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Using VEGETATION satellite data and the crop model STICS-Prairie to estimate pasture production at the national level in France
Physics and Chemistry of the Earth 30 (2005) 3–9 www.elsevier.com/locate/pce
Using VEGETATION satellite data and the crop model STICS-Prairie to estimate pasture production at the national level in France C. Di Bella b
a,c,* ,
R. Faivre b, F. Ruget c, B. Seguin
c
a Instituto de Clima y Agua – CIRN INTA, Los Reseros y Las Caban˜as S/N, Castelar, Buenos Aires 1712, Argentina Unite´ de Biome´trie et Intelligence Artificielle (UBIA), INRA – Toulouse, BP 27, 31326 Castanet-Tolosan Cedex, France c Unite´ Climat, Sol et Environnement (CSE), INRA -Avignon, Site Agroparc, 84914 Avignon Cedex 9, France
Accepted 24 August 2004
Abstract In France, pastures constitute an important land cover type, sustaining principally husbandry production. The absence of lowcost methods applicable to large regions has conducted to the use of simulation models, as in the ISOP system. Remote sensing data may be considered as a potential tool to improve a correct diagnosis in a real time framework. Thirteen forage regions (FR) of France, differing in their soil, climatic and productive characteristics were selected for this purpose. SPOT4-VEGETATION images have been used to provide, using subpixel estimation models, the spectral signature corresponding to pure pasture conditions. This information has been related with some growth variables estimated by STICS-Prairie model (inside ISOP system). Beyond the good general agreement between the two types of data, we found that the best relations were observed between NDVI middle infrared based index (SWVI) and leaf area index. The results confirm the capacities of the satellite data to provide complementary productive variables and help to identify the spatial and temporal differences between satellite and model information, mainly during the harvesting periods. This could contribute to improve the evaluations of the model on a regional scale. 2004 Elsevier Ltd. All rights reserved. Keywords: Pastures; Remote sensing; Vegetation index; Biomass production; STICS; Regional scale
1. Introduction Pastures constitute an important component of terrestrial ecosystems. From a productive point of view, pastures represent an essential component of livestock In economical terms, France has the most important agricultural sector of the European Union (23% of the total European agriculture production). Within this sector, animal production represents 40% of the total agri*
Corresponding author. Present address: Instituto de Clima y Agua – CIRN INTA, Los Reseros y Las Caban˜as S/N, Castelar, Buenos Aires 1712, Argentina. Tel.: +54 1146210125; fax: +54 1146215663. E-mail address: [email protected] (C. Di Bella). 1474-7065/$ - see front matter 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2004.08.018
culture activities on 21% of the French territory. In this context, the monitoring of pasture conditions on a regional scale represents a very important tool at the national scale. Some research groups are developing different methods to establish in a precise, economic and fast way biomass production of pastures. If biomass cuts constitute a simple method to estimate biomass production (e.g., Sims et al., 1978; Sala et al., 1988) this technique is limited by its slowness, cost and especially by the number of necessary measurements to produce a reliable evaluation at the national level. For these reasons, it was necessary to develop indirect techniques to estimate some functional prairies features as biomass, net primary productivity (NPP) or leaf area index (LAI). To be operational,
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C. Di Bella et al. / Physics and Chemistry of the Earth 30 (2005) 3–9
