906 research outputs found

    Final Report of the DAUFIN project

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    DAUFIN = Data Assimulation within Unifying Framework for Improved river basiN modeling (EC 5th framework Project

    Identifying and assessing vegetation behaviour in riparian zones at large scale in the Brazilian Savannah

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    Riparian zones (RZs) have a clear distinct behaviour than the rest of the landscape. Particularly in water-limited regions, such as the Brazilian Savannah (Cerrado biome), where dry season may extend 5 months, the difference between riparian and upland zones is highly pronounced due to vegetation water access to groundwater, and this can have implications on the climatic and hydrological cycles. In order to quantify this difference at large-scale, it was herein proposed to (1) map RZs using topographical information, (2) investigate how land cover is distributed among topographic gradients and (3) investigate vegetation behaviour through remote sensing vegetation measurements and evapotranspiration (ET) estimation. A 140,000 km² upland region inside the Cerrado biome, called the Urucuia aquifer system, was chosen as study site. The region has seen a huge agricultural expansion during the last decades, with mechanized and irrigated crops increasingly using water from its underground reserves, which associated with climate change can have a big impact on the ecosystem, and understanding the role of RZs can be essential to quantify this impact. The height above nearest drainage (HAND) index was used to map RZs, by visually assessing bellow which values the index provided a reasonable RZ buffer comparing with Google Earth imagery. We also used HAND to quantify across its values the historical land cover distribution obtained by the MapBiomas database, and analyse vegetation behaviour in RZs and upland zones (UZs) using remote sensing vegetation measurements of normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) and ET estimation from the surface energy balance algorithm for land (SEBAL). A necessary step for HAND computation is a defined stream network, for which the main challenge is identifying channel heads. Herein it was developed an algorithm that produced a varying draining area threshold (vDAT) map for channel initiation, using the topographic position index (TPI) as an auxiliary variable. This algorithm was tested using MERIT-DEM. With the stream network, HAND values bellow 5 m provided the best RZ buffer. As for land cover distribution, we captured that forests naturally occur more densely in the extreme values of HAND (very shallow and very deep) and that farmland historical occupation in the Urucuia region occur more in the upper portions of the terrain, possibly due to soil conditions stablished during landscape formation and evolution. As for vegetation activity, the land cover class seems to have more influence on vegetation behaviour than topographic position, for all indicators computed. Yet, NDMI values in Riparian Forests are greater than in Upland Forests, particularly towards drier conditions, in terms of both seasonality (drier months) and inter-annual variability (drier years). Despite this indication of more water available in RZs than UZs, the ET estimation could not capture these differences, possibly due to difficulties in estimating this variable in natural vegetation with high degree of water stress

    Modeling grassland productivity through remote sensing products

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    Mixed grasslands in south Canada serve a variety of economic, environmental and ecological purposes. Numerical modeling has become a major method used to identify potential grassland ecosystem responses to environment changes and human activities. In recent years, the focus has been on process models because of their high accuracy and ability to describe the interactions among different environmental components and the ecological processes. At present, two commonly-used process models (CENTURY and BIOME-BGC) have significantly improved our understanding of the possible consequences and responses of terrestrial ecosystems under different environmental conditions. However, problems with these models include only using site-based parameters and adopting different assumptions on interactions between plant, environmental conditions and human activities in simulating such complex phenomenon. In light of this shortfall, the overall objective of this research is to integrate remote sensing products into ecosystem process model in order to simulate productivity for the mixed grassland ecosystem in the landscape level. Data used includes 4-years of field measurements and diverse satellite data (System Pour l’Observation de la Terre (SPOT) 4 and 5, Landsat TM and ETM, Advanced Very High Resolution Radiometer (AVHRR) imagery). Using wavelet analyses, the study first detects that the dominant spatial scale is controlled by topography and thus determines that 20-30 m is the optimum resolution to capture the vegetation spatial variation for the study area. Second, the performance of the RDVI (Renormalized Difference Vegetation Index), ATSAVI (Adjusted Transformed Soil-Adjusted Vegetation Index), and MCARI2 (Modified Chlorophyll Absorption Ratio Index 2) are slightly better than the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating CAI (Cellulose Absorption Index) as a litter factor in ATSAVI, a new VI is developed (L-ATSAVI) and it improves LAI estimation capability by about 10%. Third, vegetation maps are derived from a SPOT 4 image based on the significant relationship between LAI and ATSAVI to aid spatial modeling. Fourth, object-oriented classifier is determined as the best approach, providing ecosystem models with an accurate land cover map. Fifth, the phenology parameters are identified for the study area using 22-year AVHRR data, providing the input variables for spatial modeling. Finally, the performance of popular ecosystem models in simulating grassland vegetation productivity is evaluated using site-based field data, AVHRR NDVI data, and climate data. A new model frame, which integrates remote sensing data with site-based BIOME-BGC model, is developed for the mixed grassland prairie. The developed remote sensing-based process model is able to simulate ecosystem processes at the landscape level and can simulate productivity distribution with 71% accuracy for 2005

