Multi-temporal evaluation of soil moisture and land surface temperature dynamics using in situ and satellite observations

Soil moisture (SM) is an important component of the Earth’s surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth’s surface SM using L-band radiometers: ESA’s Soil Moisture and Ocean Salinity (SMOS) and NASA’s Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites, such as NASA’s Terra/Aqua MODIS or ESA & EUMETSAT’s MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011–2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM–LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R ˜ -0.6 to -0.8), and between SMOS SM and MODIS LST Terra/Aqua day (R ˜ - 0.7). At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R ˜ -0.5 to -0.7; satellite R ˜ -0.4 to -0.7) indicating SM–LST coupling, than in winter (in situ R ˜ +0.3; satellite R ˜ -0.3) indicating SM–LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ~0.12 m3/m3 , stable over the study period and consistent between in situ and satellite observations. A better understanding of the SM–LST link could not only help improving the representation of LST in current hydrological and climate prediction models, but also refining SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status.Peer ReviewedPostprint (published version

Harmonization of remote sensing land surface products : correction of clear-sky bias and characterization of directional effects

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Deteção Remota), Universidade de Lisboa, Faculdade de Ciências, 2018Land surface temperature (LST) is the mean radiative skin temperature of an area of land resulting from the mean energy balance at the surface. LST is an important climatological variable and a diagnostic parameter of land surface conditions, since it is the primary variable determining the upward thermal radiation and one of the main controllers of sensible and latent heat fluxes between the surface and the atmosphere. The reliable and long-term estimation of LST is therefore highly relevant for a wide range of applications, including, amongst others: (i) land surface model validation and monitoring; (ii) data assimilation; (iii) hydrological applications; and (iv) climate monitoring. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Satellite LST products generally rely on measurements in the thermal infrared (IR) atmospheric window, i.e., within the 8-13 micrometer range. Beside the relatively weak atmospheric attenuation under clear sky conditions, this band includes the peak of the Earth’s spectral radiance, considering surface temperature of the order of 300K (leading to maximum emission at approximately 9.6 micrometer, according to Wien’s Displacement Law). The estimation of LST from remote sensing instruments operating in the IR is being routinely performed for nearly 3 decades. Nevertheless, there is still a long list of open issues, some of them to be addressed in this PhD thesis. First, the viewing position of the different remote sensing platforms may lead to variability of the retrieved surface temperature that depends on the surface heterogeneity of the pixel – dominant land cover, orography. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should correspond to the ensemble directional radiometric temperature of all surface elements within the FOV. In this thesis, a geometric model is presented that allows the upscaling of in situ measurements to the any viewing configuration. This model allowed generating a synthetic database of directional LST that was used consistently to evaluate different parametric models of directional LST. Ultimately, a methodology is proposed that allows the operational use of such parametric models to correct angular effects on the retrieved LST. Second, the use of infrared data limits the retrieval of LST to clear sky conditions, since clouds “close” the atmospheric window. This effect introduces a clear-sky bias in IR LST datasets that is difficult to quantify since it varies in space and time. In addition, the cloud clearing requirement severely limits the space-time sampling of IR measurements. Passive microwave (MW) measurements are much less affected by clouds than IR observations. LST estimates can in principle be derived from MW measurements, regardless of the cloud conditions. However, retrieving LST from MW and matching those estimations with IR-derived values is challenging and there have been only a few attempts so far. In this thesis, a methodology is presented to retrieve LST from passive MW observations. The MW LST dataset is examined comprehensively against in situ measurements and multiple IR LST products. Finally, the MW LST data is used to assess the spatial-temporal patterns of the clear-sky bias at global scale.Fundação para a Ciência e a Tecnologia, SFRH/BD/9646

Estimación de flujos de agua entre suelo, vegetación y atmósfera mediante teledetección = Water fluxes estimation between soil, vegetation and atmosphere using remote sensing

