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Chapter 11 MUST – a medium scale surface temperature mission dedicated to environment and agriculture Alain Vidal, Philippe Duthil, Catherine Ottlé, Vicente Caselles, Antonio Yagüe and John Murtagh 11.1 Introduction The Medium Scale Surface Temperature (MUST) study was carried out in the framework of the European Commission (DG XII) fourth “Research and Development Work Programme.” The objective of this study was the definition and demonstration of interest of a large swath, medium resolution thermal infrared imager mission, named MUST. More precisely speaking the specific objectives were: • • • • to demonstrate the relevance and efficiency of the products of the MUST mission in the relevant application fields and to assess the economical benefits of the mission; to further develop methodologies for retrieving thermal- and waterrelated surface parameters from the sensor data; to design a medium-resolution, large-swath thermal imager, that is, compact and affordable; to analyze the operational implementation of the ground segment. The study was co-ordinated by Matra Marconi Space (MMS) and their partners Cemagref (France), CNRS/CETP (France), the Universitat de Valencia (Spain), INFOCARTO (Spain), and the NRSCL (UK). It included the whole Mission and System definition process, starting with the definition of the user requirements, including the space and ground segments, the cost estimates, and ending with the evaluation of the MUST mission benefits versus costs and the final recommendations on the potential continuation of the programme. A development and implementation of the MUST sensor was then proposed in the framework of the European Space Agency Coastal Zone Earth Watch mission. “chap11” — 2004/1/20 — page 405 — #1 406 Alain Vidal et al. 11.2 The MUST mission and related applications 11.2.1 Applications The application of thermal infrared measurements from space are based on the relation existing between surface temperature and the soil and vegetation hydric state as introduced later. They can be classified into three main classes: (a) the assessment of the vegetation hydric state, important for applications such as agriculture (crop yield forecasts, potential stress due to drought, illness, or other pests), irrigation management, and forest fires risks assessment; (b) the assessment of surface (soil and vegetation) evapotranspiration, and thereby the evaluation of water consumption, useful for irrigation management and the evaluation of soil moisture that is helpful in hydrology applications; (c) the assessment of surface temperature itself or the air temperature as a by-product of surface temperature. The related applications are mapping frosts on agricultural surfaces or heat islands on urban surfaces. In addition, the MUST thermal infrared data are expected to be useful for the global monitoring of the biosphere and as a contribution to the Global Circulation Models providing data on the water fluxes at the global scale. The different fields of operational applications for the thermal infrared data are listed in Table 11.1. 11.2.2 The MUST information products The MUST information products can be classified into three types, based on equation (11.1): Ts = Ta + (Ts − Ta ) (11.1) where Ts is the surface temperature measured by MUST and Ta the air temperature. This simple equation explains the double dependence of Ts on: (a) the climatic conditions, expressed through Ta ; (b) the energy balance of Table 11.1 Main land applications identified for a thermal imager Domain Parameter of interest Agriculture Hydric state of vegetation for crop yield forecasts and irrigation management Areas of frost risks Irrigation water consumption assessment Areas of fire risks Hydric state of soil Heat islands in urban centres Complement to VEGETATION data: water fluxes, hydric state of vegetation and soils Irrigation Forests Hydrology Environment Scientific biosphere global monitoring, Global Circulation Models “chap11” — 2004/1/20 — page 406 — #2 MUST mission 407 the considered surface, where equilibrium is the difference between surface and air temperatures (Ts − Ta ). Product type 1: vegetation stress index product Measured through Ts − Ta , this product mainly concerns crop yield estimation in agriculture, irrigation monitoring, and risk assessment of forest fires. The evaluation of vegetation stress is derived from the analysis of the surface energy balance terms. The energy balance is usually expressed with the following equation: Rn = G + H + LE (11.2) where Rn is the net radiation flux, G the soil heat flux, H the sensible heat flux, and LE the latent heat flux or evapotranspiration. This partition depends on the availability of water in soil (for soil evaporation) or in canopy (for canopy transpiration). As shown by many authors (Perrier 1975; Jackson et al. 1981), a reduction of soil/plant surface evapotranspiration results in an increase of Ts − Ta , whereas an increase of evapotranspiration results