Abstract Proliferation of evapotranspiration (ET) products warrants comparison of these products. The study objective was to assess uncertainty in ET output from four landsurface models (LSMs), Noah, Mosaic, VIC, and SAC in NLDAS-2, two remotesensing-based products, MODIS and AVHRR, and GRACE-inferred ET from a water budget with precipitation from PRISM, monitored runoff, and total water storage change (TWSC) fromGRACEsatellites. The three cornered hat method, which does not require a priori knowledge of the true ET value, was used to estimate ET uncertainties. In addition, TWSC or total water storage anomaly (TWSA) fromGRACE was compared with water budget estimates of TWSC from a ﬂux-based approach or TWSA from a storage-based approach. The analyses were conducted using data from three regions (humid-arid) in the South Central United States as case studies. Uncertainties in ET are lowest in LSM ET (5 mm/mo), moderate in MODIS or AVHRR-based ET (10–15 mm/mo), and highest in GRACE- inferred ET (20–30 mm/month). There is a trade-off between spatial resolution and uncertainty, with lower uncertainty in the coarser-resolution LSM ET (14 km) relative to higher uncertainty in the ﬁner-resolution (1–8 km) RS ET. Root-mean-square (RMS) of uncertainties in water budget estimates of TWSC is about half of RMS of uncertainties in GRACE-derived TWSC for each of the regions. Future ET estimation should consider a hybrid approach that integrates strengths of LSMs and satellite-based products to constrain uncertainties.
Abstract. Evapotranspiration (ET) may be used as an ecological indicator to address the ecosystem complexity. The accurate measurement of ET is of great significance for studying environmental sustainability, global climate changes, and biodiversity. Remotesensing technologies are capable of monitoring both energy and water fluxes on the surface of the Earth. With this advancement, existing mod- els, such as SEBAL, S SEBI and SEBS, enable us to estimate the regional ET with limited temporal and spatial coverage in the study areas. This paper extends the existing modeling ef- forts with the inclusion of new components for ET estimation at different temporal and spatial scales under heterogeneous terrain with varying elevations, slopes and aspects. Follow- ing a coupled remotesensing and surface energy balance approach, this study emphasizes the structure and function of the Surface Energy Balance with Topography Algorithm (SEBTA). With the aid of the elevation and landscape infor- mation, such as slope and aspect parameters derived from the digital elevation model (DEM), and the vegetation cover derived from satellite images, the SEBTA can account for the dynamic impacts of heterogeneous terrain and changing land cover with some varying kinetic parameters (i.e., rough- ness and zero-plane displacement). Besides, the dry and wet pixels can be recognized automatically and dynamically in image processing thereby making the SEBTA more sensi- tive to derive the sensible heat flux for ET estimation. To prove the application potential, the SEBTA was carried out
While the methods so far presented consider the observed surface as a single- layer, TSEB scheme is a two-source approach based on landsurface separation into two distinct, but linked components (soil surfaces and vegetation canopies), aimed at modeling in a more physically meaningful way surface resistance characteristics. TSEB derives energy flux estimates frommodeling the land as a resistance network, between energy sources from soil, vegetation, and the overlying atmosphere. Two main variants of TSEB exist, one strictly applicable at local scales (as described in Norman et al., 1995), while the other, known as DisAlexi (Anderson et al., 1997; Mecikalski et al., 1999) is also useful at regional scales since it also models energy exchange at the atmospheric boundary layer. TSEB has three key assumptions: turbulent fluxes are constant within the near surface layer (Monin–Obukhov similarity is used for stability correction), radiometric temperature can be repartitioned into soil and vegetation components, and Priestley–Taylor transpiration (Priestley and Taylor, 1972) is applied for unstressed vegetation.
