The land surface is a key component of the climate system and exchanges energy, water and carbon with the overlying atmosphere. It is the location of the terrestrial carbon sink and changes in the land surface can impact weather and climate at various time and spatial scales. It's ability to act as a source or a sink can influence atmospheric CO2 concentrations. Both models and observations have shown the reduced ability of the land surface to absorb increased anthropogenic CO2 emissions with results from the Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP) and phase 5 of the Coupled Model Intercomparison Project (CMIP5) have shown that the terrestrial carbon cycle is a major source of model uncertainty. Land surface models (LSMs) represent the interaction between the biosphere and atmosphere in earth system models (ESMs) and are important for simulating the terrestrial carbon cycle. In the context of land surface modelling, uncertainty arises from an incomplete understanding of land surface processes and the inability to model these processes correctly. As LSMs become more advanced, there is a need to understand their accuracy. In this thesis, the ability of the Joint UK Land Environment Simulator (JULES), the land surface scheme of the UK Met Office United Model, to simulate Gross Primary Productivity (GPP) fluxes is evaluated at various spatial scales (point, regional and global) in order to identify and quantify sources of uncertainty in the model. This thesis has three main objectives. Firstly, JULES is evaluated at the point scale across a range of biomes and climatic conditions using local (site-specific), global and satellite datasets. It was found that JULES is biased with total annual GPP underestimated by 16% and 30% across all sites compared to observations when using local and global data, respectively. The model's phenology module was tested by comparing results from simulations using the default phenology model to those forced with leaf area index (LAI) from the MODIS sensor. Model parameters were found to be a minor source of uncertainty compared to the meteorological driving data at the point scale as was the default phenology module in JULES. Secondly, in addition to evaluating simulated GPP fluxes at the point scale, the ability of JULES to simulate GPP at the global and regional scale for 2000-2010 was investigated with being able to simulate interannual variability and simulated global GPP estimates were found to be greater than the observation-based estimates, FLUXNET-MTE and MODIS, by 8% and 25%, respectively. At the regional scale, differences in GPP between JULES, FLUXNET-MTE and MODIS were observed mostly in the tropics and this was the reason for differences at the global scale. Simulating tropical GPP was found to be a major source of uncertainty in JULES. JULES was found to be insensitive to spatial resolution and when driven with the PRINCETON meteorological dataset, differences between model simulations driven using WFDEI-GPCC and PRINCETON occurred in the tropics (at 5°N-5°S) and extratropics (at 30°N-60°N). Finally, the response of JULES to changes in climate (surface air temperature, precipitation, atmospheric CO2 concentrations) was explored at the global and regional scale. Simulated GPP was found to have greater sensitivity to changes in precipitation and CO2 concentrations than air temperature at the global scale while LAI was sensitive only to changes in temperature and insensitive to changes in precipitation and CO2 concentrations. It was found that model sensitivity to climate at the global scale was determined by its behaviour at the regional scale.