in a decrease of Ts − Ta . Physically, Ts ranges from a maximum value of Ts max when evapotranspiration is null (LE = 0) to a minimum value of Ts min when evapotranspiration reaches its maximal (or potential) value LE = LEp (Moran et al. 1994; Vidal et al. 1997). LEp depends on the atmospheric conditions (air temperature and moisture) and on the plant characteristics (resistance to heat exchange with air and resistance to evapotranspiration). The ratio of actual LE to LEp (LE/LEp ) provides a precise assessment of the vegetation stress, which is minimal when LE/LEp = 1, and maximal when LE/LEp = 0. Several indices have been developed to estimate this ratio, LE/LEp , using remote sensing measurements. The more classical ones are based on the CWSI (Crop Water stress Index) approach where (Jackson et al. 1981): LE Ts − Ts max ≈ 1 − CWSI = LEp Ts min − Ts max (11.3) Product type 2: daily/weekly surface evapotranspiration product Estimated also through Ts − Ta , this product mainly concerns irrigation monitoring and water resources management. A generic expression has been derived by many authors (Jackson et al. 1977; Seguin and Itier 1983; Vidal and Perrier 1988) from the surface energy balance for estimating the daily evapotranspiration from an instantaneous midday remote sensing “chap11” — 2004/1/20 — page 407 — #3 408 Alain Vidal et al. measurement of Ts − Ta : LEd = Rnd + A − B (Ts − Ta ) (11.4) where LEd and Rnd are the daily evapotranspiration and net radiation, A and B are constants depending on the canopy, and Ts − Ta is the instantaneous difference between surface and air temperatures measured near midday. Product type 3: interpolated air temperature,Ta This is derived by correlating surface and air temperature, assuming air temperature to be known at some meteorological station point. Some of the primary applications include frosts prediction and detection of urban heat islands. A strong correlation is found between surface and air temperatures, when low air temperatures occur, which are the usual conditions when frosts maps or urban heat island maps, are required. 11.2.3 Methodology followed for assessing the user requirements and benefits The User Requirements phase has been a major step in the definition of the MUST Mission and System, as no structured user community exists. The scientific community has not necessarily evaluated all the issues related to end-user requirements for information products using land surface temperature. The user requirements and benefit assessments have therefore been established with three National user groups in United Kingdom, Spain, and France (Table 11.2). The user groups were involved in two main steps of the process. First, they expressed their requirements in terms of products and services. Second, after the products had been simulated, they indicated more precisely their interest for the products. This provided an assessment of the benefits derived from MUST products by the user community. 11.2.4 The information products’ requirements and simulations The main applications in agriculture, water resources, and forest fires will be presented henceforth. In all the cases, MUST surface temperatures were simulated from Landsat TM thermal IR data (120-m resolution). Since 250-m resolution was envisaged for MUST, Landsat thermal data were resampled at 250-m resolution using bicubic convolution. The maps presented in this chapter derived from such resampled thermal IR data. “chap11” — 2004/1/20 — page 408 — #4 MUST mission 409 Table 11.2 Composition of user groups in the three partner countries of the MUST project France Agriculture Water resources EC MARS Project,Agricultural College/Research Institute (remote sensing department), Cereals Trader, Sugar Beet Technical Institute Irrigation companies (South West, South East) Forest administration (Haute Corse), Services Provider in Forest Fire mitigation Fruits Production Technical Institute, Forestry Producers Association Water distribution by large companies Spain Agriculture, irrigation Frosts risk and damage Forest fires Heat island Agronomic Research Institute Meteorological Institute Administration of Andalucia Urban environment administration UK Agriculture Agricultural Advisory Service (ADAS), Horticulture research, Farming online,Value added company ADAS, School of Agriculture (Silsoe) UK Met Office, British Sugar Irrigation Forest fires Frosts prevention Irrigation Frosts prevention MUST information products for agriculture INPUTS TO YIELD PREDICTION MODELS Users described that yield prediction models do not sufficiently take into account the actual vegetation stress. In this field, remote sensing is already used (e.g. by the EU MARS project), but it primarily involves the estimation of biomass using reflected solar wavelengths. Following the present tendencies in the use of EO data for yield prediction, it was suggested to use MUST data as a direct input in “efficiency” models, for example, the Monteith model (Monteith 1972), or the 3M “Modified Monteith Model” recently developed by the MARS project with Cemagref (Laguette et al. 1995, 1997). In these models, the dry matter (DM) is estimated as a cumulative product of efficiencies and global radiation (Rg ), then transformed into crop yield using harvest indexes (HI). In this case, a MUST-derived water requirement satisfaction index SI can be used in the expression of the conversion efficiency, which is usually considered as a constant:
