Towards a harmonized long‐term spaceborne record of far‐red solar induced fluorescence

Far‐red solar‐induced chlorophyll fluorescence (SIF) has been retrieved from multiple satellites with nearly continuous global coverage since 1996. Multiple official and research‐grade retrievals provide a means for cross validation across sensors and algorithms, but produces substantial variation across products due to differences in instrument characteristics and retrieval algorithm. The lack of a consistent, calibrated SIF data set hampers scientific interpretation of planetary photosynthesis. NASA's Orbiting Carbon Observatory 2 (OCO‐2) offers small sampling footprints, high data acquisition, and repeating spatially resolved targets at bioclimatically diverse locations, providing a unique benchmark for spaceborne sensors traceable to ground data. We leverage overlap between the longer running Global Ozone Monitoring Instrument version 2 (GOME‐2) SIF time series, and more recent state‐of‐the‐art OCO‐2 and TROPOspheric Monitoring Instrument (TROPOMI) data, in a first attempt to reconcile inconsistencies in the long‐term record. After screening and correcting for key instrument differences (time of day, wavelength, Sun‐sensor geometry, cloud effects, footprint area), we find that Global Ozone Monitoring Instrument version 2 and TROPOspheric Monitoring Instrument perform exceedingly well in capturing spatial, seasonal, and interannual variability across OCO‐2 targets. However, Global Ozone Monitoring Instrument version 2 retrieval methods differ by up to a factor of 2 in signal‐to‐noise and magnitude. Magnitude differences are largely attributed to retrieval window choice, with wider windows producing higher magnitudes. The assumed SIF spectral shape has negligible effect. Substantial research is needed to understand remaining sensitivities to atmospheric absorption and reflectance. We conclude that OCO‐2 and TROPOspheric Monitoring Instrument have opened up the possibility to produce a multidecadal SIF record with well‐characterized uncertainty and error quantification for overlapping instruments, enabling back‐calibration of previous instruments and production of a consistent, research‐grade, harmonized time series

Influence of snow properties on directional surface reflectance in Antarctica

The significance of the polar regions for the Earth’s climate system and their observed amplified response to climate change indicate the necessity for high temporal and spatial coverage for the monitoring of the reflective properties of snow surfaces and their influencing factors. Therefore, the specific surface area (SSA, as a proxy for snow grain size) and the hemispherical directional reflectance factor (HDRF) of snow were measured for a 2-month period in central Antarctica (Kohnen research station) during austral summer 2013/14. The SSA data were retrieved on the basis of ground-based spectral surface albedo measurements collected by the COmpact RAdiation measurement System (CORAS) and airborne observations with the Spectral Modular Airborne Radiation measurement sysTem (SMART). The snow grain size and pollution amount (SGSP) algorithm, originally developed to analyze spaceborne reflectance measurements by the MODerate Resolution Imaging Spectroradiometer (MODIS), was modified in order to reduce the impact of the solar zenith angle on the retrieval results and to cover measurements in overcast conditions. Spectral ratios of surface albedo at 1280 and 1100 nm wavelength were used to reduce the retrieval uncertainty. The retrieval was applied to the ground-based and airborne observations and validated against optical in situ observations of SSA utilizing an IceCube device. The SSA retrieved from CORAS observations varied between 29 and 96 m2 kg-1. Snowfall events caused distinct relative maxima of the SSA which were followed by a gradual decrease in SSA due to snow metamorphism and wind-induced transport of freshly fallen ice crystals. The ability of the modified algorithm to include measurements in overcast conditions improved the data coverage, in particular at times when precipitation events occurred and the SSA changed quickly. SSA retrieved from measurements with CORAS and MODIS agree with the in situ observations within the ranges given by the measurement uncertainties. However, SSA retrieved from the airborne SMART data underestimated the ground-based results. The spatial variability of SSA in Dronning Maud Land ranged in the same order of magnitude as the temporal variability revealing differences between coastal areas and regions in interior Antarctica. The validation presented in this study provided an unique test bed for retrievals of SSA under Antarctic conditions where in situ data are scarce and can be used for testing prognostic snowpack models in Antarctic conditions. The HDRF of snow was derived from airborne measurements of a digital 180° fish-eye camera for a variety of conditions with different surface roughness, snow grain size, and solar zenith angle. The camera provides radiance measurements with high angular resolution utilizing detailed radiometric and geometric calibrations. The comparison between smooth and rough surfaces (sastrugi) showed significant differences in the HDRF of snow, which are superimposed on the diurnal cycle. By inverting a semi-empirical kernel-driven model for the bidirectional reflectance distribution function (BRDF), the snow HDRF was parameterized with respect to surface roughness, snow grain size, and solar zenith angle. This allows a direct comparison of the HDRF measurements with BRDF products from satellite remote sensing