    Quantification of a continuous-cover forest in Sweden using remote sensing techniques

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    Mapping and quantifying forest information about e.g. land cover, tree height and biomass has traditionally been a both time-consuming and labour-intensive part of forestry and forest research as field measurements typically are collected manually using handheld equipment. Remote sensing has proved to be a valuable complement to field based measurements as it enables for fast and relatively cheap collection of data from areas that would be hard to access from the ground. The aim of this thesis was to map and quantify the Romperöd forest outside Glimåkra in southern Sweden where selective thinning forestry has been practised since the 1960’s. The study was carried out using high resolution multispectral aerial images and small-footprint discrete-return LiDAR data included in the Swedish national elevation model in conjunction with field measurements. The results revealed a mixed forest where Norway spruce was the most dominating tree species, accounting for 40.2 % of the total coverage of the study area, followed by Scots pine (13.8 %), broadleaved trees (8.7 %), succession (6.7 %) and bare-ground (4.1 %). The elevation of the terrain varies between 76.2 and 107.3 meters above sea level, with a ridge extending from south to north. The canopy height of the forest varies greatly throughout the study area and ranged between 1.0 and 34.6 m with an average height of 15.1 m and a standard deviation of 8 m. Above-ground biomass (AGB) was estimated by fitting a multiple regression model to LiDAR-derived vegetation metrics (independent variables) and AGB estimates based on field measurements (dependent variable). The model managed to explain 70 % of the variability in the field measured AGB estimates and was applied to the entire study area yielding an average AGB of 122 900 kg/ha and a standard deviation of 50 497 kg/ha. The inclusion of remote sensing data improved the AGB estimates compared to those based solely on field measurements. The results were compared to the AGB data included in the SLU Forest Map which showed low correlation with AGB estimates based on field measurements (adjusted R2: 0.14), proving it unsuitable for the part of the Romperöd forest characterized by selective thinning.Att karlägga och kvantifiera skogsinformation angående exempelvis marktäcke, terräng, trädhöjder och volym är en traditionellt både tidskrävande och dyr del av skogsbruk och forskning eftersom mätningar vanligtvis samlas in i fält med handhållna instrument. Fjärranalys har visat sig vara ett värdefullt komplement till fältbaserade mätningar eftersom det möjliggör för snabb och relativt billig insamling av data från områden som skulle vara svåra att besöka i fält. Syftet med denna uppsats var att kartlägga och kvantifiera Romperödskogen utanför Glimåkra i nordöstra Skåne där blädningsskogsbruk har praktiserats sedan 1960-talet. Studien genomfördes med hjälp av fjärranalysdata i kombination med mätdata som samlats in i fält och blottlade en blandskog där gran utgör det dominerande trädslaget (40,2 % av studieområdet), följt av tall (13,8 %), lövträd (8,7 %), föryngringar (6,7 %) och bar mark (4,1 %). Terrängen varierar från 76,2 till 107,3 meter över havet med en ås som sträcker sig från söder till norr. Höjden på krontaket är heterogent i hela studieområdet och varierar mellan 1,0 och 34,6 m med en medelhöjd på 15,1 m och en standardavikelse på 8 m. Biomassa ovan jord uppskattades för hela studieområdet och visade ett genomsnitt på 122 900 kg/ha med en standardavikelse på 50 497 kg. Resultaten jämfördes med biomassa ovan jord enligt SLUs Skogskarta som visade låg överensstämmelse med skattningar baserade på fältmätningar vilket visar att SLU:s Skogskarta ej är applicerbar för den del av Romperödskogen som kännetecknas av blädning. Den föreslagna metodiken kan användas för att planera skogsbruk eller för att studera framtida förändringar eller störningar i skogen. Resultaten kan även vara till hjälp vid framtida forskning angående Romperödskogen och dess kolutbyte med atmosfären då biomassa ovan jord direkt kan konverteras till kolförråd, vilket är ett viktigt steg för att kunna studera effekten blädningsskogsbruk har på kolcykeln i skogen