Model performance evaluation by comparing model-simulated outputs with corresponding observations is fundamental to hydrological model calibration. For decades, remotesensing images were frequently used for hydrological state variables or heat fluxes in water cycle retrieval, especially in basins with sparse or few data available. These remotesensing based outputs are quite a popular alternative to traditional observations gauged from climate stations. They are superior to traditional observations and model simulations in the space scale, since the latter are both interpolated grid outputs whose spatial patterns depend on the locations and numbers of climate stations. However, multiple factors such as the scan cycle of satellites, the running status of the equipment and the weather conditions at the data acquisition time lead to only a limited number of high quality remotesensing images in specific time periods being available. Nouri et al. (2014) summarized that the uncertainty in aerodynamic components estimation and errors in narrow vegetation areas, such as riparian zones measurement, are also noted shortages of use of remotesensing techniques to measure evapotranspiration.
Abstract. In the current work we investigate the utility of remote-sensing-based surface parameters in the Noah UCM (urban canopy model) over a highly developed urban area. Landsat and fused Landsat–MODIS data are utilized to gen- erate high-resolution (30 m) monthly spatial maps of green vegetation fraction (GVF), impervious surface area (ISA), albedo, leaf area index (LAI), and emissivity in the Los An- geles metropolitan area. The gridded remotely sensed param- eter data sets are directly substituted for the land-use/lookup- table-based values in the Noah-UCM modeling framework. Model performance in reproducing ET (evapotranspiration) and LST (landsurface temperature) fields is evaluated uti- lizing Landsat-based LST and ET estimates from CIMIS (California Irrigation Management Information System) sta- tions as well as in situ measurements. Our assessment shows that the large deviations between the spatial distributions and seasonal fluctuations of the default and measured pa- rameter sets lead to significant errors in the model predic- tions of monthly ET fields (RMSE = 22.06 mm month −1 ). Results indicate that implemented satellite-derived parameter maps, particularly GVF, enhance the capability of the Noah UCM to reproduce observed ET patterns over vegetated areas in the urban domains (RMSE = 11.77 mm month −1 ). GVF plays the most significant role in reproducing the observed ET fields, likely due to the interaction with other parameters in the model. Our analysis also shows that remotely sensed GVF and ISA improve the model’s capability to predict the LST differences between fully vegetated pixels and highly developed areas.
analysis using different values for d and concluded that estimates of H were less sensitive to changes in d. Note that the vineyard was rather short with maximum vegetation height of about 1.0 m and leaf area index (LAI) of 0.4. Also because of the irregular terrain along the path, De Bruin et al. (1995) used weighted average effective beam height by utilizing topographic maps adding to the uncertainty in their estimates of H. Note that, unlike the studies by Meijninger et al. (2002a, 2002b, 2006), in which the scintillometers were installed well above the surface and thus reliable estimates can be obtained with the free convection formula, in De Bruin et al. (1995) it was installed relatively close to surface at about 3.25 m. Hartogensis et al. (2003) developed formulas to properly estimate scintillometer effective height, z eff , considering the effects of the slanted path of the scintillometer beam height, non-flat terrain, and the stability conditions that lead to improved estimates of H. Their analysis was carried over the La Poza region in Mexico, a region characterized by heterogeneous landsurface and variable terrain, where z 0
Landsurface energy balance (EB) models, using remotesensing data from ground, airborne, or satellite platforms at different spatial resolutions, have been found to be promising for mapping daily and seasonal ET at a regional scale. In this article, a brief review of numerous remotesensing based models was made to assess the current status of research, the underlying principle for each method, their data requirements, and their strengths and weaknesses. Reliable regional ET estimates are essential to improve spatial crop water management. The spatial and temporal remotesensing data from the existing set of earth observing satellite sensors are not sufficient enough to use their ET products for irrigation scheduling. This will be constrained further by the possible disappearance of thermal sensors on future Landsat satellites. However, research opportunities exist to improve the spatial and temporal resolution of ET by developing data fusion/ subpixel extraction algorithms to improve spatial resolution of surface temperature data derived from Landsat/ASTER/ MODIS thermal images using same/other sensor high resolution visible, NIR, and SWIR images.