En la frontera entre la superficie terrestre y la atmósfera se producen numerosos procesos físicos relacionados con el ciclo hidrológico. Cuando se producen precipitaciones en forma de lluvia, y el agua alcanza la superficie terrestre, una parte llega al suelo y otra parte puede ser interceptada por la vegetación. La fracción que llega al suelo se infiltra en la zona no saturada donde se almacena, lo humedece, disuelve los elementos que son absorbidos posteriormente por la vegetación y modifica las propiedades físicas del suelo. Para que la vegetación pueda desarrollarse es necesario que la planta abra los estomas, absorba CO2 y realice la fotosíntesis. Durante este proceso se produce una pérdida de agua a través de la hoja, que si es lo suficientemente grande puede llegar a hacer que la planta marchite si no es capaz de reponerla del suelo. El agua del suelo es devuelta a la atmósfera posteriormente mediante la evaporación y la transpiración de las plantas. La primera parte del trabajo se ha centrado en la estimación de parámetros biofísicos y estructurales de la vegetación, concretamente los relacionados con el contenido de agua. Para ello se han empleado numerosos datos recogidos en campo a lo largo de dos años fenológicos completos y se relacionaron con las medidas espectrales a dos escalas diferentes, campo y sensor MODIS (500 m). El contenido de agua se calculó usando tres métricas diferentes calculadas a partir de la misma muestra, el Contenido de Humedad de la Vegetación (FMC), el Espesor Equivalente de Agua (EWT) y el Contenido de Agua del Dosel (CWC). Además se usaron dos estimaciones a partir de Modelos de Transferencia Radiativa (RTM) para la obtención del FMC y CWC que fueron comparados con las obtenidas a partir de los modelos empíricos creados a partir los índices espectrales. Otras variables relacionadas como el contenido de materia seca (Dm) y el índice de área foliar (LAI) fueron también evaluadas usando índices de vegetación. Entre los resultados destacables de este estudio se encuentran en primer lugar los relacionados con el protocolo de recogida de datos en campo. En este estudio se obtuvieron evidencias de que las diferencias temporales a la hora de recoger datos en campo son más importantes que las diferencias espaciales en este ecosistema. Además se demostró la necesidad de mostrar consistencia en el protocolo de muestreo: tamaño de la muestra, hora de recogida de las muestras, etc. y en la importancia de evitar, en lo posible la toma de decisiones, generalmente subjetivas, por parte de los operadores de campo. Otro resultado destacable ha sido demostrar la existencia de una alta variabilidad del Dm a lo largo del año. Esto indica que asumir, como sugieren algunos autores, un valor constante de Dm para la estimación del espesor equivalente de agua a partir del contenido de humedad de la vegetación no es una opción viable en este ecosistema. De los índices de vegetación que fueron comparados en el estudio, el que presentó menores correlaciones fue el Índice de Vegetación Resistente a la Atmósfera (VARI). Se observaron algunas diferencias en el comportamiento de los modelos empíricos obtenidos con MODIS y las producidas a partir de medidas espectrales de campo, obteniendo resultados algo mejores en el caso de MODIS. Este hecho posiblemente sea debido a que las adquisiciones de del sensor MODIS presentan diferentes ángulos de observación, lo que reduce la proporción de suelo captada por el sensor y por lo tanto capturando una mayor proporción del dosel. La comparación entre los modelos empíricos y las estimaciones a partir de RTM demostró que en este caso los modelos empíricos aún mejoran las estimaciones de los modelos físicos desarrollados en zonas similares para estimar el contenido de humedad de la vegetación. La segunda parte del trabajo se ha centrado en la estimación del contenido de humedad del suelo combinando datos ópticos y térmicos mediante el cálculo del Índice de Temperatura y Sequedad de la Vegetación (TVDI) cuya obtención se basa en la técnica del triángulo. Se han investigado diferentes factores que afectan a la definición del triángulo, y cómo estos afectan los valores del TVDI y a su relación final con el contenido de humedad del suelo. En este trabajo se introdujo una modificación al cálculo del TVDI en la que se sustituyó el Índice de Vegetación de Diferencia Normalizada (NDVI) por el Índice de Diferencia Infraroja Normalizada (NDII). Esta modificación se tradujo en una mejora en el comportamiento de los modelos empíricos para estimar el contenido de humedad del suelo. Finalmente en la tesis se investiga el comportamiento de la EF en la zona de estudio y su estimación a partir de teledetección. El principal motivo del empleo de la EF es que ha sido ampliamente utilizada para estimar la evapotranspiración diaria, asumiendo que la EF es constante a lo largo del día. A partir de las medidas recogidas por una torre de flujos se han evaluado las variaciones diarias y se han validado las estimaciones de EF calculadas a partir de imágenes Landsat. Se ha usado una nueva versión modificada de la técnica del triángulo en la que se ha introducido el índice de área foliar adaptado a la escala Landsat a partir del producto MODIS (de 1 km a 30 m) como sustituto del índice de vegetación. Además se muestra un innovador método basado en las estadísticas propias del triángulo para la selección de las fechas a incluir en el análisis estadístico. La validación de las estimaciones de EF se ha llevado a cabo de dos maneras diferentes: usando las contribuciones de todos los pixeles incluidos en la zona de influencia de la torre; y utilizando el valor del único pixel correspondiente a la localización de la torre, mostrando ambas aproximaciones escasas diferencias en cuanto a resultados. Además se han comparado las EF diarias y la correspondiente a la hora de la pasada de Landsat sobre la zona de estudio. En este caso se observaron mayores diferencias, lo cual indica que el supuesto de una EF constante a lo largo del día ha de ser tomada con ciertas precauciones si el objetivo final es el cálculo de la evapotranspiración diaria