these methods must be precise, rapid, objective and require a minimum of standardization. Growth simulation models allowed during the last years to propose methodological and operational progresses in this domain. Pasture modeling has been the objective of different groups. With the same objectives, the STICS model developed by INRA (Brisson et al., 1998) was adapted to pastures conditions bye adding specific modules or functions to simulate some pasture characteristic (Ruget et al., 1999). To develop the ISOP system (Information and Objective Follow-up of Pastures), the CSE unit (Climate, Soil and Environment – INRA – Avignon) and Meteo-France have worked in close cooperation for the construction and setting of this tool. ISOP system, using a geographical information system (GIS), combined productive, edaphic and climatic information to estimate for every forage region (FR) the pastures production and to calculate an indicator of water stress using 16 years of data comparisons. Remote sensing offers interesting perspectives to enhance the achievements of simulation models, particularly the use of spatial and repetitive information provided by sensors with different spatial resolution. The use of remote sensing for prairies monitoring has been centered mainly on the identification of species and plant communities (Benoit et al., 1988; Hobbs, 1990) and on the survey of their geographical distribution (e.g., Girard and Rippstein, 1994; Lauver, 1997; Azzali and Menenti, 2000). Also, several papers demonstrate the existence of strong relationship between satellite information (principally from NOAA–AVHRR satellite) and biomass or NPP for different regions and ecosystems of the world (Goward et al., 1985; Tucker et al., 1985; Box and Holben, 1989; Burke et al., 1991; Prince, 1991; Hobbs, 1995; Paruelo et al., 2000). Other studies tried to identify drought periods or particularly dry zones (for example Seguin, 1993). The major interest of remote sensing data lies in the possibility to extrapolate acquired data at pixel resolution to get spatially continuous information with a reasonable delay and cost. It also appears as a unique tool for obtaining effective ground truth information, which may complete model outputs that only offer a potential view of biospheric functioning based upon the available existing inputs. It was decided to use the availability of VEGETATION sensor data (launched in April 1998 on the SPOT4 platform) together with existing ISOP system outputs for proposing a survey of pastures in France at the national level. The present work consists therefore in exploring the possibilities to improve pasture productivity evaluation on a regional scale, while associating the measurements provided by remote sensing to simulation results. In a first step, we estimated pure pastures spectral responses from the radiometric information integrated at pixel scale (1 km2). After obtaining the
spectral response of pure prairie for different dates and regions we evaluated the possibilities offered by the satellite information as an indicator of some biophysical variables as LAI or biomass estimated by the ISOP system using the STICS-Prairie simulation model. Finally, we analyzed the possibilities offered by coupling model and sensor data.
2. Materials and methods 2.1. The study areas We adopted the same spatial scale of the ISOP system, applicable to the whole French territory: the FR. To limit the work volume (the whole territory is covered by 200 FRs), we chose a total of thirteen FRs representing most of the pasture production, climate and topography conditions (Fig. 1): 2503, 2505 and 2516 (region of Normandy); 4313 (region of High Jura); 7312 and 7315 (region of Southern France Pyrenees) and 8301, 8302, 8303, 8305, 8309, 8310 and 8311 (region of the Central Plains). Inside each FR, we defined a window of 25 km2 (5 · 5 pixels). 2.2. Satellite data and the subpixel estimation We used SPOT4 VEGETATION sensor data acquired during 1998, 1999 and 2000 received as part of the VEGA 2000 project. All these images were georeferenced and coregistered in LAT/LON projection. The year 1998 data has been obtained as P product (daily basis images without atmospheric corrections). For the following years, the data have been received as S products
Fig. 1. Thirteen forage regions (FR) selected as study areas.