Yield = HI · DM = HI · εs (εi0 NDVIn ) (εc0 (t) SI) Rg dt (11.5) where NDVIn is the NDVI (normalized difference vegetation index) normalized between its maximal and minimal values during the crop season, NDVIn = (NDVI − NDVImin )/(NDVImax − NDVImin ), SI is a linear function “chap11” — 2004/1/20 — page 409 — #5 410 Alain Vidal et al. of CWSI, εs is the climatic efficiency, εi0 is the interception efficiency for maximal NDVI, and εc0 is the conversion efficiency for maximal SI. The product of Rg with efficiencies is integrated from the beginning of the cropping season to the date of the cycle where yield is estimated/predicted. The aforementioned authors have shown that, when the “3M model” is used with a continuous series of NOAA-AVHRR images, the final yield of wheat can be retrieved with a precision of 1.2 tons ha−1 instead of 2.4 tons ha−1 obtained when not accounting for water stress effects on yield. SIMULATED PRODUCTS The 3M model was applied on maize fields in the Orthez region (South West of France). Yield prediction figures obtained with remote sensing data have been compared to actual yield figures derived from in situ measurements in sample plots. The ideal process would have been to acquire remotely sensed data along the whole crop season with a sampling interval of typically 10 days and integrate them. Unfortunately, this was not possible because Landsat TM images were available in cloud-free conditions on a single date (20 July, 1996). Consequently, it was decided to compare this single date remote sensing result (which is actually the DM accumulation derivative) with the in-situ DM variation measurement averaged on the period around the available date. The results, sketched in Figure 11.1, are not conclusive on the capability of IR-derived water stress information to improve the crop DM and yield prediction. Since this result is not coherent with the aforementioned MARS project research results, it is believed that it is a consequence of the single-date available acquisition. Ground-measured final DM production (g m–2) 900 y = 10.135x + 422.93 r = 0.435** 800 700 600 500 400 300 200 0 2 4 6 8 10 12 14 16 18 20 MUST-derived daily DM production (g m–2) 22 24 26 Figure 11.1 Comparison of the daily dry matter (DM) production estimated from onedate MUST-simulated thermal IR data with the ground measured final DM production on maize (Orthez – France). “chap11” — 2004/1/20 — page 410 — #6 MUST mission 411 MUST information products for irrigation and water resources The users involved in irrigation, from both agricultural and water management points-of-view, identified three information products. In order of priority, these are: the spatial distribution of water consumption (derived from the evapotranspiration LE), maps of irrigated surfaces, and maps of crop water stress for monitoring water application and irrigation scheduling. The users involved in water quality management (the domestic water distribution companies) were interested in soil moisture maps at the scale of small to medium watershed area. This information provides the means for identifying and assessing the importance of water contributing areas, as input for water quality models. They were also considering the crop water consumption (LE estimation) to derive infiltration/runoff as input for water quality models. SIMULATED PRODUCTS The objective of the simulations was mostly to show the users spatially distributed evapotranspiration information at 250-m resolution to demonstrate its advantage in comparison to sampled information and to 1-km resolution information. The simulated products are therefore daily evapotranspiration maps on the sites of Orthez (France) (Figure 11.2), the LE < 3 mm day–1 3 < LE < 4 4 < LE < 5 5 < LE < 5.5 5.5 < LE < 6 6 < LE < 6.5 6.5 < LE < 7 LE > 7 mm day –1 LEp = 8.1 mm day–1 Maize area Rivers 0 2 km Figure 11.2 Daily evapotranspiration map obtained from MUST-simulated thermal data using equation (11.4) (Orthez – France) (see Colour Plate XXX). “chap11” — 2004/1/20 — page 411 — #7 412 Alain Vidal et al. Orgeval river basin (France, part of the Seine river basin), and of Barrax (Albacete–La Mancha–Spain), using the approach in equation (11.4). Forest fires Fire-fighting authorities have been using short-term fire risk indexes for a long time. These indexes are usually based on actual and predicted meteorological parameters, such as wind speed, air moisture, and temperature. Vegetation stress is usually represented by a simple budget between rainfall and potential evapotranspiration, which is difficult to transpose