Based on the Gaussian Fitting Method to Derive Daily Evapotranspiration from Remotely Sensed Instantaneous Evapotranspiration

Evapotranspiration (ET) is a significant component in the water cycle, and the estimation of it is imperative in water resource management. Regional ET can be derived by using remote sensing technology which combines remote sensing inputs with ground-based measurements. However, instantaneous ET values estimated through remote sensing directly need to be converted into daily totals. In this study, we attempted to retrieve daily ET from remotely sensed instantaneous ET. The study found that the Gaussian fitting curve closely followed the ET measurements during the daytime and hence put forward the Gaussian fitting method to convert the remotely sensed instantaneous ET into daily ETs. The method was applied to the middle reaches of Heihe River in China. Daily ETs on four days were derived and evaluated with ET measurements from the eddy covariance (EC) system. The correlation between daily ET estimates and measurements showed high accuracy, with a coefficient of determination (R2) of 0.82, a mean average error (MAE) of 0.41 mm, and a root mean square error (RMSE) of 0.46 mm. To make more scientific assessments, percent errors were calculated on the estimation accuracy, which ranged from 0% to 18%, with more than 80% of locations having the percent errors within 10%. Analyses on the relationship between daily ET estimates and land use status were also made to assess the Gaussian fitting method, and the results showed that the spatial distribution of daily ET estimates well demonstrated ET differences caused by land use types and was intimately linked with the vegetation pattern. The comparison between the Gaussian fitting method and the sine function method and the ETrF method indicated that results derived through the Gaussian fitting method had higher precision than that obtained by the sine function method and the ETrF method.</jats:p

Estimation of High-Resolution Evapotranspiration in Heterogeneous Environments Using Drone-Based Remote Sensing

Evapotranspiration (ET) is a key element of hydrological cycle analysis, irrigation demand, and for better allocation of water resources in the ecosystem. For successful water resources management activities, precise estimate of ET is necessary. Although several attempts have been made to achieve that, variation in temporal and spatial scales constitutes a major challenge, particularly in heterogeneous canopy environments such as vineyards, orchards, and natural areas. The advent of remote sensing information from different platforms, particularly the small unmanned aerial systems (sUAS) technology with lightweight sensors allows users to capture high-resolution data faster than traditional methods, described as “flexible in timing”. In this study, the Two Source Energy Balance Model (TSEB) along with high-resolution data from sUAS were used to bridge the gap in ET issues related to spatial and temporal scales. Over homogeneous vegetation surfaces, relatively low spatial resolution information derived from Landsat (e.g., 30 m) might be appropriate for ET estimate, which can capture differences between fields. However, in agricultural landscapes with presence of vegetation rows and interrows, the homogeneity is less likely to be met and the ideal conditions may be difficult to identify. For most agricultural settings, row spacing can vary within a field (vineyards and orchards), making the agricultural landscape less homogenous. This leads to a key question related to how the contextual spatial domain/model grid size could influence the estimation of surface fluxes in canopy environments such as vineyards. Furthermore, temporal upscaling of instantaneous ET at daily or longer time scales is of great practical importance in managing water resources. While remote sensing-based ET models are promising tools to estimate instantaneous ET, additional models are needed to scale up the estimated or modeled instantaneous ET to daily values. Reliable and precise daily ET (ETd) estimation is essential for growers and water resources managers to understand the diurnal and seasonal variation in ET. In response to this issue, different existing extrapolation/upscaling daily ET (ETd) models were assessed using eddy covariance (EC) and sUAS measurements. On the other hand, ET estimation over semi-arid naturally vegetated regions becomes an issue due to high heterogeneity in such environments where vegetation tends to be randomly distributed over the land surface. This reflects the conditions of natural vegetation in river corridors. While significant efforts were made to estimate ET at agricultural landscapes, accurate spatial information of ET over riparian ecosystems is still challenging due to various species associated with variable amounts of bare soil and surface water. To achieve this, the TSEB model with high-resolution remote sensing data from sUAS were used to characterize the spatial heterogeneity and calculate the ET over a natural environment that features arid climate and various vegetation types at the San Rafael River corridor

Balanço de energia com base no modelo S-SEBI sobre gramíneas em Barrax, Espanha e no bioma Pampa do sul do Brasil