C. Di Bella et al. / Physics and Chemistry of the Earth 30 (2005) 3–9
(integrating available atmospheric corrections as 10days synthetic data) (Rahman and Dedieu, 1994; Berthelot and Dedieu, 1997). All images have been received in HDF format (16 bits) and transformed in radiometric values coded on 8 bits (0–255) (ENVI 3.1). This treatment allowed us to reduce the file size and so the computing time. However, this transformation altered original reflectance values while keeping the whole spatial and temporal dynamics. In a SPOT4 VEGETATION image, every pixel integrates the spectral signatures of each cover type included in a pixel (1 km2). For the particular case of VEGETATION sensor, this integration corresponds to a mean reflectance value in the blue (B0), the red (B2), the near infrared (B3 or PIR) and the middle infrared (B4 or MIR) bands of the electromagnetic spectrum. This integration results in a mixed value in regions occupied by a great number of land uses. To recover the spectral response of a specific land cover type, it is possible to apply the subpixel techniques. These techniques were described by several authors. In France, Fischer (1994) studied the temporal evolution of NDVI for pure cultures from mixed pixels while using for every land use an empiric model assuming a double logistical function for the NDVI evolution. From the mixed spectral values and land use, it is possible to recover every double logistics parameters. Later, Kerdiles and Grondona (1995) and Faivre and Fischer (1997) supposed that the spectral variability inside the mixed pixels was only due to land use. Using a linear model, every pure response was derived from the mixed reflectance considering that the reflectance of a mixed pixel is the linear combination of every component reflectance inside the pixel and multiplied by their respective proportions inside the pixel. In order to adopt this technique, we firstly calculated the proportion of every land cover type for every FR and pixel (cell) of one square kilometer using the Corine Land Cover database. To achieve the pixel decomposition data from VEGETATION sensor data (1 km · 1 km), we adapted the model developed by Faivre and Fischer (1997) with a specific programming format ( Matlab). This model requires two sort of inputs providing the necessary information to get pure spectral responses of every land cover type inside a pixel of 1 km2: (a) the proportion of each land cover type per pixel; (b) the reflectance values of the mixed pixels in the different bands corresponding to the window area. From these data, it is possible to generate, for every date and spectral band, the mean reflectance value of the different land uses inside the pixel and the variance of the reflectances of each land use over the considered area. As a result of subpixel techniques, we obtained ‘‘pure pasture’’ reflectance values for all spectral bands. It is well known that their combination in vegetation indices explains some structural and functional features of vegetation better as green biomass and LAI (for example
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Hatfield et al., 1985; Wiegand et al., 1985; Sellers, 1985; Baret and Guyot, 1990) as well as NPP (Law and Warning, 1994; Paruelo et al., 2000). For these reasons, we tested the performances of several among them. In the first place, we tested the most popular in the majority of the applications as the NDVI and others as SAVI (Soil Adjusted Vegetation Index, Huete, 1988) or PVI (Perpendicular Vegetation Index, Richardson and Wiegand, 1977) that take in account the advantages of the correction of the soil effects, and the SWVI (Middle Infrared based NDVI, Cayrol et al., 2000) that reduces the effects of the atmospheric absorption. 2.3. STICS-Prairie model and the ISOP system The ISOP system provides pasture production estimates at the FR scale using a simulation model (STICS-Prairie). This model simulates daily values of Leaf Area Index (LAI, in m2 leafs/m2 soil), Standing Dry Biomass (MSECj, in kg of Dry matter present every day) and Dry Matter Production (DMSEC – calculated as the difference between two successive MSEC values (MSECt MSECt 1) as growing variables of the system. Some stress indicators are also available as model outputs like the Available Soil Water Storage (RU, in mm), Water Stress Index (TURFAC – calculated as the ratio of actual evapotranspiration (ETR) to maximum evapotranspiration (ETM)) and Nitrogen Stress Index (INNS – calculated as the ratio of the actual to a reference nitrogen content of the aerial biomass (Lemaire and Salette, 1984)). The simulated value corresponding to each FR resulted from the weighting of all possible combinations of pasture management variables as number of cuts, levels of fertilization and the relative importance of each soil type into the FR.
3. Results Using the subpixel model (Faivre and Fischer, 1997), we obtained for every FR and date the spectral response corresponding to pure pastures conditions inside the pixel. Comparing differences in the different FR, it is possible to evaluate the influence of prairies proportion inside the pixel and the type of accompanying crop inside the pixel. In general, all RFs showed a good correspondence in the temporal evolution of the radiometric values of PIR between mean pixel values and those corresponding to the pure prairie estimations. However, depending on pasture proportion or the accompanying crop into the pixel, the subpixel estimations resulted in different seasonal spectral values. Once we obtained the pure pasture condition, and considering the set of productive variables obtained from STICS-Prairie simulation model inside ISOPSystem, we found that LAI and aerial biomass (MSEC)
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C. Di Bella et al. / Physics and Chemistry of the Earth 30 (2005) 3–9
Table 1 Correlation coefficients between satellite and productive variables during 1999 and 2000 campaigns SAT./PROD.