Critical Evaluations Of Modis And Misr Satellite Aerosol Products For Aerosol Modeling Applications

The study of uncertainties in satellite aerosol products is essential to aerosol data assimilation and modeling efforts. In this study, with the assistance of ground- based observations, uncertainties in Moderate Resolution Imaging Spectroradiometer (MODIS) collection 5 Deep Blue (DB), Multi-Angle Imaging Spectroradiometer (MISR) version 22 aerosol products, and the newly released collection 6 Dark Target over-ocean and DB products were evaluated. For each product, systematic biases were analyzed against observing conditions. Empirical correction procedures and data filtering steps were generated to develop noise and bias reduced DA-quality aerosol products for modeling related applications. Special attention was also directed at the potential low bias in satellite aerosol optical depth (AOD) climatology due to misclassification of aerosols as clouds over Asia. A heavy aerosol identifying system (HAIS) was developed through the combined use of the Ozone Monitoring Instrument (OMI) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products for detecting heavy smoke aerosol plumes. Upon extensive evaluation, HAIS was applied to one year of collocated OMI, CALIOP, and MODIS data to study the misclassifications related low bias. This study suggests that the misclassification of heavy smoke aerosol plumes by MODIS is rather infrequent and thus introduces an insignificant low bias to its AOD climatology. Still, this study confirms that misclassification happens in both active- and passive- based satellite aerosol products and needs to be studied for forecasting these events

Exploring Aerosols near Clouds with High-Spatial-Resolution Aircraft Remote Sensing During SEAC4RS

Since aerosols are important to our climate system, we seek to observe the variability of aerosol properties within cloud systems. When applied to the satelliteborne Moderateresolution Imaging Spectroradiometer (MODIS), the Dark Target retrieval algorithm provides global aerosol optical depth (AOD; at 0.55 m) in cloudfree scenes. Since MODIS' resolution (500m pixels, 3 or 10km product) is too coarse for studying nearcloud aerosol, we ported the Dark Target algorithm to the highresolution (~50m pixels) enhancedMODIS Airborne Simulator (eMAS), which flew on the highaltitude ER2 during the Studies of Emissions, Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys Airborne Science Campaign over the United States in 2013. We find that even with aggressive cloud screening, the ~0.5km eMAS retrievals show enhanced AOD, especially within 6 km of a detected cloud. To determine the cause of the enhanced AOD, we analyze additional eMAS products (cloud retrievals and degradedresolution AOD), coregistered Cloud Physics Lidar profiles, MODIS aerosol retrievals, and groundbased Aerosol Robotic Network observations. We also define spatial metrics to indicate local cloud distributions near each retrieval and then separate into nearcloud and farfromcloud environments. The comparisons show that low cloud masking is robust, and unscreened thin cirrus would have only a small impact on retrieved AOD. Some of the enhancement is consistent with clearcloud transition zone microphysics such as aerosol swelling. However, 3D radiation interaction between clouds and the surrounding clear air appears to be the primary cause of the high AOD near clouds

