Wim G.M. Bastiaanssen

A remote sensing surface energy balance algorithm for land (SEBAL); 1 formulation

The major bottlenecks of existing algorithms to estimate the spatially distributed surface energy balance in composite terrain by means of remote sensing data are briefly summarised. The relationship between visible and thermal infrared spectral radiances of areas with a sufficiently large hydrological contrast (dry and wet land surface types, vegetative cover is not essential) constitute the basis for the formulation of the new Surface Energy Balance Algorithm for Land (SEBAL). The new algorithm (i) estimates the spatial variation of most essential hydro-meteorological parameters empirically, (ii) requires only field information on short wave atmospheric transmittance, surface temperature and vegetation height, (iii) does not involve numerical simulation models, (iv) calculates the fluxes independently from land cover and (v) can handle thermal infrared images at resolutions between a few meters to a few kilometers. The empirical relationships are adjusted to different geographical regions and time of image acquisition. Actual satellite data is inserted in the derivation of the regression coefficients. Part 2 deals with the validation of SEBAL. q 1998 Elsevier Science BV. All rights reserved.

SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey

Surface Energy Balance Algorithm for Land (SEBAL) is a relatively new parameterization of surface heat fluxes based on spectral satellite measurements. SEBAL requires spatially distributed, visible, near-infrared and thermal infrared data, which can be taken from Landsat Thematic Mapper. The SEBAL parameterization is an iterative and feedback-based numerical procedure that deduces the radiation, heat and evaporation fluxes. The sensible and latent heat fluxes across the lower Gediz River Basin in Western Turkey have been estimated. The energy balance during satellite overpass, and the integrated 24 h fluxes are computed on a pixel-by-pixel basis. The temporal variability in heat fluxes between June and August will be evaluated. The effect of irrigation on the partitioning of energy and crop water stress is discussed.

Irrigation Performance Indicators Based on Remotely Sensed Data: a Review of Literature

The earlier generation of irrigation performanceindicators was based on canal flow data. Commonly,they quantify performance in a command area downstreamof a discharge measurement device. Remote sensingdeterminants, such as actual evapo-transpiration, soilwater content and crop growth reflect the overallwater utilization at a range of scales, up to fieldlevel. Crop evapo-transpiration includes wateroriginating from irrigation supply, water fromprecipitation, groundwater and water withdrawn fromthe unsaturated zone. Hence, this is a refinement inspatial scale as compared to the classicallycollected flow measurements, and describes moreoverdepletion from all water resources. If thesepossibilities are well implemented, we expect that anew generation of irrigation performance indicatorscan be quantified in a cost-effective manner.Especially, because satellite measurements pave a wayto standardize data collection between differentirrigation schemes and among different countries atcosts which are currently decreasing. These challengescan only turn into a success if irrigation managersare involved in pilot projects and demonstrationstudies exploring satellite data.

Is large-scale inverse modelling of unsaturated flow with areal average evaporation and surface soil moisture as estimated from remote sensing feasible?

Abstract The potentiality of combining large-scale inverse modelling of unsaturated flow with remote sensing determination of areal evaporation and areal surface moisture is assessed. Regional latent and sensible heat fluxes are estimated indirectly using remotely sensed measurements by parameterizing the surface energy balance equation. An example of evapotranspiration mapping from northern and central Egypt is presented. The inverse problem is formulated with respect to the type of information available. Two examples of estimation of soil hydraulic properties by the dynamic one-dimensional soil-water-vegetation model SWATRE are given: one refers to a classical lysimeter scale and another one to a catchment scale. It is concluded that small-scale soil physics may describe large-scale hydrological behaviour adequately, and that the effective hydraulic parameters concerned may be derived by an inverse modelling approach. Remotely sensed data on surface reflectance, surface temperature and soil moisture content derived from multifrequency microwave techniques provide a useful data set on the mesoscale. The inverse modelling approach presented combined with a meso-scale data set on evaporation and surface soil moisture, considerable potentialities arise to determine effective meso-scale hydraulic properties.

