Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA

The Evaporative Stress Index (ESI) quantifies temporal anomalies in a normalized evapotranspiration (ET) metric describing the ratio of actual-to-reference ET (fRET) as derived from satellite remote sensing. At regional scales (3–10 km pixel resolution), the ESI has demonstrated the capacity to capture developing crop stress and impacts on regional yield variability in water-limited agricultural regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to spatial and temporal limitations in the standard ESI products. In this study, we investigated potential improvements to ESI by generating maps of ET, fRET, and fRET anomalies at high spatiotemporal resolution (30-m pixels, daily time steps) using a multi-sensor data fusion method, enabling separation of landcover types with different phenologies and resilience to drought. The study was conducted for the period 2010–2014 covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of maize yield were investigated at both the field and county level to assess the potential of ESI as a yield forecasting tool. To examine the role of crop phenology in yield-ESI correlations, annual input fRET time series were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). At the resolution of the operational U.S. ESI product (4 km), adjusting fRET alignment to a regionally reported emergence date prior to anomaly computation improves r2 correlations with county-level yield estimates from 0.28 to 0.80. At 30-m resolution, where pure maize pixels can be isolated from other crops and landcover types, county-level yield correlations improved from 0.47 to 0.93 when aligning fRET by emergence date rather than calendar date. Peak correlations occurred 68 days after emergence, corresponding to the silking stage for maize when grain development is particularly sensitive to soil moisture deficiencies. The results of this study demonstrate the utility of remotely sensed ET in conveying spatially and temporally explicit water stress information to yield prediction and crop simulation models

Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards

Irrigation in the Central Valley of California is essential for successful wine grape production. With reductions in water availability in much of California due to drought and competing water-use interests, it is important to optimize irrigation management strategies. In the current study, we investigate the utility of satellite-derived maps of evapotranspiration (ET) and the ratio of actual-to-reference ET (fRET) based on remotely sensed land-surface temperature (LST) imagery for monitoring crop water use and stress in vineyards. The Disaggregated Atmosphere Land EXchange Inverse (ALEXI/DisALEXI) surface-energy balance model, a multi-scale ET remote-sensing framework with operational capabilities, is evaluated over two Pinot noir vineyard sites in central California that are being monitored as part of the Grape Remote-Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). A data fusion approach is employed to combine ET time-series retrievals from multiple satellite platforms to generate estimates at both the high spatial (30 m) and temporal (daily) resolution required for field-scale irrigation management. Comparisons with micrometeorological data indicate reasonable model performance, with mean absolute errors of 0.6 mm day−1 in ET at the daily time step and minimal bias. Values of fRET agree well with tower observations and reflect known irrigation. Spatiotemporal analyses illustrate the ability of ALEXI/DisALEXI/data fusion package to characterize heterogeneity in ET and fRET both within a vineyard and over the surrounding landscape. These findings will inform the development of strategies for integrating ET mapping time series into operational irrigation management framework, providing actionable information regarding vineyard water use and crop stress at the field and regional scale and at daily to multi-annual time scales.info:eu-repo/semantics/acceptedVersio

The Grape Remote Sensing Atmospheric Profile and Evapotranspiration Experiment

Particularly in light of California’s recent multiyear drought, there is a critical need for accurate and timely evapotranspiration (ET) and crop stress information to ensure long-term sustainability of high-value crops. Providing this information requires the development of tools applicable across the continuum from subfield scales to improve water management within individual fields up to watershed and regional scales to assess water resources at county and state levels. High-value perennial crops (vineyards and orchards) are major water users, and growers will need better tools to improve water-use efficiency to remain economically viable and sustainable during periods of prolonged drought. To develop these tools, government, university, and industry partners are evaluating a multiscale remote sensing–based modeling system for application over vineyards. During the 2013–17 growing seasons, the Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data within adjacent pinot noir vineyards in the Central Valley of California. Additionally, each year ground, airborne, and satellite remote sensing data were collected during intensive observation periods (IOPs) representing different vine phenological stages. An overview of the measurements and some initial results regarding the impact of vine canopy architecture on modeling ET and plant stress are presented here. Refinements to the ET modeling system based on GRAPEX are being implemented initially at the field scale for validation and then will be integrated into the regional modeling toolkit for large area assessment