No Brasil, existem seis biomas, sendo eles Amazônia, Mata Atlântica, Cerrado, Caatinga, Pantanal e Pampa. Cada bioma possui características únicas e importantes para a manutenção dos seus processos ecossistêmicos. Neste sentido, no bioma Pampa há uma dinâmica socioambiental que influencia a vegetação, o manejo agrícola e o modo de vida da população local. Este bioma é único no mundo porque traz na vegetação rasteira sua fonte de biomassa e energia como em nenhum outro ecossistema, seus campos nativos são os responsáveis pela conservação e preservação dos recursos hídricos, da fauna silvestre e da biodiversidade. A supressão da vegetação nativa deste bioma para a monocultura de grãos compromete a manutenção da biodiversidade e gera impactos nos recursos naturais, alterando as suas condições ambientais, a disponibilidade de água e a temperatura de superfície. Além disso, as mudanças climáticas têm modificado os componentes do Balanço de Energia (BE). Em relação ao balanço energético este bioma tem, no estado do Rio Grande do Sul, a mesma importância climática que as florestas em regiões tropicais, já que cobre 63% do Estado e possui influência nas dinâmicas atmosféricas. Sendo assim, o objetivo deste trabalho é avaliar as particularidades ambientais do BE e do cálculo de evapotranspiração (ET) no bioma Pampa. A ET é a responsável pelas interações da biosfera- atmosfera-hidrosfera. Estas interações se dão por utilizar energia eletromagnética para a formação de vapor d’água a partir da transpiração vegetal e da evaporação da água. O uso do Sensoriamento Remoto tem sido eficaz nas estimativas de fluxo de calor sensível e fluxo de calor latente por diferentes métodos, porém a aplicação de forma operacional, a heterogeneidade da superfície e a influência da temperatura de superfície (Ts) são desafios deste trabalho. O modelo S-SEBI para recuperação de dados de ET foi avaliado no bioma Pampa e em Barrax, um sítio de validação localizado no mediterrâneo espanhol. O modelo demonstrou ser eficaz em vegetação campestre, além de ser menos dependente da Ts em relação a outros modelos reportados na literatura. Os resultados deste trabalho visam contribuir para a geração de melhor qualidade de dados de ET em futuras análises de mudanças de uso do solo, mudanças climáticas e gestão dos recursos hídricos para todo o bioma Pampa.In Brazil, there are six biomes, namely the Amazon, Atlantic Forest, Cerrado, Caatinga, Pantanal, and Pampa. Each biome has unique and important characteristics for the maintenance of the ecosystemic processes of each environment. In this sense, in the Pampa biome there is a socio-environmental dynamic that influences the vegetation, agricultural management, and the way of life of the local population. This biome is unique in the world because it brings in its undergrowth vegetation its source of biomass and energy like no other ecosystem; its native grasslands are responsible for the conservation and preservation of water resources, wildlife, and biodiversity. The suppression of the native vegetation of this biome for the monoculture of grains compromises the maintenance of biodiversity and generates impacts on natural resources, altering the environmental conditions of the ecosystem, water availability, and surface temperature. In addition, climate change has modified the components of the Energy Balance (EB). In relation to the energy balance, in the state of Rio Grande do Sul, this biome has the same climatic importance as the forests in tropical regions, since it covers 63% of the state and influences the atmospheric dynamics. Therefore, the objective of this work is to evaluate the environmental particularities of BE and the calculation of evapotranspiration (ET) in the Pampa biome. ET is responsible for biosphere-atmosphere-hydrosphere interactions. These interactions occur by using electromagnetic energy for the formation of water vapor from plant transpiration and water evaporation. The use of Remote Sensing has been effective in estimating sensible heat flux and latent heat flux by different methods, but the application in an operational way, the heterogeneity of the surface and the influence of the surface temperature (Ts) are challenges of this work. The S-SEBI model for ET data retrieval was evaluated in the Pampa biome and in Barrax, a validation site located in the Spanish Mediterranean. The model proved to be effective in grassland vegetation, and is less dependent on Ts compared to other models reported in the literature. The results of this work aim to contribute to the generation of better quality ET data in future analyses of land use change, climate change, and water resource management for the entire Pampa biome

Modeling Spatial Surface Energy Fluxes of Agricultural and Riparian Vegetation Using Remote Sensing