Distributed agro-hydrological modeling of an irrigation system in Western Turkey

Abstract A clear understanding of all the components of the water balance is essential to analyze possible measures of water savings in irrigated agriculture. However, most components of the water balance are not easily measurable either in terms of the required time interval or the complexity of the processes. For an irrigated area in the Western part of Turkey, the physically based one-dimensional agro-hydrological model SWAP for water transport and crop growth was applied in a distributed manner to reveal all the terms of the water balance. A combination of point data and distributed areal data was used as input for the model. The emphasis was put on the conversion from available data to required data. Simulations were carried out for the period 1985–1996 and detailed analyses were performed for two successive years, a pre-drought year, 1988, and a dry year, 1989. Irrigation deliveries were reduced substantially in 1989, resulting in a modified water balance. Lateral fluxes to drains were reduced, bottom fluxes were changed from groundwater recharge to capillary rise, evapotranspiration was lower, and, most importantly, relative yield was reduced. All these changes were obtained for specific sites by combining existing soil data with cropping patterns. It was concluded that the use of the SWAP model in a distributed way is a useful tool to analyze all the components of the water balance for a whole irrigation system.

Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin

The availability of accurate rainfall data at proper temporal and spatial scales is vital for knowledge of renewable water resources and safe withdrawals for irrigation. Rain gauge networks in mountainous basins such as the Indus are sparse and insufficient to plan withdrawals and water management applications. Satellite rainfall estimates can be used as an alternative source of information but need area-specific calibration and validation due to the indirect nature of the radiation measurements. In this study, a calibration protocol is worked out for rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite because uncalibrated TRMM rainfall data are inaccurate for use in rainfall–runoff studies and in soil water balance studies. Two alternative techniques, regression analysis (RA) and geographical differential analysis (GDA), were used to calibrate TRMM rainfall data for different periods and spatial distributions. The validity of these techniques was tested using Nash–Sutcliffe efficiency and the standard error of estimate. The GDA technique proved to be better, with higher efficiency and smaller error in complex mountainous terrains. The deviation between TRMM data and rain gauge data was decreased considerably from 10.9% (pre-calibration at 625 km2) to 6.1% (post-calibration at 3125 km2) for annual time periods. For monthly periods, the deviation of 34.9% (pre-calibration at 625 km2) was decreased to 15.4% (post-calibration at 3125 km2). Calibration can be improved further if more rain gauges are available. The GDA technique can be applied to calibrate TRMM rainfall data in regions with limited rain gauge data and can provide a sufficiently accurate estimate of the key hydrological process that can be used in water management applications.

A New Methodology for Assimilation of Initial Soil Moisture Fields in Weather Prediction Models Using Meteosat and NOAA Data

In this study, a simple method is described and tested for deriving initial soil moisture fields for numerical weather prediction purposes using satellite imagery. Recently, an algorithm was developed to determine surface evaporation maps from high- and low-resolution satellite data, which does not require information on land use and synoptic data. A correction to initial soil moisture was calculated from a comparison between the evaporation fields produced by a numerical weather prediction model and the satellite algorithm. As a case study, the method was applied to the Iberian Peninsula during a 7-day period in the summer of 1994. Two series of short-term forecasts, initialized from a similar initial soil moisture field, were run in parallel: a control run in which soil moisture evolved freely and an experimental run in which soil moisture was updated daily using the simple assimilation procedure. The simple assimilation resulted in a decrease of the bias of temperature and specific humidity at 2-m height during the daytime and a small decrease of the root-mean-square error of these quantities. The results show that the surface evaporation maps, derived from the satellite data, contain a signal that may be used to assimilate soil moisture in numerical weather prediction models.

Relating Crop Water Consumption to Irrigation Water Supply by Remote Sensing

A new set of irrigation performance indicators based on remote-sensing estimates of evapotranspiration is introduced. These ‘evapotranspiration indicators’ are the relative evapotranspiration or crop stress and the water efficiency as well as their uniformity. With a remote-sensing evapotranspiration algorithm (SEBAL) maps of actual crop water consumption are derived. These maps are one of the inputs in the evapotranspiration indicators, together with GIS data (digitized irrigation unit boundaries) and field data (irrigation delivery schedule and water flow). This approach is applied on the Rio Tunuyan irrigation scheme, Mendoza, Argentina, which is served by surface water and privately owned ground water pumps. A homogeneous pattern of actual crop water consumption is detected from the highest irrigation level till the lowest (farm) level (coefficient of variance from 8.6% to 6.1% and 14.0% of secondary, tertiary and pixel level, respectively). Considering that a rotational irrigation schedule at tertiary and farm level is present, the results indicate that ground water supply through extraction and capillary rise equalize the spatial patterns in crop water consumption. The latter is proved by a comparison between (i) the areal water consumption from remote-sensing measurements, (ii) the areal water supply and (iii) additional field information on ground water extraction and capillary rise.