to forest areas, mainly due to spatial variations in the terms of this budget, and on how this budget is exploited by soil and tree root zones. It has recently been shown that using surface temperature measurements to derive the vegetation stress improved the fire risk prediction on both a short-term (daily forecast) and mid-term (weekly–monthly) range (Vidal et al. 1994; Vidal and DevauxRos 1995). Based on this rationale and on the operational way to fight fires in Corsica, two types of requirements were expressed by the fire fighting users: • • a real-time, daily-risk index integrating climatic and vegetation stress, at the scale of large forested areas (typically larger that 50,000 ha) useful for a better positioning of the fire fighting teams put in alert during summer months; a weekly risk index at a more local scale, usually for areas ranging from 5,000 to 20,000 ha, needed in order to support decisions on concentrating or moving means (staff and material) of fire watch patrols. In addition, the forests officials were interested in two types of products: • • long-term risk maps on usually stressed areas to be used for the establishment of risk prevention plans at a 1/50,000 scale; fire damage maps: the thermal infrared data to be used in combination with visible, near-infrared (NIR), and short wave infrared (SWIR) data are expected to significantly enhance the accuracy of the damage maps established with visible, NIR, SWIR data only. SIMULATED PRODUCTS The different types of products have been simulated for Corsica (Figure 11.4) and Spain (Figure 11.5), assuming that MUST would enable an observation every day or 2–3 days. In the case of Corsica, an extension of CWSI (see equation 11.3) to sparse vegetation, called Water Deficit Index (WDI), has been used. This index, introduced by Moran et al. (1994) and applied to forests by Vidal and Devaux-Ros (1995), is based on the representation “chap11” — 2004/1/20 — page 412 — #8 Fractional vegetation cover MUST mission 413 1: Well-watered vegetation 1 2: Water-stressed vegetation 0.8 A 0.6 C B 0.4 0.2 3: Saturated bare soil 4: Dry bare soil 0 –10 0 10 Ts – Ta (°C) 20 Figure 11.3 The theoretical trapezoidal shape showing the different biomass versus water stress conditions of the canopy–soil continuum (from Moran et al. 1994). The WDI of point C is given by AC/AB as shown in equation (11.6). Daily fire danger map Reinforce protection Haute-Corse, June 1993 250-m resolution WDI map derived from Landsat TM Operational units Median Very low danger Low Median High Very high Vegetation units 0 5 10 15 20 km WDI < 0.2 0.2–0.4 0.4–0.5 0.5–0.6 0.6–0.7 0.7–0.9 > 0.9 Weekly fire danger map Re-location of mitigation means Figure 11.4 Daily and weekly fire risk index on the right part are the results of sub-sampling a full scale risk index obtained from MUST-simulated thermal data (on the left), useful for the establishment of 1/50,000 long-term risk maps (see Colour Plate XXXI). of the soil-canopy continuum conditions in a fractional vegetation cover versus the difference between surface and air temperature (Ts −Ta ) diagram. Actually, its position is theoretically comprised within a trapezoidal pattern: Figure 11.3 presents such a pattern and the definition of its limits. “chap11” — 2004/1/20 — page 413 — #9 414 Alain Vidal et al. Burnt area TM345 color composite TM645 color composite Figure 11.5 Classification of fire damaged areas using different bands of a LandsatTM image. Respectively, red, NIR, SWIR (on the left), and thermal infrared, NIR and SWIR (on the right). The latter provides a higher accuracy (see Colour Plate XXXII). These authors have proposed both a theoretical and a graphic simple estimation of the soil–canopy evaporation for a given fractional vegetation cover, knowing its potential evaporation LEp : (Ts − Ta ) − (Ts − Ta )dry BC LE = = = 1 − WDI LEp (Ts − Ta )wet − (Ts − Ta )dry AB (11.6) where Ts is the composite surface temperature of the soil–canopy continuum as estimated from thermal infrared measurements, BC and AB are the distances represented in Figure 11.3, and the wet and dry indices correspond to the left and right limits of the trapezoid. The main interest of this approach is the possibility of estimating both Ts − Ta and fractional vegetation cover from remote sensing measurements. In the WDI approach, both NDVI and Soil Adjusted Vegetation Index (SAVI) have been used to estimate fractional vegetation cover: NDVI = ρNIR − ρR ρNIR + ρR SAVI = (ρNIR − ρR )/(ρNIR + ρR + L)(1 + L) (11.7) (11.8) where ρNIR and ρR are the reflectances in the sensor’s near-infrared and red wavebands, and L is a unitless constant assumed to be 0.5 for a wide variety of leaf area index values (Huete 1988). 11.2.5 System requirements derived from user requirements From the above step of identification of MUST applications and information products, a synthetic table (Table 11.3) was prepared and validated by users during each user meeting and after national interviews. “chap11” — 2004/1/20 — page 414 — #10