Modeling of surface energy fluxes and evapotranspiration (ET) requires the understanding of the interaction between land and atmosphere as well as the appropriate representation of the associated spatial and temporal variability and heterogeneity. This dissertation provides new methodology showing how to rationally and properly incorporate surface features characteristics/properties, including the leaf area index, fraction of cover, vegetation height, and temperature, using different representations as well as identify the related effects on energy balance flux estimates including ET. The main research objectives were addressed in Chapters 2 through 4 with each presented in a separate paper format with Chapter 1 presenting an introduction and Chapter 5 providing summary and recommendations. Chapter 2 discusses a new approach of incorporating temporal and spatial variability of surface features. We coupled a remote sensing-based energy balance model with a traditional water balance method to provide improved estimates of ET. This approach was tested over rainfed agricultural fields ~ 10 km by 30 km in Ames, Iowa. Before coupling, we modified the water balance method by incorporating a remote sensing-based estimate for one of its parameters to ameliorate its performance on a spatial basis. Promising results were obtained with indications of improved estimates of ET and soil moisture in the root zone. The effects of surface features heterogeneity on measurements of turbulence were investigated in Chapter 3. Scintillometer-based measurements/estimates of sensible heat flux (H) were obtained over the riparian zone of the Cibola National Wildlife Refuge (CNWR), California. Surface roughness including canopy height (hc), roughness length, and zero-plane displacement height were incorporated in different ways, to improve estimates of H. High resolution, 1-m maps of ground surface digital elevation model and canopy height, hc, were derived from airborne LiDAR sensor data to support the analysis. The effects of using different pixel resolutions to account for surface feature variability on modeling energy fluxes, e.g., net radiation, soil, sensible, and latent heat, were studied in Chapter 4. Two different modeling approaches were applied to estimate energy fluxes and ET using high and low pixel resolution datasets obtained from airborne and Landsat sensors, respectively, provided over the riparian zone of the CNWR, California. Enhanced LiDAR-based hc maps were also used to support the modeling process. The related effects were described relative to leaf area index, fraction of cover, hc, soil moisture status at root zone, groundwater table level, and vegetation stress conditions

Improved Modeling of Evapotranspiration using Satellite Remote Sensing at Varying Spatial and Temporal Scales

The overall objective of the dissertation was to improve the spatial and temporal representation and retrieval accuracy of evapotranspiration (ET) using satellite imagery. Specifically, (1) aiming at improving the spatial representation of daily net radiation (Rn,24) under rugged terrains, a new algorithm, which accounts for terrain effects on available shortwave radiation throughout a day and utilizes four observations of Moderate-resolution Imaging Spectroradiometer (MODIS)-based land surface temperature retrievals to simulate daily net longwave radiation, was developed. The algorithm appears to be capable of capturing heterogeneity in Rn,24 at watershed scales. (2) Most satellite-based ET models are constrained to work under cloud-free conditions. To address this deficiency, an approach of integrating a satellite-based model with a large-scale feedback model was proposed to generate ET time series for all days. Results show that the ET time series estimates can exhibit complementary features between the potential ET and the actual ET at watershed scales. (3) For improving the operability of Two-source Energy Balance (TSEB) which requires computing resistance networks and tuning the Priestley-Taylor parameter involved, a new Two-source Trapezoid Model for ET (TTME) based on deriving theoretical boundaries of evaporative fraction (EF) and the concept of soil surface moisture availability isopleths was developed. It was applied to the Soil Moisture and Atmosphere Coupling Experiment (SMACEX) site in central Iowa, U.S., on three Landsat TM/ETM imagery acquisition dates in 2002. Results show the EF and latent heat flux (LE) estimates with a mean absolute percentage difference (MAPD) of 6.7 percent and 8.7 percent, respectively, relative to eddy covariance tower-based measurements after forcing closure by the Bowen ratio technique. (4) The domain and resolution dependencies of the Surface Energy Balance Algorithm for Land (SEBAL) and the triangle model were systematically investigated. Derivation of theoretical boundaries of EF for the two models could effectively constrain errors/uncertainties arising from these dependencies. (5) A Modified SEBAL (M-SEBAL) was consequently proposed, in which subjectivity involved in the selection of extreme pixels by the operator is eliminated. The performance of M-SEBAL at the SMACEX site is reasonably well, showing EF and LE estimates with an MAPD of 6.3 percent and 8.9 percent, respectively