The capability to map anthropogenic aerosol quantities and properties over land can provide significant insights for climate and environmental studies on global and regional scales. One of the primary challenges in aerosol information monitoring is separating two signals measured by downward-viewing airborne or spaceborne instruments: the light scattered from the aerosols and light reflected from the Earth's surface. In order to study the aerosols independently, the surface signal needs to be subtracted out from the measurements. Some observational modalities, such as multispectral and multiangle, do not provide enough information to uniquely define the Earth's directional reflectance properties for this task due to the high magnitude and inhomogeneity of albedo for land surface types. Polarization, however, can provide additional information to define surface reflection. To improve upon current measurement capabilities of aerosols over urban areas, Jet Propulsion Laboratory developed the Multiangle SpectroPolarimetric Imager (MSPI) that can accurately measure the Degree of Linear Polarization to 0.5%. In particular, data acquired by the ground-based prototype, GroundMSPI, is used for directional reflectance studies of outdoor surfaces in this dissertation. This work expands upon an existing model, the microfacet model, to characterize the polarized bidirectional reflectance distribution function (pBRDF) of surfaces and validate an assumption, the Spectral Invariance Hypothesis, on the surface pBRDF that is used in aerosol retrieval algorithms. The microfacet model is commonly used to represent the pBRDF of Earth's surface types, such as ocean and land. It represents a roughened surface comprised of randomly oriented facets that specularly reflect incoming light into the upward hemisphere. The analytic form of the pBRDF for this model assumes only a single reflection of light from the microfaceted surface. If the incoming illumination is unpolarized, as it is with natural light from the Sun, the reflected light is linearly polarized perpendicular to the plane that contains the illumination and view directions, the scattering plane. However, previous work has shown that manmade objects, such as asphalt and brick, show a polarization signature that differs from the single reflection microfacet model. Using the polarization ray-tracing (PRT) program POLARIS-M, a numerical calculation for the pBRDF is made for a roughened surface to account for multiple reflections that light can experience between microfacets. Results from this numerical PRT method shows rays that experience two or more reflections with the microfacet surface can be polarized at an orientation that differs from the analytical single reflection microfacet model. This PRT method is compared against GroundMSPI data of manmade surfaces. An assumption made regarding the pBRDF for this microfacet model is verified with GroundMSPI data of urban areas. This is known as the Spectral Invariance Hypothesis and asserts that the magnitude and shape of the polarized bidirectional reflectance factor (pBRF) is the same for all wavelengths. This simplifies the microfacet model by assuming some surface parameters such as the index of refraction are spectrally neutral. GroundMSPI acquires the pBRF for five prominent region types, asphalt, brick, cement, dirt, and grass, for day-long measurements on clear sky conditions. Over the course of each day, changing solar position in the sky provides a large range of scattering angles for this study. The pBRF is measured for the three polarimetric wavelengths of GroundMSPI, 470, 660, and 865nm, and the best fit slope of the spectral correlation is reported. This investigation shows agreement to the Spectral Invariance Hypothesis within 10% for all region types excluding grass. Grass measurements show a large mean deviation of 31.1%. This motivated an angle of linear polarization (AoLP) analysis of cotton crops to isolate single reflection cases, or specular reflections, from multiple scattering cases of light in vegetation. Results from this AoLP method show that specular reflections off the top surface of leaves follow the Spectral Invariance Hypothesis.

Increasing populations, shifting demographics and changes in diet are driving increases in crop production. However, any increases in food demand are ultimately limited by water availability, which is under pressure globally, but especially so in arid and semi-arid regions. To address this challenge, spatially distributed information on crop water use, vegetation health, soil moisture status and a range of other water, energy and carbon variables are all required. However, critical to the determination of many of these processes is an accurate characterization of the land surface temperature (LST). The only feasible manner by which to estimate this variable across a range of spatial and temporal scales is using thermal infrared (TIR) satellite data. Here we investigate the estimation of LST, focusing on its accurate retrieval across a range of different spatial scales. First, we examine the influence of atmospheric correction on retrieval accuracy by employing a radiative transfer model and Landsat data using a variety of available atmospheric profile data, with the aim of identifying an optimal product combination for retrieval. Using these results, we then investigate the potential to downscale coarse resolution (O~103 m) MODIS satellite data to scales appropriate for agricultural application (less than O~102 m), using a machine-learning approach. To further advance the downscaling technique, we explore the utility of novel Cubesat data to produce within-field scale (O~101 m) distributions of land surface temperature. Finally, to expand upon the multi-resolution/multi-satellite LST strategy explored here, we examine the capacity of ultra-high resolution (O~10-1 m) thermal imagery from an unmanned aerial vehicle to characterize surface temperature response and behavior, focusing on the retrieval accuracy and diurnal variability of these spatially and temporally varying land surface temperature estimates. The ultimate goal of this research is to advance the utility of LST for agricultural application by providing description and insights into product development, accuracy issues, and identifying some limitations and opportunities of both current and future remote observation platforms.