    Analysis of the spatial heterogeneity of land surface parameters and energy flux densities

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    This work was written as a cumulative doctoral thesis based on reviewed publications. Climate projections are mainly based on the results of numeric simulations from global or regional climate models. Up to now processes between atmosphere and land surface are only rudimentarily known. This causes one of the major uncertainties in existing models. In order to reduce parameterisation uncertainties and to find a reasonable description of sub grid heterogeneities, the determination and evaluation of parameterisation schemes for modelling require as many datasets from different spatial scales as possible. This work contributes to this topic by implying different datasets from different platforms. Its objective was to analyse the spatial heterogeneity of land surface parameters and energy flux densities obtained from both satellite observations with different spatial and temporal resolutions and in-situ measurements. The investigations were carried out for two target areas in Germany. First, satellite data for the years 2002 and 2003 were analysed and validated from the LITFASS-area (Lindenberg Inhomogeneous Terrain - Fluxes between Atmosphere and Surface: a longterm Study). Second, the data from the experimental field sites of the FLUXNET cluster around Tharandt from the years 2006 and 2007 were used to determine the NDVI (Normalised Difference Vegetation Index for identifying vegetated areas and their "condition"). The core of the study was the determination of land surface characteristics and hence radiant and energy flux densities (net radiation, soil heat flux, sensible and latent heat flux) using the three optical satellite sensors ETM+ (Enhanced Thematic Mapper), MODIS (Moderate Resolution Imaging Spektroradiometer) and AVHRR 3 (Advanced Very High Resolution Radiometer) with different spatial (30 m – 1 km) and temporal (1 day – 16 days) resolution. Different sensor characteristics and different data sets for land use classifications can both lead to deviations of the resultant energy fluxes between the sensors. Thus, sensor differences were quantified, sensor adaptation methods were implemented and a quality analysis for land use classifications was performed. The result is then a single parameterisation scheme that allows for the determination of the energy fluxes from all three different sensors. The main focus was the derivation of the latent heat flux (L.E) using the Penman-Monteith (P-M) approach. Satellite data provide measurements of spectral reflectance and surface temperatures. The P-M approach requires further surface parameters not offered by satellite data. These parameters include the NDVI, Leaf Area Index (LAI), wind speed, relative humidity, vegetation height and roughness length, for example. They were derived indirectly from the given satellite- or in-situ measurements. If no data were available so called default values from literature were taken. The quality of these parameters strongly influenced the exactness of the radiant- and energy fluxes. Sensitivity studies showed that NDVI is one of the most important parameters for determination of evaporation. In contrast it could be shown, that the parameters as vegetation height and measurement height have only minor influence on L.E, which justifies the use of default values for these parameters. Due to the key role of NDVI a field study was carried out investigating the spatial variability and sensitivity of NDVI above five different land use types (winter wheat, corn, grass, beech and spruce). Methods to determine this parameter not only from space (spectral), but also from in-situ tower measurements (broadband) and spectrometer data (spectral) were compared. The best agreement between the methods was found for winter wheat and grass measurements in 2006. For these land use types the results differed by less than 10 % and 15 %, respectively. Larger differences were obtained for the forest measurements. The correlation between the daily MODIS-NDVI data and the in-situ NDVI inferred from the spectrometer and the broadband measurements were r=0.67 and r=0.51, respectively. Subsequently, spatial variability of land surface