeres) zur Datenauswertung verwendet. Die Ergebnisse zeigen große Unterschiede zwischen Verschmutzungsaerosolen und Wüstenaerosolen bezüglich ihrer mikroskopischen Eigenschaften und Strahlungseigenschaften. Weitere Fallstudien in dieser Arbeit haben gezeigt, dass Wolken unter Aerosolschichten einen entscheidenden Einfluss auf sowohl das Vorzeichen als auch den Betrag des Strahlungsantriebes der Aerosole haben

Theoretical and observational comparison of cirrus cloud radiative properties, A

August 1989.Includes bibliographical references.Sponsored by NSF ATM-8812353.Sponsored by NSF ATM-8519160.Sponsored by DOD AFOSR-88-0143.Sponsored by ONR N00014-87-K-0228/P00001