The effects of surface heterogeneity in ET estimation have been studied here by employing the IPUS, TRFA, and TSFA methods over heterogeneous surface. Compared to the IPUS and TRFA methods, the TSFA method exhibits more con- sistent agreement with in situ measurements (energy balance forced by the residual closure method) based on the footprint validation results. The IPUS approach does not consider sur- face heterogeneity at all, which causes significant error in the heat fluxes (i.e., 186 W m −2 ). The TRFA considers het- erogeneity of landscapes besides LST heterogeneity, with a heat flux error (i.e., 49 W m −2 ) that is less than that of IPUS. However, this error is non-negligible. As a sensitive variable of the ET model, canopy height is mainly determined by clas- sification, and the application of classification at a 30 m res- olution can improve the accuracy of the canopy height. Ad- ditionally, the sharpened surface temperature at a resolution of 30 m decreases the thermodynamic uncertainty caused by the landsurface. The TSFA method can capture the hetero- geneities of the landsurface and integrate the effects of land- scapes in mixed pixels that are neglected at coarse spatial resolutions.
Abstract. Four upscaling methods for estimating daytime ac- tual evapotranspiration (ET) from single time-of-day snap- shots, as commonly retrieved using remotesensing, were compared. These methods assume self-preservation of the ra- tio between ET and a given reference variable over the day- time hours. The analysis was performed using eddy covari- ance data collected at 12 AmeriFlux towers, sampling a fairly wide range in climatic and land cover conditions. The choice of energy budget closure method significantly impacted per- formance using different scaling methodologies. Therefore, a statistical evaluation approach was adopted to better ac- count for the inherent uncertainty in ET fluxes using eddy covariance technique. Overall, this approach suggested that at-surface solar radiation was the most robust reference vari- able amongst those tested, due to high accuracy of upscaled fluxes and absence of systematic biases. Top-of-atmosphere irradiance was also tested and proved to be reliable under near clear-sky conditions, but tended to overestimate the ob- served daytime ET during cloudy days. Use of reference ET as a scaling flux yielded higher bias than the solar radia- tion method, although resulting errors showed similar lack of seasonal dependence. Finally, the commonly used evap- orative fraction method yielded satisfactory results only in summer months, July and August, and tended to underesti- mate the observations in the fall/winter seasons from Novem- ber to January at the flux sites studied. In general, the pro- posed methodology clearly showed the added value of an in- tercomparison of different upscaling methods under scenar- ios that account for the uncertainty in eddy covariance flux measurements due to closure errors.
soil are considered as a single pixel, or two source (e.g. At- mospheric Land Exchange Inverse Model; Anderson et al., 1997), meaning vegetation and soil are considered at the sub-pixel level. Indirect methods, though accurate at multi- ple scales, can be highly uncertain in heterogeneous regions and difficult to implement operationally at regional to global scales, because of landsurface temperature uncertainty and scale dependencies, as well as the need for extensive ground- based meteorological data (Kite and Droogers, 2000). Di- rect remotesensing methods have been gaining popularity, as they do not suffer from scale dependencies and can read- ily be driven by global scale data. Direct approaches estimate ET using a series of energy and moisture constraints on at- mospheric demand (i.e. potential evapotranspiration – PET). Algorithms which produce global estimates of ET in this way are detailed in Nishida et al. (2003), Leuning et al. (2008), Mu et al. (2007a), and Fisher et al. (2008). These models have been used in sub-Saharan Africa to estimate water use efficiency for arid rangelands (Palmer and Yanusa, 2011) and to extrapolate biological nitrogen deposition from wildfires (Chen et al., 2010). The major drawback of these approaches and remotesensing methods in the tropics in general is the presence of cloud cover, which can often obscure a target from the sensor for several days. Landsurface models over- come this drawback by using long-term monthly averages of remotesensing data and simulated alternatives.