parameters and fluxes were analysed. The several spatial resolutions of the satellite sensors can be used to describe subscale heterogeneity from one scale to the other and to study the effects of spatial averaging. Therefore land use dependent parameters and fluxes were investigated to find typical distribution patterns of land surface properties and energy fluxes. Implying the distribution patterns found here for albedo and NDVI from ETM+ data in models has high potential to calculate representative energy flux distributions on a coarser scale. The distribution patterns were expressed as probability density functions (PDFs). First results of applying PDFs of albedo, NDVI, relative humidity, and wind speed to the L.E computation are encouraging, and they show the high potential of this method. Summing up, the method of satellite based surface parameter- and energy flux determination has been shown to work reliably on different temporal and spatial scales. The data are useful for detailed analyses of spatial variability of a landscape and for the description of sub grid heterogeneity, as it is needed in model applications. Their usability as input parameters for modelling on different scales is the second important result of this work. The derived vegetation parameters, e.g. LAI and plant cover, possess realistic values and were used as model input for the Lokalmodell of the German Weather Service. This significantly improved the model results for L.E. Additionally, thermal parameter fields, e.g. surface temperature from ETM+ with 30 m spatial resolution, were used as input for SVAT-modelling (Soil-Vegetation-Atmosphere-Transfer scheme). Thus, more realistic L.E results were obtained, providing highly resolved areal information.Die vorliegende Arbeit wurde auf der Grundlage begutachteter Publikationen als kumulative Dissertation verfasst. Klimaprognosen basieren im Allgemeinen auf den Ergebnissen numerischer Simulationen mit globalen oder regionalen Klimamodellen. Eine der entscheidenden Unsicherheiten bestehender Modelle liegt in dem noch unzureichenden Verständnis von Wechselwirkungsprozessen zwischen der Atmosphäre und Landoberflächen und dem daraus folgenden Fehlen entsprechender Parametrisierungen. Um das Problem einer unsicheren Modell-Parametrisierung aufzugreifen und zum Beispiel subskalige Heterogenität in einer Art und Weise zu beschreiben, dass sie für Modelle nutzbar wird, werden für die Bestimmung und Evaluierung von Modell-Parametrisierungsansätzen so viele Datensätze wie möglich benötigt. Die Arbeit trägt zu diesem Thema durch die Verwendung verschiedener Datensätze unterschiedlicher Plattformen bei. Ziel der Studie war es, aus Satellitendaten verschiedener räumlicher und zeitlicher Auflösung sowie aus in-situ Daten die räumliche Heterogenität von Landoberflächenparametern und Energieflussdichten zu bestimmen. Die Untersuchungen wurden für zwei Zielgebiete in Deutschland durchgeführt. Für das LITFASS-Gebiet (Lindenberg Inhomogeneous Terrain - Fluxes between Atmosphere and Surface: a longterm Study) wurden Satellitendaten der Jahre 2002 und 2003 untersucht und validiert. Zusätzlich wurde im Rahmen dieser Arbeit eine NDVI-Studie (Normalisierter Differenzen Vegetations Index: Maß zur Detektierung von Vegetationflächen, deren Vitalität und Dichte) auf den Testflächen des FLUXNET Clusters um Tharandt in den Jahren 2006 und 2007 realisiert. Die Grundlage der Arbeit bildete die Bestimmung von Landoberflächeneigenschaften und daraus resultierenden Energieflüssen, auf Basis dreier optischer Sensoren (ETM+ (Enhanced Thematic Mapper), MODIS (Moderate Resolution Imaging Spectroradiometer) und AVHRR 3 (Advanced Very High Resolution Radiometer)) mit unterschiedlichen räumlichen (30 m – 1 km) und zeitlichen (1 – 16 Tage) Auflösungen. Unterschiedliche Sensorcharakteristiken, sowie die Verwendung verschiedener, zum Teil ungenauer Datensätze zur Landnutzungsklassifikation führen zu Abweichungen in den Ergebnissen der einzelnen Sensoren. Durch die Quantifizierung der Sensorunterschiede, die Anpassung der Ergebnisse der Sensoren aneinander und eine Qualitätsanalyse von verschiedenen Landnutzungsklassifikationen, wurde eine Basis für eine vergleichbare Parametrisierung der Oberflächenparameter und damit auch für die daraus berechneten