Synergistic optical and microwave remote sensing approaches for soil moisture mapping at high resolution

Aplicat embargament des de la data de defensa fins al dia 1 d'octubre de 2022Soil moisture is an essential climate variable that plays a crucial role linking the Earth’s water, energy, and carbon cycles. It is responsible for the water exchange between the Earth’s surface and the atmosphere, and provides key information about soil evaporation, plant transpiration, and the allocation of precipitation into runoff, surface flow and infiltration. Therefore, an accurate estimation of soil moisture is needed to enhance our current climate and meteorological forecasting skills, and to improve our current understanding of the hydrological cycle and its extremes (e.g., droughts and floods). L-band Microwave passive and active sensors have been used during the last decades to estimate soil moisture, since there is a strong relationship between this variable and the soil dielectric properties. Currently, there are two operational L-band missions specifically devoted to globally measure soil moisture: the ESA’s Soil Moisture and the Ocean Salinity (SMOS), launched in November 2009; and the NASA’s Soil Moisture Active Passive (SMAP), launched in January 2015. The spatial resolution of the SMOS and SMAP radiometers, in the order of tens of kilometers (~40 km), is adequate for global applications. However, to fulfill the needs of a growing number of applications at local or regional scale, higher spatial detail (< 1 km) is required. To bridge this gap and improve the spatial resolution of the soil moisture maps, a variety of spatial enhancement or spatial (sub-pixel) disaggregation approaches have been proposed. This Ph.D. Thesis focuses on the study of the Earth’s surface soil moisture from remotely sensed observations. This work includes the implementation of several soil moisture retrieval techniques and the development, implementation, validation and comparison of different spatial enhancement or downscaling techniques, applied at local, regional, and continental scale. To meet these objectives, synergies between several active/passive microwave sensors (SMOS, SMAP and Sentinel-1) and optical/thermal sensors (MODIS) have been explored. The results are presented as follows: - Spatially consistent downscaling approach for SMOS using an adaptive moving window A passive microwave/optical downscaling algorithm for SMOS is proposed to obtain fine-scale soil moisture maps (1 km) from the native resolution (~40 km) of the instrument. This algorithm introduces the concept of a shape-adaptive window as a central improvement of the disaggregation technique presented by Piles et al. (2014), allowing its application at continental scales. - Assessment of multi-scale SMOS and SMAP soil moisture products across the Iberian Peninsula The temporal and spatial characteristics of SMOS and SMAP soil moisture products at coarse- and fine-scales are assessed in order to learn about their distinct features and the rationale behind them, tracing back to the physical assumptions they are based upon. - Impact of incidence angle diversity on soil moisture retrievals at coarse and fine scales An incidence angle (32.5°, 42.5° and 52.5°)-adaptive calibration of radiative transfer effective parameters single scattering albedo and soil roughness has been carried out, highlighting the importance of such parameterization to accurately estimate soil moisture at coarse-resolution. Then, these parameterizations are used to examine the potential application of a physically-based active-passive downscaling approach to upcoming microwave missions, namely CIMR, ROSE-L and Sentinel-1 Next Generation. Soil moisture maps obtained for the Iberian Peninsula at the three different angles, and at coarse and fine scales are inter-compared using in situ measurements and model data as benchmarks.La humedad del suelo es una variable climática esencial que juega un papel crucial en la relación de los ciclos del agua, la energía y el carbono de la Tierra. Es responsable del intercambio de agua entre la superficie de la Tierra y la atmósfera, y proporciona información crucial sobre la evaporación del suelo, la transpiración de las plantas y la distribución de la precipitación en escorrentía, flujo superficial e infiltración. Por lo tanto, es necesaria una estimación precisa de la humedad del suelo para mejorar las predicciones climáticas y meteorológicas, y comprender mejor el ciclo hidrológico y sus extremos (v.g., sequías e inundaciones). Los sensores pasivos y activos en banda L se han usado durante las últimas décadas para estimar la humedad del suelo debido a la relación directa que existe entre esta variable y las propiedades dieléctricas del suelo. Actualmente, hay dos misiones operativas en banda L específicamente dedicadas a medir la humedad del suelo a escala global: la misión Soil Moisture and Ocean Salinity (SMOS) de la ESA, lanzada en noviembre de 2009; y la misión Soil Moisture Active Passive (SMAP) de la NASA, lanzada en enero de 2015. La resolución espacial de los radiómetros SMOS y SMAP, del orden de unas decenas de kilómetros (~40 km), es adecuada para aplicaciones a escala global. Sin embargo, para satisfacer las necesidades de un número creciente de aplicaciones a escala local o regional, se requiere más detalle espacial (<1 km). Para solventar esta limitación y mejorar la resolución espacial de los mapas de humedad, se han propuesto diferentes técnicas de mejora o desagregación espacial. Esta Tesis se centra en el estudio de la humedad de la superficie terrestre a partir de datos obtenidos a través de teledetección. Este trabajo incluye la implementación de distintos algoritmos de recuperación de la humedad del suelo y el desarrollo, implementación, validación y comparación de distintas técnicas de desagregación, aplicadas a escala local, regional y continental. Para cumplir estos objetivos, se han explorado sinergias entre diferentes sensores de microondas activos/pasivos (SMOS, SMAP y Sentinel-1) y sensores ópticos/térmicos. Los resultados se presentan de la siguiente manera: - Técnica de desagregación espacialmente consistente, basada en una ventana móvil adaptativa, aplicada a los datos SMOS Se propone un algoritmo de desagregación del píxel basado en datos obtenidos de medidas radiométricas de microondas en banda L y datos ópticos, para mejorar la resolución espacial de los mapas de humedad del suelo desde la resolución nativa del instrumento (~40 km) hasta resoluciones de 1 km. El algoritmo introduce el concepto de una ventana de contorno adaptativo, como mejora principal sobre la técnica de desagregación presentada en Piles et al. (2014), permitiendo su implementación a escala continental. - Análisis multiescalar de productos de humedad del suelo SMAP y SMOS sobre la Península Ibérica Se han evaluado las características temporales y espaciales de distintos productos de humedad del suelo SMOS y SMAP, a baja y a alta resolución, para conocer sus características distintivas y comprender las razones de sus diferencias. Para ello, ha sido necesario rastrear los supuestos físicos en los que se basan. - Impacto del ángulo de incidencia en la recuperación de la humedad del suelo a baja y a alta resolución Se ha llevado a cabo una calibración adaptada al ángulo de incidencia (32.5°, 42.5° y 52.5°) de los parámetros efectivos, albedo de dispersión simple y rugosidad del suelo, descritos en el modelo de transferencia radiativa � − �, incidiendo en la importancia de esta parametrización para estimar la humedad del suelo de forma precisa a baja resolución. El resultado de las mismas se ha utilizado para estudiar la potencial aplicación de un algoritmo activo/pasivo de desagregación basado en la física para las próximas misiones de microondas, llamadas CIMR, ROSE-L y Sentinel-1 Next Generation. Los mapas de humedad recuperados a los tres ángulos de incidencia, tanto a baja como a alta resolución, se han obtenido para la Península Ibérica y se han comparado entre ellos usando como referencia mediciones de humedad in situ.Postprint (published version