MODELLING WATER AND ENERGY BALANCE OF THE LAND-ATMOSPHERE SYSTEM USING HIGH RESOLUTION REMOTE SENSING DATA

La rilevanza assunta dal risparmio della risorsa idrica negli ultimi anni ha spinto verso una corretta quanti cazione delle perdite legate al processo evapotraspirativo, al ne di una gestione parsimoniosa della risorsa stessa. In particolare nei sistemi agricoli soggetti a stress severo, sia la misura che la stima dell'evapotraspirazione (ET) ad un'adeguata risoluzione spaziale e temporale sono uno dei principali problemi da a rontare per la comunit a scienti ca. Recentemente, le tecniche di telerilevamento sono divenute un ulteriore strumento a supporto della modellistica idrologica distribuita; in particolare, le immagini acquisite nelle onde corte e nell'infrarosso termico risultano essere di notevole interesse. In questo contesto, i due scopi principali di questa ricerca sono stati: la quanti cazione dell'accuratezza delle misure micro-meteorologiche in sistemi agricoli vegetati con colture alte e sparse; e l'analisi dei modelli basati su dati telerilevati per la stima di ET ad alta risoluzione spaziale e temporale. L'area di studio e caratterizzata da un tipico clima Mediterraneo e da colture olivicole, e si trova localizzata nei pressi di Castelvetrano (Italia). Quest'area e stata oggetto nella primavera-estate 2008 di una campagna di misura mediante istallazioni eddy covariance e scintillometrica, e, contestualmente, dall'acquisizione di 7 immagini multi-spettrali ad alta risoluzione. L'analisi delle misure micro-meteorologiche ha permesso di quanti care l'accordo tra le due tecniche e ha portato allo sviluppo di un nuovo approccio di calibrazione dei dati scintillometrici. Inoltre, alcune ipotesi alla base della stima dei ussi giornalieri sono state discusse in dettaglio. L'analisi degli algoritmi per la simulazione dei processi di scambio nel continuo suolo-pianta-atmosfera e stata focalizzata: i) sulle stime hot-spot di ET mediante un approccio di bilancio energetico residuale, ii) sulla stima in continuo di ET alla scala di campo mediante diversi approcci. Quest'ultima analisi ha evidenziato i buoni risultati del modello accoppiato energetico/idrologico per la stima dei ussi di acqua ed energia sia a scala oraria che giornaliera. In ne, l'applicabilit a di due approcci di data assimilation e stata testata utilizzando sia osservazioni arti ciali che reali.In view of the increased relevance of water saving issues in the last decades, the correct quanti cation of water loss due to evapotranspirative process became fundamental for a parsimonious management of this resource. Especially in agricultural systems subjected to severe water stress, both the measurement and the modelling of evapotranspiration (ET) at adequate temporal and spatial resolution, are important topics for the hydrologist scienti c community. Recently, the remote sensing techniques provide an additional tool to support the hydrologic spatially distributed models; in particular, images acquired in the short-wave and the thermal spectral regions have quite interesting applications. Within this framework, the two principal aims of this work were: to quantify the accuracy of surface energy uxes measured by micro-meteorological techniques in sparse tall vegetated system; and to analyze the capability of remote sensing-based approach to retrieve ET at high temporal and spatial resolution. The selected test site was an area characterized by Mediterranean climate and olive crops, located near Castelvetrano (Italy). This area, during the spring-summer period in 2008, was interested by in-situ measurements campaigns with eddy covariance and scintillometer instruments, and, contextually, by the acquisition of 7 high resolution multi-spectral images. The analysis of micro-meteorological measurements allows to evaluate the agreement between these techniques in the study site, also by means of a novel algorithm for the elaboration of scintillometer data. Moreover, some fundamental hypothesis of daily uxes estimation was critically discussed. The analysis of the algorithms for the simulation of the exchange processes in the continuum soil-plant-atmosphere was focused on: i) the retrieval of hot-spot ET maps by means of residual energy balance approach and ii) the continuous ET estimation at eld scale using di erent approaches. This latter analysis highlights the good performance of a coupled energy/hydrological model for the assessment of energy and water uxes at both hourly and daily scale. Finally, the applicability of two data assimilation schemes was tested using both arti cial and real observations