Uncertainty in model predictions is associated with data, parameters, and model structure. The estimation of these contributions to uncertainty is a critical issue in hydrology. Using a variety of single and multiple criterion methods for sensitivity analysis and inverse modeling, the behaviors of two state-of-the-art land surface models, the Simple Biosphere Model 3 and Noah model, are analyzed. The different algorithms used for sensitivity and inverse modeling are analyzed and compared along with the performance of the land surface models. Generalized sensitivity and variance methods are used for the sensitivity analysis, including the Multi-Objective Generalized Sensitivity Analysis, the Extended Fourier Amplitude Sensitivity Test, and the method of Sobol. The methods used for the parameter uncertainty estimation are based on Markov Chain Monte Carlo simulations with Metropolis type algorithms and include A Multi-algorithm Genetically Adaptive Multi-objective algorithm, Differential Evolution Adaptive Metropolis, the Shuffled Complex Evolution Metropolis, and the Multi-objective Shuffled Complex Evolution Metropolis algorithms. The analysis focuses on the behavior of land surface model predictions for sensible heat, latent heat, and carbon fluxes at the surface. This is done using data from hydrometeorological towers collected at several locations within the Large-Scale Biosphere Atmosphere Experiment in Amazonia domain (Amazon tropical forest) and at locations in Arizona (semiarid grass and shrub-land). The influence that the specific location exerts upon the model simulation is also analyzed. In addition, the Santarém kilometer 67 site located in the Large-Scale Biosphere Atmosphere Experiment in Amazonia domain is further analyzed by using datasets with different levels of quality control for evaluating the resulting effects on the performance of the individual models. The method of Sobol was shown to give the best estimates of sensitivity for the variance-based algorithms and tended to be conservative in terms of assigning parameter sensitivity, while the multi-objective generalized sensitivity algorithm gave a more liberal number of sensitive parameters. For the optimization, the Multi-algorithm Genetically Adaptive Multi-objective algorithm consistently resulted in the smallest overall error; however all other algorithms gave similar results. Furthermore the Simple Biosphere Model 3 provided better estimates of the latent heat and the Noah model gave better estimates of the sensible heat.

We propose the use of a small unmanned aerial vehicle (UAV) equipped with temperature, pressure, and relative humidity sensors to estimate sensible and latent heat fluxes over an active agricultural area in east-central Mississippi. The Bowen ratio method is applied to vertical soundings from the surface to 120 meters at 10-meter intervals. A number of flights were conducted at Mississippi State University during the late stages of the growing season with the purpose of obtaining heat flux estimates over different land surface/cover types. Results show that the UAV platform is able to provide reasonable heat and moisture flux estimates, and that the fluxes show substantial variability among different land cover types over a small spatial scale. Future work must be done to quantify the diurnal and seasonal changes in heat flux estimates over various crop types and investigate flight plans and sensor mounting options to maximize sensor precision.

The land surface and atmosphere interact through complex feedback loops that link energy and water cycles. Effectively characterizing these linkages is critical to modeling weather and climate extremes accurately. Seasonal variability in vegetation growth and human-driven land cover changes (LCC) can alter the biophysical properties of the land surface, which can in turn influence the water cycle. We quantified the impacts of seasonal variability in vegetation growth on land surface energy and water balances using ecosystem-scale eddy covariance and large aperture scintillometer observations. Our results indicated that the monthly precipitation and seasonal vegetation characteristics such as leaf area index, root length, and stomatal resistance are the main factors influencing ecosystem land surface energy and water balances when soil moisture and available energy are not limited. Using a regional-scale climate model, we examined the effect of LCC and irrigation on summer water cycle characteristics. Changes in biophysical properties due to LCC reducing the evapotranspiration, atmospheric moisture, and summer precipitation over the contiguous United States (CONUS). The combined effects of LCC and irrigation indicated a significant drying over the CONUS, with increased duration and decreased intensity of dry spells, and reduced duration, frequency, and intensity of wet spells. Irrigated cropland areas will become drier due to the added effect of low-precipitation wet spells and long periods (3-4% increase) of dry days, whereas rainfed croplands are characterized by intense (1-5% increase), short-duration wet spells and long periods of dry days. An analysis based on future climate change projections indicated that 3–4 °C of warming and an intensified water cycle will occur over the CONUS by the end of the 21st century. The results of this study highlighted the importance of the accurate representation of seasonal vegetation changes and LCC while forecasting present and future climate. / Doctor of Philosophy / The land surface and atmosphere interact through complex feedback loops that link energy and water cycles. Effectively characterizing these linkages is critical to accurately model weather and climate extremes. We quantified the influence of human-driven land cover change (LCC), in this case, LCC associated with irrigated agriculture, and seasonal vegetation growth on the water cycle using a regional climate model and ecosystem-level observations. Our results indicated that monthly precipitation and seasonal vegetation growth are the main factors influencing land surface energy and water balances when soil moisture and solar energy are not limited. Our results showed that irrigation-related LCC reduced summer precipitation over the contiguous United States (CONUS), with an increased number of dry days (days with less than 1 mm precipitation) and reduced hourly, daily, and summer precipitation totals. Irrigated cropland areas are becoming drier due to the combined effects of low precipitation and long dry days, whereas rainfed croplands are characterized by intense short-duration precipitation and long dry days. Climate change analyses indicate that 3–4 °C of warming and an intensified water cycle will occur over the CONUS by the end of the 21st century. The results of this study highlight the importance of the accurate representation of LCC while forecasting future climate.