The grape remote sensing atmospheric profile and evapotranspiration experiment

Particularly in light of California’s recent multiyear drought, there is a critical need for accurate and timely evapotranspiration (ET) and crop stress information to ensure long-term sustainability of high-value crops. Providing this information requires the development of tools applicable across the continuum from subfield scales to improve water management within individual fields up to watershed and regional scales to assess water resources at county and state levels. High-value perennial crops (vineyards and orchards) are major water users, and growers will need better tools to improve water-use efficiency to remain economically viable and sustainable during periods of prolonged drought. To develop these tools, government, university, and industry partners are evaluating a multiscale remote sensing–based modeling system for application over vineyards. During the 2013–17 growing seasons, the Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data within adjacent pinot noir vineyards in the Central Valley of California. Additionally, each year ground, airborne, and satellite remote sensing data were collected during intensive observation periods (IOPs) representing different vine phenological stages. An overview of the measurements and some initial results regarding the impact of vine canopy architecture on modeling ET and plant stress are presented here. Refinements to the ET modeling system based on GRAPEX are being implemented initially at the field scale for validation and then will be integrated into the regional modeling toolkit for large area assessment.info:eu-repo/semantics/publishedVersio

Uso de sensores remotos en el seguimiento de la vegetación de dehesa y su influencia en el balance hidrológico a escala de cuenca