Consistent assimilation of MERIS FAPAR and atmospheric CO2 into a terrestrial vegetation model and interactive mission benefit analysis

The terrestrial biosphere is currently a strong sink for anthropogenic CO2 emissions. Through the radiative properties of CO2, the strength of this sink has a direct influence on the radiative budget of the global climate system. The accurate assessment of this sink and its evolution under a changing climate is, hence, paramount for any efficient management strategies of the terrestrial carbon sink to avoid dangerous climate change. Unfortunately, simulations of carbon and water fluxes with terrestrial biosphere models exhibit large uncertainties. A considerable fraction of this uncertainty reflects uncertainty in the parameter values of the process formulations within the models. This paper describes the systematic calibration of the process parameters of a terrestrial biosphere model against two observational data streams: remotely sensed FAPAR (fraction of absorbed photosynthetically active radiation) provided by the MERIS (ESA’s Medium Resolution Imaging Spectrometer) sensor and in situ measurements of atmospheric CO2 provided by the GLOBALVIEW flask sampling network. We use the Carbon Cycle Data Assimilation System (CCDAS) to systematically calibrate some 70 parameters of the terrestrial BETHY (Biosphere Energy Transfer Hydrology) model. The simultaneous assimilation of all observations provides parameter estimates and uncertainty ranges that are consistent with the observational information. In a subsequent step these parameter uncertainties are propagated through the model to uncertainty ranges for predicted carbon fluxes. We demonstrate the consistent assimilation at global scale, where the global MERIS FAPAR product and atmospheric CO2 are used simultaneously. The assimilation improves the match to independent observations. We quantify how MERIS data improve the accuracy of the current and future (net and gross) carbon flux estimates (within and beyond the assimilation period). We further demonstrate the use of an interactive mission benefit analysis tool built around CCDAS to support the design of future space missions. We find that, for long-term averages, the benefit of FAPAR data is most pronounced for hydrological quantities, and moderate for quantities related to carbon fluxes from ecosystems. The benefit for hydrological quantities is highest for semi-arid tropical or sub-tropical regions. Length of mission or sensor resolution is of minor importance.JRC.H.7-Climate Risk Managemen

Regional analysis of maize-based land use systems for early warning applications

Conventional analytical crop growth models cannot handle actual Land Use Systems because of massive data needs, algorithm complexity and prohibitive error propagation. It is possible however to describe rigidly simplified 'Production Situations' representing Land Use Systems with annual row crops and minimal environmental constraints. The simplest Production Situation imaginable is a Land Use System in which all constraints that can be eliminated by a farmer are indeed (assumed to be) eliminated. Crop growth and yield are then entirely conditioned by crop physiology and weather conditions, notably by the temperature and radiation during the crop cycle. The calculated production level is not the actual production but the production potential.In many countries, water availability to the crop is the main constraint to crop production. The biophysical production potential model has therefore been extended with a water budget routine that matches actual water use with the crop's requirement in order to calculate the "water-limited production potential". In this configuration, crop physiology, temperature, radiation and water availability condition the calculated level of crop (potential) production. This thesis discusses the use of satellite-derived rainfall data for regional analysis of water-limited yield potentials.Monitoring and crop yield forecasting for early warning applications require insight in farmers' reality. Often, a score of environmental and socio-economic constraints reduce on-farm production to a level that lags far behind the theoretical production potential. This thesis explores farmers' insights, in an attempt to identify the causes and structure of the "yield gap" between potential (reference) production levels and production levels realized on-farm.So far, actual production could only be established through field measurements. This thesis presents a methodology for estimating regional levels of actual crop production. The difference between remotely sensed canopy temperature and ambient temperature is used to estimate the degree of stomata closure of the crop. Introducing this Remote Sensing based degree of stomata closure in calculations of assimilatory activity permits to calculate the actual rate of crop growth over regions.Repeated measurements during the crop cycle allow monitoring of the sufficiency of actual management practices. Introducing estimated or forecast weather data in crop growth calculations for the remainder of the crop cycle permits to make repeated estimates of anticipated crop production and to timely signal a need for remedial action

A water storage reanalysis over the European continent: assimilation of GRACE data into a high-resolution hydrological model and validation