Monitoring and understanding plant phenology is becoming an increasingly important way to identify and model global changes in vegetation life cycle events. Although numerous studies have used synoptically sensed data to study phenological patterns at the continental and global scale, relatively few have focused on characterizing the land surface phenology of specific ecosystems. Mountain environments provide excellent examples of how variations in topography, elevation, solar radiation, temperature, and spatial location affect vegetation phenology. High elevation biomes cover twenty percent of the Earth's land surface and provide essential resources to both the human and non-human population. These areas experience limited resource availability for plant growth, development, and reproduction, and are one of the first ecosystems to reflect the harmful impact of climate change. Despite this, the phenology of mountain ecosystems has historically been understudied due to the rough and variable terrain and inaccessibility of the area. Here, we use two MODIS/Terra satellite 16-day products, Vegetation Index and Nadir BRDF Adjusted Reflectance, to assess start of season (SOS) for the 2007 calendar year. Independent data for elevation, slope, aspect, solar radiation, and temperature as well as longitude and latitude were then related to the SOS output. Based on the results of these analyses, we found that SOS can be predicted with a significant R² (0.55-0.64) for each individual zone as well as the entire western mountain range. While both elevation and latitude have significant influences on the timing of SOS for all six study areas. When examined at the regional scale and accounting for aspect, SOS follows closely with Hopkins' findings in regard to both elevation and latitude. / Master of Science

This work introduces diagnostic methods for land surface model (LSM) evaluation that enable developers to identify structural shortcomings in model parameterizations by evaluating model 'signatures' (characteristic temporal and spatial patterns of behavior) in feature, cost-function, and parameter spaces. The ensemble-based methods allow researchers to draw conclusions about hypotheses and model realism that are independent of parameter choice. I compare the performance and physical realism of three versions of Noah LSM (a benchmark standard version [STD], a dynamic-vegetation enhanced version [DV], and a groundwater-enabled one [GW]) in simulating high-frequency near-surface states and land-to-atmosphere fluxes in-situ and over a catchment at high-resolution in the U.S. Southern Great Plains, a transition zone between humid and arid climates. Only at more humid sites do the more conceptually realistic, hydrologically enhanced LSMs (DV and GW) ameliorate biases in the estimation of root-zone moisture change and evaporative fraction. Although the improved simulations support the hypothesis that groundwater and vegetation processes shape fluxes in transition zones, further assessment of the timing and partitioning of the energy and water cycles indicates improvements to the movement of water within the soil column are needed. Distributed STD and GW underestimate the contribution of baseflow and simulate too-flashy streamflow. This work challenges common practices and assumptions in LSM development and offers researchers more stringent model evaluation methods. I show that, because of equifinality, ad-hoc evaluation using single parameter sets provides insufficient information for choosing among competing parameterizations, for addressing hypotheses under uncertainty, or for guiding model development. Posterior distributions of physically meaningful parameters differ between models and sites, and relationships between parameters themselves change. 'Plug and play' of modules and partial calibration likely introduce error and should be re-examined. Even though LSMs are 'physically based,' model parameters are effective and scale-, site- and model-dependent. Parameters are not functions of soil or vegetation type alone: they likely depend in part on climate and cannot be assumed to be transferable between sites with similar physical characteristics. By helping bridge the gap between the model identification and model development, this research contributes to the continued improvement of our understanding and modeling of environmental processes. / text