DMLAI
DMMSEC
DMRU
DMTURFAC
DMINNS
DMDM
BO B2 B3 BMIR NDVI SWVI B3DB4 B2MB3 RVI TVI SAVI PVI ARVI
.1772 .1581 .4190 .0417 .0608 .5540 .4289 .4349 .0359 .0671 .3649 .4283 .0253
.0114 .0015 .2299 .1146 .1626 .4041 .3409 .2804 .0234 .1605 .2633 .2862 .1160
.0568 .1093 .0673 .0539 .1334 .0544 .0107 .0288 .0657 .1276 .0272 .0184 .1231
.0793 .1163 .0667 .1041 .1130 .0060 .0540 .0245 .0212 .1062 .0067 .0134 .1047
.0242 .0086 .1261 .0293 .0435 .1208 .1569 .1499 .0389 .0403 .1211 .1523 .0581
.0548 .0832 .0622 .0699 .1923 .1498 .0977 .1168 .0830 .1859 .1494 .1276 .1403
LAI ¼ 3:256 þ 6:141 SWVI; which allows to derive LAI values from VEGETATION data.
The comparison with LAI values produced by the ISOP model displayed an underestimation of the lower values and an overestimation of the higher ones (Fig. 2). In order to improve this first attempt, an exponential relation between vegetation indices and LAI was considered (e.g., Hatfield et al., 1985; Sellers, 1987; Running and Nemani, 1988). In a first approach, we defined a basic relation as SWVI ¼ SWVImax þ ðSWVImin SWVIx ÞekLAI with considering, in a first time, a k value of 0.550, SWVImin of 0.357 and SWVImax of 0.828. After using an iterative procedure to reduce the mean quadratic error for a total of 7005 cases during the period March–October 1999 and 2000, and keeping a maximal value of the LAIisop of 8 and SWVI between 0.828 and 0.357, we calculated the k value that minimizes the sum of quadratic errors between LAIVGT and LAIisop. The k value determined by this procedure of optimization (k = 0.215) has been kept therefore for the SWVI relation = f(LAI). The application of this relation allowed to improve the linear relationship, by increasing the internal data dynamics and decreasing the minimal 12 10
LAI (VGT)
were the best correlated variables to the most satellite variables (Table 1). These results, in agreement with Sellers et al. (1992) showed the potentiality of vegetation indices to describe vegetation functioning and mainly to join them to fluxes as photosynthesis or NPP. The SWVI vegetation index and the difference between NIR and MIR bands (B3MB4) were the satellite variables best correlated to productive estimations. The relation with the LAI presented a correlation coefficient of 0.55 for the SWVI and 0.42 for B3MB4. The relation with MSEC presented a coefficient of 0.40 for SWVI and 0.41 for B3MB4 for a total of 594 cases. It is necessary to note that these two indices are very close since the difference between B3 and B4 (B3MB4) is the numerator of the SWVI index. Although the relationship with the other productive variables is raised, these results showed the interest of MIR spectral band. This band not only corresponds to a sensible portion to leaves water content but also to dry matter content and leaf internal structure (Ceccato et al., 2002), resulting in SWVI less sensitive than NDVI to the atmospheric effects. Another explanation could be also the one described by Guerif et al. (1995): in that work, based-upon the PVI (Perpendicular Vegetation Index), the PVImir calculated from the MIR band resulted in a better estimation of the LAI in comparison with the PVI calculated from the visible band (PVIvis). The authors found that the PVIvis is considerably influenced by differences observed in the chlorophyll concentration in the leaves. After having established that the SWVI vegetation index is the best descriptor of the biophysical variables characterizing prairie production (LAI or MSEC), a general linear regression between SWVI and DMLAI allowed to establish the following linear function (SD = 1.85 and p < 0.005):
8 6 4 2 0
0
2
4
6
8
10
12
LAI (ISOP) Fig. 2. Linear relationship between leaf area index (LAI) estimated from remote sensing data (LAIVGT) and LAI simulated by STICSPrairie model.