Continental water storage and redistribution within the Earth’s system are key variables of the terrestrial water cycle. Changes in water storage and fluxes may affect resources for drinking water and irrigation, lead to drought or flood conditions, or cause severe changes of ecosystems e.g., through salinification. Hydrological models, which map water storages and fluxes, are being continuously improved and deepen our understanding of geophysical processes related to the water cycle. However, models are built on a simplified representation of reality, which leads to limited predicting skills of the simulation results. Assimilating remotely sensed total water storage variability from the Gravity Recovery and Climate Experiment (GRACE) mission has become a valuable tool for reducing uncertainties of hydrological model simulations. Simultaneously, coarse GRACE observations are disaggregated spatially and temporally through data assimilation. In this thesis, GRACE data are assimilated into the Community Land Model version 3.5 (CLM3.5) yielding a unique daily 12.5 km reanalysis of total water storage evolution over Europe (2003 to 2010). Independent observations are evaluated to identify model deficits and to validate the performance of data assimilation. For the first time, the effect of data assimilation on modeled total water storage is also shown on the level of GRACE K-band observations. Optimal strategies for assimilating GRACE data into a high-resolution hydrological model are investigated through synthetic experiments. These experiments address the choice of the assimilation algorithm, localization, inflation of the ensemble of model states, ensemble size, error model of the observations, and spatial resolution of the observation grid. As the assimilation of GRACE data into CLM3.5 is realized within the Terrestrial Systems Modeling Platform (TerrSysMP), future assimilation experiments can be extended for the groundwater and atmosphere components included in TerrSysMP.Eine Reanalyse des europäischen Wasserspeichers: Assimilierung von GRACE Daten in ein hochaufgelöstes hydrologisches Modell und Validierung Änderungen im kontinentalen Wasserspeicher und im Transport von Wasser durch das Erdsystem sind wichtige Einflussgrößen für die Verfügbarkeit von Frischwasserresourcen, die Entstehung von Dürren und Überschwemmungen, sowie für die Erhaltung von Ökosystemen, welche z.B. durch Versalzung gefährdet werden. Hydrologische Modelle, die die Speicherung und den Transport von Wassermassen abbilden, werden stetig verbessert und helfen unser Verständnis von hydrologischen Prozessen zu vertiefen. Allerdings ermöglichen hydrologische Modelle nur eine vereinfachte Abbildung der Realität, sodass die Aussagekraft der Simulationsergebnisse beschränkt ist. Die Assimilierung von Wasserspeicheränderungen, gemessen von den GRACE (Gravity Recovery and Climate Experiment) Satelliten, kann hydrologische Simulationen verbessern und erlaubt gleichzeitig eine räumliche und zeitliche Differenzierung der grobaufgelösten GRACE Beobachtungen. In dieser Doktorarbeit werden GRACE Daten in das Land-Oberflächen-Modell CLM3.5 (Community Land Model Version 3.5) assimiliert, um eine neuartige Reanalyse täglicher Wasserspeicheränderungen (2003 bis 2010) für Europa mit 12.5 km Auflösung zu generieren. Durch unabhängige Beobachtungen werden Defizite des Modells identifiziert und das Ergebnis der Datenassimilierung beurteilt. Zum ersten Mal wird auch die Auswirkung der Assimilierung direkt auf Basis der GRACE K-Band Beobachtungen untersucht. Mit Hilfe synthetischer Experimente wird die beste Strategie zur Assimilierung von GRACE Daten in ein hochaufgelöstes hydrologisches Modell ermittelt. Dabei wird der Einfluss unterschiedlicher Assimilierungsstrategien untersucht, unter anderem die Wahl des Assimilierungsalgorithmus, die Lokalisierung des Einflussbereichs von Beobachtungen, die Erhöhung der Spannweite der Ensemblemitglieder des Modells, die Ensemblegröße, das Fehlermodell der Beobachtung und die räumliche Auflösung des Beobachtungsgitters. Da die Assimilierung von GRACE in das CLM3.5 Modell unter Verwendung von TerrSysMP (Terrestrial Systems Modeling Platform) geschieht, können die Assimilierungsexperimente in Zukunft auf die zusätzliche Verwendung des in TerrSysMP enthaltenen Grundwasser- und des Atmosphärenmodells erweitert werden.</p