The Mediterranean region is characterized by hot summers with long dry periods, a situation that may be exacerbated by the progressive global warming. In these water-limited environments where productivity of the ecosystems depends mainly on water availability, the reduction of freshwater resources can have severe consequences. An increase in aridity may lead to low productivity, land degradation and unwanted changes in land use. To reduce the vulnerability of Mediterranean landscapes it is important to improve our knowledge of the hydrological processes conditioning the water exchanges, with evapotranspiration (ET) being a key indicator of the state of ecosystems and playing a crucial role in the basin's water and energy balances. The goal of this dissertation is to improve our understanding of the evapotranspiration dynamics over Mediterranean heterogeneous and complex vegetation covers, with a focus on the dehesa ecosystem. The final aim is to contribute to the conservation of the water resources in these regions in the medium to long term, supporting the decision-making processes with quantitative, distributed, and high-quality information. To reach this goal, in this research the evaluation of remote sensing-based soil water balance (SWB) and surface energy balance (SEB) models was proposed to monitor the water consumption and water stress of typical Mediterranean vegetation at different spatial and temporal scales. In particular, the VI-ETo methodology (SWB) and the ALEXI/DisALEXI approach (SEB) have been adapted and applied. ET modeling using the VI-ETo scheme has been improved through the assessment of the vegetation layers' effective parameters. A data fusion algorithm was applied to the ET maps produced by the SEB model over the dehesa ecosystem, and we analyzed the opportunities that this high-resolution ET product in time and space can provide for water and vegetation resource management. The results have demonstrated the feasibility of both approaches (SWB and SEB models) to accurately monitor ET dynamics over the dehesa landscape, adequately reproducing the annual bimodal behavior and the response of the vegetation in periods of water deficit. The error obtained using the SWB approach (the VI-ETo method) was RMSE = 0.47 mm day-1 over the whole dehesa system (grass + trees) and over an open grassland. The monitoring of water stress for both systems with different canopy structure, using as a proxy the ET/ETo ratio, and the stress coefficient (Ks), was successful. Improvements on the specific spectral properties of oak trees and layer-specific parameters were included into the modeling. We also analyzed the influence of the spectral properties of oak trees and another typical Mediterranean tree canopy, the olive orchard, in the VI-ETo model. We found that the use of appropriate values of the parameter SAVImax (0.51 for oak trees and 0.57 for olive trees) had notable implications in the computation of ET and water stress, in contrast to using a generic value for Mediterranean crops (SAVImax= 0.75). The accuracy of this water balance-based approach was also evaluated over two heterogeneous Mediterranean basins, with a mosaic of holm oaks and grasslands, shrubs, coniferous plantations, and irrigated horticultural crops. The annual discharge flows of both watersheds, which were determined from the modeled ET data and using a simple surface water balance, were very similar to those obtained with the HBV hydrological model, and to the values measured at the outlet of one of the basins, corroborating the usefulness of the VI-ETo methodology on these vegetation types. On the other hand, the resulting ET series (30 m, daily) derived with the SEB approach (ALEXI/DisALEXI method) and the STARFM fusion algorithm provided an RMSE value of 0.67 mm day-1, which was considered an acceptable error for management purposes. This error was slightly lower compared to using simpler interpolation methods, probably due to the high temporal frequency and better spatial representation of the flux tower footprint of the fused time series. The analysis of ET patterns over small heterogeneous vegetated patches that form the dehesa structure revealed the importance of having fine resolution information at field scale to distinguish the water consumed by the different vegetation components, which influences the provision of many ecosystem services. For example, it was key for identifying phenology dates of grasslands, or understanding the hydrological functioning of riverside dense evergreen vegetation with high ET rates during the whole year, in contrast with the herbaceous areas. Accurately modeling these different behaviors of dehesa microclimates is useful to support farmers‘ management and provide recommendations tailored for each structural component and requirements.La región mediterránea se caracteriza por veranos calurosos con largos períodos sin precipitaciones, situación que puede agravarse con el progresivo calentamiento global. En estos ambientes donde la productividad de los ecosistemas depende principalmente de la disponibilidad de agua, la reducción de los recursos hídricos puede tener graves consecuencias. Un aumento de la aridez puede conducir a una baja productividad, degradación de la tierra y cambios no deseados en el uso del suelo. Para reducir la vulnerabilidad de las zonas mediterráneas es importante profundizar en el estudio de los procesos hidrológicos que condicionan los intercambios de agua, siendo la evapotranspiración (ET) un indicador clave del estado de los ecosistemas y jugando un papel crucial en los balances hídricos y energéticos de la cuenca. El objetivo de esta tesis es mejorar nuestro conocimiento sobre la dinámica de la evapotranspiración en cubiertas mediterráneas heterogéneas y complejas, con el foco en el ecosistema de dehesa. El objetivo final es contribuir a la conservación de los recursos hídricos de estas regiones en el medio-largo plazo, apoyando en los procesos de toma de decisiones con información cuantitativa, distribuida y de calidad. Para alcanzar este objetivo, en esta investigación se propuso evaluar modelos de balance de agua en el suelo (SWB) y balance de energía en superficie (SEB) basados en el uso de sensores remotos, para el seguimiento del consumo de agua y el estrés hídrico de la vegetación mediterránea a diferentes escalas espaciales y temporales. En particular, se ha adaptado y aplicado la metodología VI-ETo (SWB) y el enfoque ALEXI/DisALEXI (SEB). Se ha mejorado el modelado de ET utilizando el esquema VI-ETo mediante la evaluación de los parámetros efectivos de las capas de vegetación. Se aplicó un algoritmo de fusión de datos remotos a los mapas de ET generados por el modelo SEB sobre el ecosistema de dehesa, y estudiamos las oportunidades que este producto de ET con alta resolución espacial y temporal puede aportar en la gestión de los recursos hídricos y de los ecosistemas. Los resultados han demostrado la viabilidad de ambos enfoques (modelos SWB y SEB) para monitorear con precisión la dinámica de la ET sobre el ecosistema de dehesa, reproduciendo adecuadamente el comportamiento bimodal anual y la respuesta de la vegetación en períodos de déficit hídrico. El error obtenido usando el enfoque SWB (el método VI-ETo) fue RMSE = 0.47 mm día-1, tanto para el sistema dehesa (pasto + árboles) como para una zona de pastizal. El seguimiento del estrés hídrico para ambos sistemas con diferente estructura de vegetación, utilizando la relación ET/ETo y el coeficiente de estrés (Ks), fue satisfactorio. Se incluyeron en el modelado mejoras sobre las propiedades espectrales específicas de las encinas y los parámetros específicos de los diferentes estratos de vegetación. También analizamos la influencia de las propiedades espectrales de las encinas y otra cubierta mediterránea, el olivar, en el modelo VI-ETo. Encontramos que el uso de valores apropiados del parámetro SAVImax (0,51 para robles y 0,57 para olivos) tuvo un efecto significativo en la determinación del consumo de agua y estrés hídrico, en comparación con usar un valor genérico para cultivos mediterráneos (SAVImax = 0,75). La precisión de este enfoque basado en el balance hídrico también se evaluó en dos cuencas mediterráneas heterogéneas, con un mosaico de encinas y pastizales, arbustos, plantaciones de coníferas y cultivos hortícolas de regadío. Los caudales de descarga anual de ambas cuencas, determinados a partir de los datos de ET modelados y utilizando un balance hídrico superficial muy simple, fueron muy similares a los obtenidos con el modelo hidrológico HBV, y a los valores medidos en la salida de una de las cuencas, corroborando la utilidad de la metodología VI-ETo sobre estas formaciones vegetales. Por otra parte, la serie final de ET (30 m, diaria) derivada del enfoque SEB (método ALEXI/DisALEXI) y del algoritmo de fusión STARFM proporcionó un valor de RMSE de 0,67 mm día-1, considerado un error aceptable para fines de manejo. Este error fue ligeramente inferior a los obtenidos usando métodos de interpolación más simples, debido probablemente a la alta frecuencia temporal y una mejor representación espacial del footprint de la torre de medida de flujos en la serie temporal fusionada. El análisis de los patrones de la ET sobre pequeñas manchas de vegetación heterogéneas, que forman la estructura de la dehesa, reveló la importancia de tener información con alta resolución a escala de campo para distinguir el agua consumida por los diferentes componentes de la vegetación, que tienen influencia en el aprovisionamiento de muchos servicios ecosistémicos. Por ejemplo, fue clave para identificar ciertas fechas fenológicas de los pastizales, o entender el funcionamiento hidrológico de la vegetación densa de hoja perenne en zonas de ribera con altas tasas de ET durante todo el año, en comparación con zonas de especies herbáceas. Modelar con precisión estos comportamientos diferentes de los microclimas de la dehesa es útil para apoyar la gestión de los agricultores y ofrecer recomendaciones adaptadas a cada componente y necesidades estructurales