Water balance variability across Sri Lanka for assessing agricultural and environmental water use

This paper describes a new procedure for hydrological data collection and assessment of agricultural and environmental water use using public domain satellite data. The variability of the annual water balance for Sri Lanka is estimated using observed rainfall and remotely sensed actual evaporation rates at a 1 km grid resolution. The Surface Energy Balance Algorithm for Land (SEBAL) has been used to assess the actual evaporation and storage changes in the root zone on a 10-day basis. The water balance was closed with a runoff component and a remainder term. Evaporation and runoff estimates were verified against ground measurements using scintillometry and gauge readings respectively. The annual water balance for each of the 103 river basins of Sri Lanka is presented. The remainder term appeared to be less than 10% of the rainfall, which implies that the water balance is sufficiently understood for policy and decision making. Access to water balance data is necessary as input into water accounting procedures, which simply describe the water status in hydrological systems (e.g. nation wide, river basin, irrigation scheme). The results show that the irrigation sector uses not more than 7% of the net water inflow. The total agricultural water use and the environmental systems usage is 15 and 51%, respectively of the net water inflow. The consumptive use of rain-fed and irrigated agriculture are approximately equal. The evaporation rates in agriculture and mixed vegetation are similar, so that low productivity rangelands can be transformed into rain-fed agriculture without detrimental effects on water availability to downstream users. The unused water flow to the Indian Ocean is 34% of the net inflow, hence there is scope for further water developments in Sri Lanka.

An Intercomparison of Techniques to Determine the Area‐Averaged Latent Heat Flux from Individual in Situ Observations: A remote Sensing Approach Using the European Field Experiment in a Desertification‐Threatened Area Data

A knowledge of the area-averaged latent heat flux 〈λE〉 is necessary to validate large-scale model predictions of heat fluxes over heterogeneous land surfaces. This paper describes different procedures to obtain 〈λE〉 as a weighted average of ground-based observations. The weighting coefficients are obtained from remote sensing measurements. The remote sensing data used in this study consist of a Landsat thematic mapper image of the European Field Experiment in a Desertification-Threatened Area (EFEDA) grid box in central Spain, acquired on June 12, 1991. A newly developed remote sensing algorithm, the surface energy balance for land algorithm (SEBAL), solves the energy budget on a pixel-by-pixel basis. From the resulting frequency distribution of the latent heat flux, the area-averaged latent heat flux was calculated as 〈λE〉 = 164 W m−2. This method was validated with field measurements of latent heat flux, sensible heat flux, and soil moisture. In general, the SEBAL-derived output compared well with field measurements. Two other methods for retrieval of weighting coefficients were tested against SEBAL. The second method combines satellite images of surface temperature, surface albedo, and normalized difference vegetation index (NDVI) into an index on a pixel-by-pixel basis. After inclusion of ground-based measurements of the latent heat flux, a linear relationship between the index and the latent heat flux was established. This relationship was used to map the latent heat flux on a pixel-by-pixel basis, resulting in 〈λE〉 = 194 W m−2. The third method makes use of a supervised classification of the thematic mapper image into eight land use classes. An average latent heat flux was assigned to each class by using field measurements of the latent heat flux. According to the percentage of occurrence of each class in the image, 〈λE〉 was calculated as 110 W m−2. A weighting scheme was produced to make an estimation of 〈λE〉 possible from in situ observations. The weighting scheme contained a multiplication factor for each measurement site in order to compensate for the relative contribution of that site to 〈λE〉. It was shown that 〈λE〉 derived as the arithmetic mean of 13 individual in situ observations leads to a difference of 34% (〈λE〉 = 104 W m−2), which emphasizes the need for improved weighting procedures.