C. Di Bella et al. / Physics and Chemistry of the Earth 30 (2005) 3–9
value of the LAI during the winter period for the satellite values. Looking in detail, it is also possible to observe, in the first place, that LAIVGT was different in the two growing seasons, which is not observed for the LAIISOP (Fig. 2). It is probably not a difference between
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the actual productions, but could result from the fact that the methods used to correct atmospheric and geometric effects have differed between these years. Both through studied years and different RF, the agreement between LAI estimations (Fig. 3) is highly
Fig. 3. Temporal evolution of LAI estimated from satellite data (LAIVGT – red, thick line) and LAI simulated by STICS-Prairie model (LAIISOP – green, dotted line), for 1999 and 2000.
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C. Di Bella et al. / Physics and Chemistry of the Earth 30 (2005) 3–9
variable. At beginning of the growth season, the LAI is underestimated by ISOP in Normandie (RF 2503) and Pie´mont Pyre´ne´en (RF 7315), overestimated in one small area of Massif Central (RF 8310). In the middle of spring (March–May), before the first cuts, the agreement is good. From june to september, cuts do not occur exactly at the same date with both methods. Finally, at the end of year, especially in 1999, ISOP values are higher than those derived from satellite data. Each difference can be explained either by the known defaults of the model or its input data. At the beginning of the growing season, the system ISOP assigned the same LAI values for all the areas, whatever the local climate. It was the simplest solution to fix an initial value, even if we knew that it is not the actual value. In this case, VEGETATION images can supply interesting information to solve this problem. They can also be useful to specify the dates of cuts, especially in areas smaller than the RF. In the system ISOP, these dates are only estimated from the kind of grass managements (hay, silage, etc.), and the heat sum between to successive cuts, without taking into account rain episods delaying the harvest or practice changes.
4. Conclusions For the objective of elaborating an evaluation tool of pasture production adapted to a regional scale, it has been confirmed that the use of low spatial resolution sensors, as VEGETATION, is promising. They deliver significant aggregated information with a high temporal resolution. The model developed by Faivre and Fischer (1997), and adapted for the objectives of this work, proved to be a very useful and accurate tool to obtain spectral responses, while taking in account the big spatial variability of information captured in the different wavelengths. Disaggregated information in the near infrared and middle infrared bands corresponding to pure pasture conditions showed a very good correspondence with the temporary evolution of some productive variables as the LAI and dry matter. This correspondence has been improved appreciably from the calculation of the vegetation indices, through bands combination and normalization of the strips that compose it (case of SWVI). These interrelationships have been improved significantly by applying an exponential function and selecting periods where temporary discordances do not exist with the model. Satellite information, through it relationship to biophysical variables as the LAI, give an unique measure of the true dynamics of plant cover. The evaluations from the satellite data are in agreement with the evaluations by the model; however some differences put in evi-
dence some limits of model pasture monitoring in a real time framework.
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Symposium Modeling of Cropping Systems, 21 June, Lleida, Spain, pp. 111–112. Running, S.W., Nemani, R.R., 1988. Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates. Remote Sensing of the Environment 24, 347–367. Sala, O.E., Parton, W.J., Joyce, L.A., Lauenroth, W.K., 1988. Primary production of the central grassland region of the United States. Ecology 69, 40–45. Seguin, B., 1993. NOAA/AVHRR data for crop monitoring at a regional level: possibilities and limits in the European Context. EARSEL Advances in Remote Sensing 2, 87–93. Sellers, P.J., 1985. Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing 6, 1335–1372. Sellers, P.J., 1987. Canopy reflectance, photosynthesis and transpiration. II The role of biophysics in the linearity of their interdependence. Remote Sensing of Environment 21, 143–183. Sellers, P.J., Berry, J.A, Collatz, C.B., Field, C.B, Hall, F.G., 1992. Canopy reflectance, photosynthesis and transpiration. III A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sensing of Environment 42, 187–216. Sims, P., Singh, J.S., Lauenroth, W.K., 1978. The structure and function of ten western North American grassland. Journal of Ecology 66, 251–285. Tucker, C.J., Van Praet, C.L., Sharman, M.J., Van Ittersum, G., 1985. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel. Remote Sensing of the Environment 17, 233– 249. Wiegand, C.L., Richardson, A.J., Nixon, P.R., 1985. Spectral components analysis, a bridge between spectral and agrometeorological crop models. In: AMS 17th Conference on Agriculture and Forest Meteorology, pp. 203–205.