The study area, the town of Wadi ad-Dawasir, is located on the plateau of Najd at Lat 44 ◦ 43 0 and Lon 20 ◦ 29 0 ; about 300 km south of the capital city Riyadh (Fig. 1). This study area comprised of gravelly tableland disconnected by insignificant sandy oases and isolated mountain bundles. Across the Arabian Peninsula the tableland slopes toward the east from an elevation of 1360 m in the west to 750 m at its easternmost limit. Wadi ad-Dawasir and Wadi al-Rummah are the most important remaining riverbeds in the study area. Wadi ad-Dawasir and Najran regions are the major irriga- tion water abstraction from Al-Wajid aquifer. Agriculture in the Wadi ad-Dawasir area consists of technically highly de-
Despite the considerable advances made in the development of classification algorithms, including neural networks, the accuracy of land cover type maps derived from remotely sensed imagery is still often insufficient for operational application (Wilkinson, 1996). Further increases in accuracy may be made by refinements to the training and testing stages of the classification (e.g., training set refinement to remove or down-weight ambiguous training samples, acquisition of larger training and testing sets). A major limit to the accuracy of digital image classifications has, however, been the tendency to use only spectral information in the classification (Curran et al., 1998). The classifications have, therefore, tended to use only the amount of radiation recorded for each pixel in each spectral waveband to discriminate between land cover types. Thus, while human interpretation uses tone, texture and context most digital image classifications have used only tonal information which may be relatively uninformative. Image texture, which describes simply the local variability of image tone, can be quantified with relative ease from remotely sensed imagery (Mather, 1999a). Popular measures of image texture range from simple measures of tonal variability within a local window, particularly those based on grey level co-occurrence matrices (Haralick and Shanmugam, 1974; Holmes et al., 1984) through to geostatistical descriptors of the local variability of image tone (Miranda and Carr, 1994; Berberoglu et al., 2000; Lloyd et al., 2000). Context describes a range of features such as the size, shape and location of objects to be classified. Although it is difficult to quantify context from remotely sensed imagery it may be used to increase significantly the accuracy with which land cover type is mapped (Gurney and Townshend, 1983; Harris, 1985; Groom et al., 1996). In addition to image based information there may be various ancillary data that can enhance class separability. For example, ancillary data on altitude or soil type may help discriminate vegetation communities that have a similar appearance in the imagery but are known to be located in different environments. The integration of ancillary information into the analysis has often been found to increase the accuracy of a variety of classifications (Strahler, 1980; Hutchinson, 1982; Peddle et al., 1994). Despite these refinements to the techniques available no classification is ideal, this is because there remain several fundamental problems with classification as a tool for land cover mapping (Foody, 1999; Mather, 1999b).