Esse trabalho tem como objetivo compreender as relações entre a Land Surface Temperature (LST), Normalized Difference Vegetation Índex (NDVI) e os padrões do uso da terra do município de São Paulo no período de 1985 a 2010. Analisou-se 15 bairros, nos quais foram extraídas 45 amostras aleatórias de diferentes padrões de uso da terra; subdivididas em baixo padrão, médio padrão e médio alto padrão. Com o aporte de geotecnologia, foi feita a extração dos dados das imagens de satélite Landsat 5 (TM) e das Ortofotos do ano de 2010. O comportamento das amostras variou de acordo como os diferentes perfis dos grupos analisados. O grupo de baixo padrão foi o que apresentou as maiores amplitudes térmicas, ausência de arborização urbana atreladas a um baixo padrão construtivo. O grupo de médio padrão é caracterizado pela predominância de área verticalizada e apresenta uma arborização urbana escassa em meio a uma malha urbana consolidada. O grupo de médio alto padrão foi o que mais apresentou arborização urbana, distribuída de forma homogênea na maioria das amostras, portanto foi o grupo que teve baixas amplitudes térmicas e o índice de Normalized Difference Vegetation Index (NDVI) com pouca variação. Os testes mostraram fortes correlações negativas entre as amostras de Land Surface Temperature (LST) e o índice de Normalized Difference Vegetation Index (NDVI), sendo -0,58 em 1985, -0,43 em 2004 e -0,82 em 2010. Os diferentes padrões de uso da terra, relacionados à temperatura de superfície, e o índice de vegetação, aliado à preocupação com o planejamento ambiental, deve resultar na melhoria da qualidade de vida da população. Esta pesquisa faz parte do Projeto Temático processo FAPESP 08/58161 -1, \"Assessment of Impacts and Vulnerability to Climate Change in Brazil and strategies for Adaptation options\", Component 5: Vulnerability of the metropolitan region of São Paulo to climate Change. / This study aims to understand the relationship between Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and the patterns of land use in the municipality of São Paulo, from 1985 to 2010. A totoal of 45 random samples were extracted from the 15 districts used in this study, with different patterns of land use which were subdivided into three different clases: low-end, middle and middle-high. Geospatial approaches allowed the extraction of satellite image data from Landsat 5 data (TM) and from Orthophotos from 2010. The behavior of the samples varied accordingly to the different group profiles. The low-end group presented the highest thermal amplitudes and more significant absence of urban vegetation linked, both to low urbanization and construction standards. The average standard group is characterized by the predominance of vertical buildings and lacks urban trees amidst a consolidated urban landscape. The average-high standard group displayed the highest concentration of green urban areas, distributed homogeneously in most samples, so this group presented low variations both in temperature amplitude and in the Normalized Difference Vegetation Index (NDVI). The correlation tests showed strong negative correlations between samples of Land Surface Temperature (LST) and the NDVI samples, of -0.58 in 1985, -0.43 in 2004 and -0.82 in 2010. Understanding the relations between the different patterns of land use, surface temperature and the NDVI (with due concern for environmental planning) is an important step in the identification and rehabilitation of enviromentally. This research is part of the Thematic Project FAPESP 08/58161 -1 process, \"Assessment of Impacts and Vulnerability to Climate Change in Brazil and strategies for Adaptation options\", Component 5: Vulnerability of the metropolitan region of São Paulo to climate Change.

Temperature characteristics of surface using remote sensing methods Abstract The aim of this thesis is to design a methodology for refining the land surface temperature values obtained from Landsat 8 satellite data in areas with diverse land cover. The research section describes factors influencing the radiation of the Earth's surface. Also mentioned are current methods used for processing infrared thermal data and calculate land surface temperature. The practical part describes satellite and airborne data used in the analytical and verification process. All parts of the applied method leading to the subpixel value of the land surface temperature are described in detail in the method part. The results are then compared with airborne verification data with better spatial resolution and with currently used methods. Finally, the pros and cons of this method and its possible improvement in the future are mentioned. Key words: land surface temperature, land surface emissivity, satellite data, Landsat 8, airborne data, subpixel method, Czech Republic