Using VEGETATION satellite data and the crop model STICS-Prairie to estimate pasture production at the national level in France C. Di Bella b
a,c,* ,
R. Faivre b, F. Ruget c, B. Seguin
c
a Instituto de Clima y Agua – CIRN INTA, Los Reseros y Las Caban˜as S/N, Castelar, Buenos Aires 1712, Argentina Unite´ de Biome´trie et Intelligence Artificielle (UBIA), INRA – Toulouse, BP 27, 31326 Castanet-Tolosan Cedex, France c Unite´ Climat, Sol et Environnement (CSE), INRA -Avignon, Site Agroparc, 84914 Avignon Cedex 9, France
Accepted 24 August 2004
Abstract In France, pastures constitute an important land cover type, sustaining principally husbandry production. The absence of lowcost methods applicable to large regions has conducted to the use of simulation models, as in the ISOP system. Remote sensing data may be considered as a potential tool to improve a correct diagnosis in a real time framework. Thirteen forage regions (FR) of France, differing in their soil, climatic and productive characteristics were selected for this purpose. SPOT4-VEGETATION images have been used to provide, using subpixel estimation models, the spectral signature corresponding to pure pasture conditions. This information has been related with some growth variables estimated by STICS-Prairie model (inside ISOP system). Beyond the good general agreement between the two types of data, we found that the best relations were observed between NDVI middle infrared based index (SWVI) and leaf area index. The results confirm the capacities of the satellite data to provide complementary productive variables and help to identify the spatial and temporal differences between satellite and model information, mainly during the harvesting periods. This could contribute to improve the evaluations of the model on a regional scale. 2004 Elsevier Ltd. All rights reserved. Keywords: Pastures; Remote sensing; Vegetation index; Biomass production; STICS; Regional scale
1. Introduction Pastures constitute an important component of terrestrial ecosystems. From a productive point of view, pastures represent an essential component of livestock In economical terms, France has the most important agricultural sector of the European Union (23% of the total European agriculture production). Within this sector, animal production represents 40% of the total agri*
Corresponding author. Present address: Instituto de Clima y Agua – CIRN INTA, Los Reseros y Las Caban˜as S/N, Castelar, Buenos Aires 1712, Argentina. Tel.: +54 1146210125; fax: +54 1146215663. E-mail address: [email protected] (C. Di Bella). 1474-7065/$ - see front matter 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2004.08.018
culture activities on 21% of the French territory. In this context, the monitoring of pasture conditions on a regional scale represents a very important tool at the national scale. Some research groups are developing different methods to establish in a precise, economic and fast way biomass production of pastures. If biomass cuts constitute a simple method to estimate biomass production (e.g., Sims et al., 1978; Sala et al., 1988) this technique is limited by its slowness, cost and especially by the number of necessary measurements to produce a reliable evaluation at the national level. For these reasons, it was necessary to develop indirect techniques to estimate some functional prairies features as biomass, net primary productivity (NPP) or leaf area index (LAI). To be operational,
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C. Di Bella et al. / Physics and Chemistry of the Earth 30 (2005) 3–9
these methods must be precise, rapid, objective and require a minimum of standardization. Growth simulation models allowed during the last years to propose methodological and operational progresses in this domain. Pasture modeling has been the objective of different groups. With the same objectives, the STICS model developed by INRA (Brisson et al., 1998) was adapted to pastures conditions bye adding specific modules or functions to simulate some pasture characteristic (Ruget et al., 1999). To develop the ISOP system (Information and Objective Follow-up of Pastures), the CSE unit (Climate, Soil and Environment – INRA – Avignon) and Meteo-France have worked in close cooperation for the construction and setting of this tool. ISOP system, using a geographical information