Energieflüsse geschaffen. Der Schwerpunkt lag dabei auf der Bestimmung des latenten Wärmestromes (L.E) mit Hilfe des Penman-Monteith Ansatzes (P-M). Satellitendaten liefern Messwerte der spektralen Reflexion und der Oberflächentemperatur. Die P-M Gleichung erfordert weitere Oberflächenparameter wie zum Beispiel den NDVI, den Blattflächenindex (LAI), die Windgeschwindigkeit, die relative Luftfeuchte, die Vegetationshöhe oder die Rauhigkeitslänge, die jedoch aus den Satellitendaten nicht bestimmt werden können. Sie müssen indirekt aus den oben genannten Messgrößen der Satelliten oder aus in-situ Messungen abgeleitet werden. Stehen auch aus diesen Quellen keine Daten zur Verfügung, können sogenannte Standard- (Default-) Werte aus der Literatur verwendet werden. Die Qualität dieser Parameter hat einen großen Einfluss auf die Bestimmung der Strahlungs- und Energieflüsse. Sensitivitätsstudien im Rahmen der Arbeit zeigen die Bedeutung des NDVI als einen der wichtigsten Parameter in der Verdunstungsbestimmung nach P-M. Im Gegensatz dazu wurde deutlich, dass z. B. die Vegetationshöhe und die Messhöhe einen relativ kleinen Einfluss auf L.E haben, so dass für diese Parameter die Verwendung von Standardwerten gerechtfertigt ist. Aufgrund der Schlüsselrolle, welche der NDVI in der Bestimmung der Verdunstung einnimmt, wurden im Rahmen einer Feldstudie Untersuchungen des NDVI über fünf verschiedenen Landnutzungstypen (Winterweizen, Mais, Gras, Buche und Fichte) hinsichtlich seiner räumlichen Variabilität und Sensitivität, unternommen. Dabei wurden verschiedene Bestimmungsmethoden getestet, in welchen der NDVI nicht nur aus Satellitendaten (spektral), sondern auch aus in-situ Turmmessungen (breitbandig) und Spekrometermessungen (spektral) ermittelt wird. Die besten Übereinstimmungen der Ergebnisse wurden dabei für Winterweizen und Gras für das Jahr 2006 gefunden. Für diese Landnutzungstypen betrugen die Maximaldifferenzen aus den drei Methoden jeweils 10 beziehungsweise 15 %. Deutlichere Differenzen ließen sich für die Forstflächen verzeichnen. Die Korrelation zwischen Satelliten- und Spektrometermessung betrug r=0.67. Für Satelliten- und Turmmessungen ergab sich ein Wert von r=0.5. Basierend auf den beschriebenen Vorarbeiten wurde die räumliche Variabilität von Landoberflächenparametern und Flüssen untersucht. Die unterschiedlichen räumlichen Auflösungen der Satelliten können genutzt werden, um zum einen die subskalige Heterogenität zu beschreiben, aber auch, um den Effekt räumlicher Mittelungsverfahren zu testen. Dafür wurden Parameter und Energieflüsse in Abhängigkeit der Landnutzungsklasse untersucht, um typische Verteilungsmuster dieser Größen zu finden. Die Verwendung der Verteilungsmuster (in Form von Wahrscheinlichkeitsdichteverteilungen – PDFs), die für die Albedo und den NDVI aus ETM+ Daten gefunden wurden, bietet ein hohes Potential als Modellinput, um repräsentative PDFs der Energieflüsse auf gröberen Skalen zu erhalten. Die ersten Ergebnisse in der Verwendung der PDFs von Albedo, NDVI, relativer Luftfeuchtigkeit und Windgeschwindigkeit für die Bestimmung von L.E waren sehr ermutigend und zeigten das hohe Potential der Methode. Zusammenfassend lässt sich feststellen, dass die Methode der Ableitung von Oberflächenparametern und Energieflüssen aus Satellitendaten zuverlässige Daten auf verschiedenen zeitlichen und räumlichen Skalen liefert. Die Daten sind für eine detaillierte Analyse der räumlichen Variabilität der Landschaft und für die Beschreibung der subskaligen Heterogenität, wie sie oft in Modellanwendungen benötigt wird, geeignet. Ihre Nutzbarkeit als Inputparameter in Modellen auf verschiedenen Skalen ist das zweite wichtige Ergebnis der Arbeit. Aus Satellitendaten abgeleitete Vegetationsparameter wie der LAI oder die Pflanzenbedeckung liefern realistische Ergebnisse, die zum Beispiel als Modellinput in das Lokalmodell des Deutschen Wetterdienstes implementiert werden konnten und die Modellergebnisse von L.E signifikant verbessert haben. Aber auch thermale Parameter, wie beispielsweise die Oberflächentemperatur aus ETM+ Daten in 30 m Auflösung, wurden als Eingabeparameter eines Soil-Vegetation-Atmosphere-Transfer-Modells (SVAT) verwendet. Dadurch erhält man realistischere Ergebnisse für L.E, die hochaufgelöste Flächeninformationen bieten