Airborne thermography and ground geophysical investigation for detecting shallow ground disturbance under vegetation

This thesis discusses the potential of airborne thermal prospection for detecting shallow ground disturbance beneath vegetation based on images acquired by the NERC Airborne Thematic Mapper (ATM) at thermal infrared wavelengths. Shallow ground disturbance creates a differential heat flux due to a variation in the thermal properties between disturbed and undisturbed soils. When observed above a canopy, the effect of vegetation growth on the thermal regime of the underlying soils is poorly understood. The research extends current understanding by examining areas where ground disturbance is known to exist under variable vegetation cover at an archaeological site at Bosworth, Leicestershire and areas of abandoned mine activity on Baildon Moor, W. Yorkshire and in the N. Pennine Orefield, Weardale. The investigation focuses on qualitative image interpretation techniques, where anomalies on day and night thermal images are compared with those manifest on the multispectral images, and a more quantitative approach of Apparent Thermal Inertia (ATI) modelling. Physical thermal inertia is a parameter that is sensitive to volumetric variations in the soil, but cannot be measured directly using remote sensing techniques. However, an apparent thermal inertia is determined by examining the day and night temperature contrast of the surface, where spatial variations can signify potential features buried in the near-surface environment. Ground temperature profiling at the Bosworth site indicates that diurnal heat dissipates between 0.20-0.50m at an early stage in vegetation development with progressively lower diurnal amplitudes observed at 0.20m as the vegetation develops. Results also show that the time of diurnal maximum temperature occurs progressively later as vegetation develops, implying an importance for thermal image acquisition. The quantitative investigation concentrates on the Bosworth site where extensive ground geophysical prospection was performed and vertical soil samples extracted across features of variable multispectral, thermal and ATI response to enable comparison of the observed airborne thermal response with physical soil properties. Results suggest that there is a high correlation between ATI and soil moisture properties at 0.15-0.25m depth (R(^2)=0.99) at an early stage in cereal crop development but has a high correlation at a wider depth range (0.10-0.30m) at a later stage in development (R(^2)=0.98). The high correlation between physical ground disturbance and the thermal response is also corroborated qualitatively with the results of the resistivity surveys. The ATI modelling reveals similar features to those evident on day or night thermal images at an early stage in vegetation growth, suggesting that thermal imaging during the day at an early stage in vegetation growth may supply sufficient information on features buried in the near-surface environment. Airborne thermal imaging therefore provides a useful complementary prospection tool for archaeological and geological applications for surfaces covered by vegetation

Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign

Robust spatial information about environmental water use at field scales and daily to seasonal timesteps will benefit many applications in agriculture and water resource management. This information is particularly critical in arid climates where freshwater resources are limited or expensive, and groundwater supplies are being depleted at unsustainable rates to support irrigated agriculture as well as municipal and industrial uses. Gridded evapotranspiration (ET) information at field scales can be obtained periodically using land–surface temperature-based surface energy balance algorithms applied to moderate resolution satellite data from systems like Landsat, which collects thermal-band imagery every 16 days at a resolution of approximately 100 m. The challenge is in finding methods for interpolating between ET snapshots developed at the time of a clear-sky Landsat overpass to provide complete daily time-series over a growing season. This study examines the efficacy of a simple gap-filling algorithm designed for applications in data-sparse regions, which does not require local ground measurements of weather or rainfall, or estimates of soil texture. The algorithm relies on general conservation of the ratio between actual ET and a reference ET, generated from satellite insolation data and standard meteorological fields from a mesoscale model. The algorithm was tested with ET retrievals from the Atmosphere–Land Exchange Inverse (ALEXI) surface energy balance model and associated DisALEXI flux disaggregation technique, which uses Landsat-scale thermal imagery to reduce regional ALEXI maps to a finer spatial resolution. Daily ET at the Landsat scale was compared with lysimeter and eddy covariance flux measurements collected during the Bushland Evapotranspiration and Agricultural Remote sensing EXperiment of 2008 (BEAREX08), conducted in an irrigated agricultural area in the Texas Panhandle under highly advective conditions. The simple gap-filling algorithm performed reasonably at most sites, reproducing observed cumulative ET to within 5–10% over the growing period from emergence to peak biomass in both rainfed and irrigated fields

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

Theoretical Modeling and Analysis of L- and P-band Radar Backscatter Sensitivity to Soil Active Layer Dielectric Variations

Freeze-thaw (FT) and moisture dynamics within the soil active layer are critical elements of boreal, arctic and alpine ecosystems, and environmental change assessments. We evaluated the potential for detecting dielectric changes within different soil layers using combined L- and P-band radar remote sensing as a prerequisite for detecting FT and moisture profile changes within the soil active layer. A two-layer scattering model was developed and validated for simulating radar responses from vertically inhomogeneous soil. The model simulations indicated that inhomogeneity in the soil dielectric profile contributes to both L- and P-band backscatter, but with greater P-band sensitivity at depth. The difference in L- and P-band responses to soil dielectric profile inhomogeneity appears suitable for detecting associated changes in soil active layer conditions. Additional evaluation using collocated airborne radar (AIRSAR) observations and in situ soil moisture measurements over alpine tundra indicates that combined L- and P-band SAR observations are sensitive to soil dielectric profile heterogeneity associated with variations in soil moisture and FT conditions

Towards a harmonized long‐term spaceborne record of far‐red solar induced fluorescence

Far‐red solar‐induced chlorophyll fluorescence (SIF) has been retrieved from multiple satellites with nearly continuous global coverage since 1996. Multiple official and research‐grade retrievals provide a means for cross validation across sensors and algorithms, but produces substantial variation across products due to differences in instrument characteristics and retrieval algorithm. The lack of a consistent, calibrated SIF data set hampers scientific interpretation of planetary photosynthesis. NASA's Orbiting Carbon Observatory 2 (OCO‐2) offers small sampling footprints, high data acquisition, and repeating spatially resolved targets at bioclimatically diverse locations, providing a unique benchmark for spaceborne sensors traceable to ground data. We leverage overlap between the longer running Global Ozone Monitoring Instrument version 2 (GOME‐2) SIF time series, and more recent state‐of‐the‐art OCO‐2 and TROPOspheric Monitoring Instrument (TROPOMI) data, in a first attempt to reconcile inconsistencies in the long‐term record. After screening and correcting for key instrument differences (time of day, wavelength, Sun‐sensor geometry, cloud effects, footprint area), we find that Global Ozone Monitoring Instrument version 2 and TROPOspheric Monitoring Instrument perform exceedingly well in capturing spatial, seasonal, and interannual variability across OCO‐2 targets. However, Global Ozone Monitoring Instrument version 2 retrieval methods differ by up to a factor of 2 in signal‐to‐noise and magnitude. Magnitude differences are largely attributed to retrieval window choice, with wider windows producing higher magnitudes. The assumed SIF spectral shape has negligible effect. Substantial research is needed to understand remaining sensitivities to atmospheric absorption and reflectance. We conclude that OCO‐2 and TROPOspheric Monitoring Instrument have opened up the possibility to produce a multidecadal SIF record with well‐characterized uncertainty and error quantification for overlapping instruments, enabling back‐calibration of previous instruments and production of a consistent, research‐grade, harmonized time series