Examination of the behavior of modis-retrieved cloud droplet effective radius through misr-modis data fusion

Listed as one of the Essential Climate Variables by the Global Climate Observing System, the effective radius (Re) of the cloud drop size distribution plays an important role in the energy and water cycles of the Earth system. Re is retrieved from several passive sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), based on a visible and near-infrared bi-spectral technique that had its foundation more than a quarter century ago. This technique makes a wide range of assumptions, including 1-D radiative transfer, assumed single-mode drop size distribution, and cloud horizontal and vertical homogeneity. It is well known that deviations from these assumptions lead to bias in the retrieved Re. Recently, an effort to characterize the bias in MODIS-retrieved Re through MISR-MODIS data fusion revealed biases in the zonal-mean values of MODIS-retrieved Re that varied from 2 to 11 µm, depending on latitude [Liang et al., 2015]. Here, in a push towards bias-correction of MODIS-retrieved Re, we further examine the bias with MISR-MODIS data fusion as it relates to other observed cloud properties, such as cloud horizontal heterogeneity, cloud optical depth, and sun-view geometry. Our results reveal that while Re bias do show a certain degree of dependence on some properties, no single property dominates the behavior in the MODIS-retrieved Re bias. Through data stratification by observed cloud properties and latitude, we introduce a bias-correction approach for MODIS-retrieved Re at regional scales. Our estimates reveal global distribution of MODIS-retrieved Re monthly mean bias ~1 to 12 μm depending on latitude and cloud types, the bias-corrected Re estimates of ~ 4 to 16 μm are consistent with available validations of MODIS Re reported in previous studies over limited regions. Removing the mean bias from the original MODIS Re2.1 and Re3.7 monthly means show more consistent behavior among the two channels that range from 0 to +0.6 μm in the marine stratocumulus regions and -2 to 0 μm in the cumuliform cloud regions. This curious finding seems to suggest that the vertical distribution of drop sizes for marine stratocumulus clouds are very different from other types of marine liquid water clouds