    AN INVESTIGATION OF REMOTELY SENSED URBAN HEAT ISLAND CLIMATOLOGY

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    Satellite remotely sensed temperatures are widely used for urban heat island (UHI) studies. However, the abilities of satellite surface and atmospheric data to assess the climatology of UHI face many unknowns and challenges. This research addresses the problems and potential for satellite remotely sensed UHI climatology by examining three different issues. The first issue is related to the temporal aggregation of land surface temperature (LST) and the potential biases that are induced on the surface UHI (SUHI) intensity. Composite LST data usually are preferred to avoid the missing values due to clouds for long-term UHI monitoring. The impact of temporal aggregation shows that SUHI intensities are more notably enhanced in the daytime than nighttime with an increasing trend for larger composite periods. The cause of the biases is highly related to the amount and distribution of clouds. The second issue is related to model validation and the appropriate use of LST for comparison to modeled radiometric temperatures in the urban environment. Sensor view angle, cloud distribution, and cloud contaminated pixels can confound comparisons between satellite LST and modeled surface radiometric temperature. Three practical sampling methods to minimize the confounding factors are proposed and evaluated for validating different aspects of model performance. The third issue investigated is to assess to what extent remotely sensed atmospheric profiles collected over the urban environment can be used to examine the UHI. The remotely sensed air and dew-point temperatures are compared with the ground observations, showing an ability to capture the temporal and spatial dynamics of atmospheric UHI at a fine scale. Finally, a new metric for quantifying the urban heat island is proposed. The urban heat island curve (UHIC), is developed to represent UHI intensity by integrating the urban surface heterogeneity in a curve. UHIC illustrates the relationship between the air temperature and the urban fractions, and emphasizes the temperature gradients, consequently decreasing the impact of the data biases. This research illustrates the potential for satellite data to monitor and increase our understanding of UHI climatology

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

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    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

    Parameterization of the Satellite-Based Model (METRIC) for the Estimation of Instantaneous Surface Energy Balance Components over a Drip-Irrigated Vineyard