Figure 1-1 Ternary feature space visualization of V-I-S model and its relation to urban land use (Source: Ridd 1995) ........................................................................................... 5 Figure 1-2 Research design and data flows .................................................................... 20 Figure 2-1 Evaluation Framework. (a) The evaluation of input features; (b) the evaluation of classifiers. ................................................................................................................. 25 Figure 2-2 Kappa-Error Diagram, image courtesy of Margineantu and Dietterich (1997) ...................................................................................................................................... 29 Figure 2-3 Illustration of SVM classifier in a two-class linear separable case; and grey features are the support vectors. ..................................................................................... 35 Figure 3-1 Study area of New Orleans and Baton Rouge. (Landsat 22/39 and 23/39) ..... 41 Figure 3-2 V-I-S fraction images and LST image. ......................................................... 45 Figure 3-3 Classification results with only multispectral reflectance variable used as the input. Red circles mark the “salt-and-pepper” effect from the image classification. Red rectangles mark the salient misclassification regions...................................................... 48 Figure 3-4 Classification results with V-I-S fractions and landsurface temperature. As compared to figure 4, the “salt and pepper” problem is alleviated. More homogenous area of vegetated area to the southwest of the study area is produced. The salient misclassification regions in Figure 3.3 are also eliminated. ............................................ 49 Figure 3-5 Accuracy and stability comparisons of five classifiers for three different input feature configurations (New Orleans, LA). .................................................................... 54 Figure 3-6 Kappa-Error diagrams produced by three different input feature configurations: (a). Multispectral reflectance only; (b). V-I-S fractions and LST combination; (c). Composite of (a) and (b), e.g., all features being included. ............................................ 55 Figure 3-7 Slice density maps of distribution of V-I-S fractions and LST in the study area. The distribution is Gaussian-like and each bump represents a certain land use pattern. (a) “Impervious surface” fraction as x and “Vegetation” fraction as y; (b) “Impervious surface” fraction as x and “Low Albedo + Soil” fraction as y. Land use classes are labeled with (A) Water; (B) Vegetated; (C) Residential; (D) Commercial. .................... 58 Figure 3-8 (a). Tree fitted with cp=0.001 and “Gini index” criteria; (b). Tree fitted with
For mapping salt affected soils in Punjab, Pakistan, Khan et al. (2001) used IRS-LISS II digital data and different remotesensing derived indices such as salinity index (SI), Normalized Difference Salinity Index (NDSI), Brightness Index (BI), Normalized Difference Vegetation Index (NDVI). Sahai el al. (1985), studied the impact of canal irrigation on ecosystem of Ukai-Kakrapar command area in Gujarat due to waterlogging/salinity using multidate, mutispectral Landsat imagery of pre and post monsoon period for 1972-82. Sharma and Bhargawa (1988), followed a collective approach comprising the Landsat 2 MSS FCC, Survey of India Topographical maps and limited field checks for mapping saline soils and wetlands. Their results showed that because of their distinct coloration and unique pattern on FCC imageries the separation of saline and waterlogged soil was possible. Dwivedi(1996), Rathore and Jain(2001), Sharma and Bhargawa (1988); found that the development of aerial photographs and the subsequent advances in satellite remotesensing and image processing techniques have enabled the detection, mapping and monitoring of waterlogged areas. Mutlaq (2002) assessed degradation in Mathura district, Uttar Pradesh, India through visual interpretation and digital image processing. He could identify different types, extent and degree of degradation. Bai and Dent (2006) reported on a pilot study done in Kenya during the global assessment of land degradation in dry lands. Ajai et al. (2009) successfully identified different types of degradation in India through remotesensing techniques. Many approaches used for mapping land degradation such as visual interpretation, unsupervised, supervised classification and remotesensing derived indices (Gupta et al. 1998 ; Saini et al. 1999 ; Porarinsdottir, 2008 ; Jafari et al. 2008 and Koshal, 2010).
This study investigated the temporal and spatial changes of landsurface tem- perature (LST) over Calabar Metropolis, Nigeria (2002 to 2016). The LST over Calabar metropolis was studied from the analysis of Landsat imageries of the investigated years (2002, 2006, 2008, 2010, 2012, 2014 and 2016). The re- sults of the LST imagery were classified using standard deviation. GIS was further applied to extract the coverage ratio of each land use in the context of Landsurface temperature (LST) pixels and results were presented in degree Celsius. The result of this study revealed a great variation in the mean LST for the selected period. The highest mean LST of 25.38˚C was observed in 2016, followed by 2002 with mean LST of 25.32˚C whereas, the least LST was ob- served in 2010. This study has shown that, the changing land use pattern of the area is capable of affecting certain characteristics of the environment such as surface temperature. The study recommends that effort should be made by the government to increase urban vegetation in order to reduce potential fu- ture increased in LST.