Remote Sensing of Environment: Current status of Landsat program, science, and applications

Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat- 1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality. Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and followup with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat

Heterogeneous sensor database framework for the sensor observation service: integrating remote and in-situ sensor observations at the database backend

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Environmental monitoring and management systems in most cases deal with models and spatial analytics that involve the integration of in-situ and remote sensor observations. In-situ sensor observations and those gathered by remote sensors are usually provided by different databases and services in real-time dynamic service systems like the Geo-Web Services. Thus, data have to be pulled from different databases and transferred over the web before they are fused and processed on the service middleware. This process is very massive and unnecessary communication and work load on the service, especially when retrieving massive raster coverage data. Thus in this research, we propose a database model for heterogeneous sensortypes that enables geo-scientific processing and spatial analytics involving remote and in-situ sensor observations at the database level of the Sensor Observation Service, SOS. This approach would be used to reduce communication and massive workload on the Geospatial Web Service, as well make query request from the user end a lot more flexible. Hence the challenging task is to develop a heterogeneous sensor database model that enables geoprocessing and spatial analytics at the database level and how this could be integrated with the geo-web services to reduce communication and workload on the service and as well make query request from the client end more flexible through the use of SQL statements

Sharpening ECOSTRESS and VIIRS Land Surface Temperature Using Harmonized Landsat-Sentinel Surface Reflectances

Land surface temperature (LST) is a key diagnostic indicator of agricultural water use and crop stress. LST data retrieved from thermal infrared (TIR) band imagery, however, tend to have a coarser spatial resolution (e.g., 100 m for Landsat 8) than surface reflectance (SR) data collected from shortwave bands on the same instrument (e.g., 30 m for Landsat). Spatial sharpening of LST data using the higher resolution multi-band SR data provides an important path for improved agricultural monitoring at sub-field scales. A previously developed Data Mining Sharpener (DMS) approach has shown great potential in the sharpening of Landsat LST using Landsat SR data co-collected over various landscapes. This work evaluates DMS performance for sharpening ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST (~70 m native resolution) and Visible Infrared Imaging Radiometer Suite (VIIRS) LST (375 m) data using Harmonized Landsat and Sentinel-2 (HLS) SR data, providing the basis for generating 30-m LST data at a higher temporal frequency than afforded by Landsat alone. To account for the misalignment between ECOSTRESS/VIIRS and Landsat/HLS caused by errors in registration and orthorectification, we propose a modified version of the DMS approach that employs a relaxed box size for energy conservation (EC). Sharpening experiments were conducted over three study sites in California, and results were evaluated visually and quantitatively against LST data from unmanned aerial vehicles (UAV) flights and from Landsat 8. Over the three sites, the modified DMS technique showed improved sharpening accuracy over the standard DMS for both ECOSTRESS and VIIRS, suggesting the effectiveness of relaxing EC box in relieving misalignment-induced errors. To achieve reasonable accuracy while minimizing loss of spatial detail due to the EC box size increase, an optimal EC box size of 180–270 m was identified for ECOSTRESS and about 780 m for VIIRS data based on experiments from the three sites. Results from this work will facilitate the development of a prototype system that generates high spatiotemporal resolution LST products for improved agricultural water use monitoring by synthesizing multi-source remote sensing data