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

A Multi-Scale Assessment of Solar-Induced Chlorophyll Fluorescence and Its Relation to Northern Hemisphere Forest Productivity

Photosynthesis, or gross primary productivity (GPP), plays a critical role in the global carbon cycle, since it is the sole pathway for carbon fixation by the biosphere. Quantifying GPP across multiple spatial scales is needed to improve our understanding of current and future behavior of biosphere-atmosphere carbon exchange and subsequent feedbacks on the climate system. Remote sensing represents one method to observe vegetation properties and processes, and solar-induced chlorophyll fluorescence (SIF), a light signal originating from leaves, has been shown to be proportional to GPP on diurnal and seasonal timescales. Recently, new techniques to retrieve SIF from satellite observations have provided an unprecedented opportunity to study GPP on a global scale. The relationship between SIF and GPP, however, is subject to significant uncertainty as it is influenced by a number of ecosystem traits (e.g. plant species, canopy structure, leaf age). In this dissertation, I evaluate SIF signals and their relation to GPP over Northern Hemisphere forest ecosystems. First, I compare climate-driven variations in satellite-based SIF to both longstanding satellite vegetation indices derived from reflected sunlight and tower-based estimates of GPP. Even when aggregated regionally, interannual variability (IAV) of SIF is found to be subject to low signal-to-noise performance, particularly during summer. However, through a statistical analysis, I show that increases in springtime temperature driven by warmer temperatures are offset by drier, less productive conditions later in the growing season. Summer productivity, however, is more strongly correlated with moisture than with temperature, suggesting that moisture exerts a greater influence on growing season-integrated signals. While these results demonstrate that satellite observations can be used to reveal meaningful carbon-climate interactions, they also show that currently available satellite observations of SIF do not allow for robust studies of IAV at scales comparable to surface-based observations. To investigate how SIF signals are related to ecosystem function at a local scale, I built and deployed a PhotoSpec spectrometer system to the AmeriFlux tower at the University of Michigan Biological Station (US-UMB) above a temperate deciduous forest. These observations show a strong correlation between SIF and GPP at diurnal and seasonal timescales, but SIF is more closely tied to solar radiation and exhibits a delayed response to water stress-induced losses in summer GPP. This decoupling during drought highlights the challenges in using SIF to detect changes in summertime productivity. However, an increased ratio between red and far-red SIF during drought indicates that the combination of SIF at multiple wavelengths may improve the detection of water stress. Lastly, I explore diurnal and directional aspects of the SIF signal. Observations of SIF are sensitive to sun-sensor geometry, with smaller incident angles (between solar and viewing angles) leading to stronger signals. However, afternoon SIF is typically lower than morning values at equivalent light levels due to ecosystem downregulation, which obfuscates angular dependencies in the afternoon. While satellite observations typically rely on a clear-sky sunlight proxy to scale instantaneous observations of SIF to daily values, these results demonstrate the need to account for sounding geometry and diurnal hysteresis in SIF signals in order to advance the interpretation of satellite observations. Overall, my results provide a multiscale assessment of SIF over Northern Hemisphere forests and emphasize that careful attention must be given to the spatial and temporal scales at which SIF can be used to make inferences about GPP.PHDAtmospheric, Oceanic & Space ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168011/1/zbutterf_1.pd