Raw device mapping (RDM) is an option in the VMware server virtualization environment that enables a storage logical unit number (LUN) to be directly connected to a virtual machine (VM) from the storage area network (SAN). RDM, which permits the use of existing SAN commands, is generally used to improve performance in I/O-intensive applications. There are 2 types of Raw Device Mapping (RDM) Compatibility Mode
Modeling and assessment of land use/cover and its impacts play a crucial role in land use planning and formulation of sustainable land use policies. In this study, remotesensing data were used within geographic information system (GIS) to map and predict land use/cover changes near Amman, where half of Jordan’s population is living. Images of Landsat TM, ETM+ and OLI were processed and visually interpreted to derive land use/cover for the years 1983, 1989, 1994, 1998, 2003 and 2013. The output maps were analyzed by using GIS and cross-tabulated to quantify land use/cover changes for the different periods. The main changes that altered the character of land use/cover in the area were the ex- pansion of urban areas and the recession of forests, agricultural areas (after 1998) and rangelands. The Markov chain was used to predict future land use/cover, based on the historical changes during 1983-2013. Results showed that pre- diction of land use/cover would depend on the time interval of the multi-temporal satellite imagery from which the probability of change was derived. The error of prediction was in the range of 2% - 5%, with more accurate prediction for urbanization and less accurate prediction for agricultural areas. The trends of land use/cover change showed that urban areas would expand at the expense of agricultural land and would form 33% of the study area (50 km × 60 km) by year 2043. The impact of these land use/cover changes would be the increased water demand and wastewater generation in the future.
Distributed hydrologic models require spatial distribution of meteorological and geographical elements such as temperature, precipitation, humidity, solar radiation, and other observation data as their main inputs or forcing parameters. Because traditional hydrologic data are point/field measurements, hydrologic analysis are limited by spatial data availability. Satellite remotesensing data have emerged as a viable alternative or supplement to in situ observations due to its availability for implementation and calibration of hydrologic models over vast ungauged regions. Distributed hydrologic model demands are often met by integrating GIS and remotesensing products. Landsat (mainly TM and ETM+), Satellite Pour l’Observation de la Terre (SPOT), MODIS, National Oceanic and Atmospheric Administration - Advanced Very High Resolution Radiometer (NOAA–AVHRR), IKONOS, and QuickBird are commonly used remotesensing products in data acquisition tasks. Scientists monitor patterns of land cover change over space and time at regional, national, and global scales using satellite and other remotely sensed data (Slonecker, et al., 2013).
The main objective of this paper was to evaluate the daily actual evapotranspiration (ET) accuracy obtained by remotesensing algorithms when compared with Bowen ratio measurements per- formed in the cotton fields. The experiment was conducted in a cotton experimental field of EMBRAPA located in Ceará State, Brazil. Seven TM Landsat-5 images acquired in 2005 and 2008 were used to perform SEBAL and SSEB algorithms. The comparison between the estimated values by remoting sensing algorithms and the measured values in situ showed an acceptable accuracy. Besides, SSEB algorithm showed to be an important tool for ET analysis in the semi-arid regions, due to the fact that it does not need the meteorological data to solve the energy balance, but only the average temperature of the “hot” and “cold” pixels. Additionally, SSEB presents simpler pro- cessing than SEBAL algorithm that needs to solve an iterative process to obtain the sensible heat flux values.
Sunspots are high magnetic field areas in the sun with comparatively lower temperature with a diameter of about 37000 km and they occur periodically. Many studies revealed that the earth’s climate is significantly related to sunspot activity which is represented by sunspot number. According to the recent researches , it is shown that the global temperature would rise as the sunspot number increases. There are more sunspots during the time of increased magnetic activity and as a result sun’s radiant energy is increased and this increases atmospheric temperature. As a result during the period of high sunspot number the probability of occurrence of drought is higher and vice versa. In the present study, Standardised Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) obtained from MODIS data (MOD13Q1) and sunspot number has been considered for drought modelling. Two multiple linear regression models for predicting agricultural drought are developed for Kharif season. Crop yield model was developed from the predicted NDVI for the major crops and was validated with the actual yield.