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    A study was carried out to parameterize the METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) model for estimating instantaneous values of albedo (shortwave albedo) (αi), net radiation (Rni) and soil heat flux (Gi), sensible (Hi) and latent heat (LEi) over a drip-irrigated Merlot vineyard (location: 35°25′ LS; 71°32′ LW; 125 m.a.s. (l). The experiment was carried out in a plot of 4.25 ha, processing 15 Landsat images, which were acquired from 2006 to 2009. An automatic weather station was placed inside the experimental plot to measure αi, Rni and Gi. In the same tower an Eddy Covariance (EC) system was mounted to measure Hi and LEi. Specific sub-models to estimate Gi, leaf area index (LAI) and aerodynamic roughness length for momentum transfer (zom) were calibrated for the Merlot vineyard as an improvement to the original METRIC model. Results indicated that LAI, zom and Gi were estimated using the calibrated functions with errors of 4%, 2% and 17%, while those were computed using the original functions with errors of 58%, 81%, and 5%, respectively. At the time of satellite overpass, comparisons between measured and estimated values indicated that METRIC overestimated αi in 21% and Rni in 11%. Also, METRIC using the calibrated functions overestimated Hi and LEi with errors of 16% and 17%, respectively while it using the original functions overestimated Hi and LEi with errors of 13% and 15%, respectively. Finally, LEi was estimated with root mean square error (RMSE) between 43 and 60 W·m−2 and mean absolute error (MAE) between 35 and 48 W·m−2 for both calibrated and original functions, respectively. These results suggested that biases observed for instantaneous pixel-by-pixel values of Rni, Gi and other intermediate components of the algorithm were presumably absorbed into the computation of sensible heat flux as a result of the internal self-calibration of METRIC

    Spatial and Temporal Distribution of Groundwater Recharge in the West Bank Using Remote Sensing and GIS Techniques

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    Estimating groundwater recharge to aquifer systems is a very important element in assessing the water resources of the West Bank. Of particular interest is the sustainable yield of the aquifers. Previous studies have developed analytical recharge models that are based on the long-term annual rainfall data. These models have been shown to be inadequate and changes over shorter periods, e.g. monthly estimates, must be known in order to study the temporal distribution of recharge. The approach used in this research integrates data derived from satellite images (e.g. land cover, evapotranspiration, rainfall, and digital elevation model) with hydrogeological data in a Geographic Information System (GIS) model to identify and map the surface recharge areas. The Surface Energy Balance Algorithm for Land (SEBAL) is applied to time series of remote sensing MODerate Resolution Imaging Spectroradiometer (MODIS) level 3 data of reflectance and surface temperature measurements to estimate monthly evapotranspiration; precipitation is derived from the monthly data sets of the Tropical Rainfall Measuring Mission (TRMM); runoff is given assumed values of 0.75 mm month-1 and 0.4 mm month-1 for the months of January and February, respectively. Recharge is quantified from November until March by applying the water balance method where evapotranspiration estimates and runoff are subtracted from precipitation. Results show good agreement between data reported in the literature and remote sensing-based analysis. Empirical models that are based on long term rainfall measurements suggest recharge values between 800 and 836 MCM yr-1 while the remote sensing based model results estimate recharge to be 700 MCM yr-1. The Western, North-Eastern, and Eastern Aquifer Basins receive 30%, 23%, and 47% of the total calculated recharge while percentages available in the literature provide 49%, 22%, and 29%, respectively. Discrepancies are mainly due to lack of field data, the overestimation of actual evapotranspiration, and underestimation of TRMM precipitation values. The recharge map indicates that the most effective groundwater recharge zones are located in the north and west of the area that is characterised by thick and well developed soil deposits, heavy vegetation, and a sub-humid climate with the potential of significant recharge occurring during the wet season. Some areas in the east include concentration of drainage and stream flows which increase the ability of to recharge the groundwater system. The least effective areas are in the south and south-west region that is more arid with much less recharge, mainly due to its isolated thin soil deposits. A sensitivity analysis was carried out to demonstrate the impact of land cover change on groundwater and natural recharge. The assessment involved the use of land covers of 1994 and 2004 with the same fixed parameters of evapotranspiration, precipitation, drainage, slope, soil, and geology. Results show a decrease in high and intermediate high recharge areas from 40.25 km2 and 2462.25 km2 in year 1994 to 15.5 km2 and 1994 km2 in 2004, respectively. This illustrates the extent of land cover/land use change influence on recharge and calls for integrated plans and strategies to preserve recharge at least at its current rates
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