system (GIS), combined productive, edaphic and climatic information to estimate for every forage region (FR) the pastures production and to calculate an indicator of water stress using 16 years of data comparisons. Remote sensing offers interesting perspectives to enhance the achievements of simulation models, particularly the use of spatial and repetitive information provided by sensors with different spatial resolution. The use of remote sensing for prairies monitoring has been centered mainly on the identification of species and plant communities (Benoit et al., 1988; Hobbs, 1990) and on the survey of their geographical distribution (e.g., Girard and Rippstein, 1994; Lauver, 1997; Azzali and Menenti, 2000). Also, several papers demonstrate the existence of strong relationship between satellite information (principally from NOAA–AVHRR satellite) and biomass or NPP for different regions and ecosystems of the world (Goward et al., 1985; Tucker et al., 1985; Box and Holben, 1989; Burke et al., 1991; Prince, 1991; Hobbs, 1995; Paruelo et al., 2000). Other studies tried to identify drought periods or particularly dry zones (for example Seguin, 1993). The major interest of remote sensing data lies in the possibility to extrapolate acquired data at pixel resolution to get spatially continuous information with a reasonable delay and cost. It also appears as a unique tool for obtaining effective ground truth information, which may complete model outputs that only offer a potential view of biospheric functioning based upon the available existing inputs. It was decided to use the availability of VEGETATION sensor data (launched in April 1998 on the SPOT4 platform) together with existing ISOP system outputs for proposing a survey of pastures in France at the national level. The present work consists therefore in exploring the possibilities to improve pasture productivity evaluation on a regional scale, while associating the measurements provided by remote sensing to simulation results. In a first step, we estimated pure pastures spectral responses from the radiometric information integrated at pixel scale (1 km2). After obtaining the
spectral response of pure prairie for different dates and regions we evaluated the possibilities offered by the satellite information as an indicator of some biophysical variables as LAI or biomass estimated by the ISOP system using the STICS-Prairie simulation model. Finally, we analyzed the possibilities offered by coupling model and sensor data.
2. Materials and methods 2.1. The study areas We adopted the same spatial scale of the ISOP system, applicable to the whole French territory: the FR. To limit the work volume (the whole territory is covered by 200 FRs), we chose a total of thirteen FRs representing most of the pasture production, climate and topography conditions (Fig. 1): 2503, 2505 and 2516 (region of Normandy); 4313 (region of High Jura); 7312 and 7315 (region of Southern France Pyrenees) and 8301, 8302, 8303, 8305, 8309, 8310 and 8311 (region of the Central Plains). Inside each FR, we defined a window of 25 km2 (5 · 5 pixels). 2.2. Satellite data and the subpixel estimation We used SPOT4 VEGETATION sensor data acquired during 1998, 1999 and 2000 received as part of the VEGA 2000 project. All these images were georeferenced and coregistered in LAT/LON projection. The year 1998 data has been obtained as P product (daily basis images without atmospheric corrections). For the following years, the data have been received as S products
Fig. 1. Thirteen forage regions (FR) selected as study areas.
C. Di Bella et al. / Physics and Chemistry of the Earth 30 (2005) 3–9
(integrating available atmospheric corrections as 10days synthetic data) (Rahman and Dedieu, 1994; Berthelot and Dedieu, 1997). All images have been received in HDF format (16 bits) and transformed in radiometric values coded on 8 bits (0–255) (ENVI 3.1). This treatment allowed us to reduce the file size and so the computing time. However, this transformation altered original reflectance values while keeping the whole spatial and temporal dynamics. In a SPOT4 VEGETATION image, every pixel integrates the spectral signatures of each cover type included in a pixel (1 km2). For the particular case of VEGETATION sensor, this integration corresponds to a mean reflectance value in the blue (B0), the red (B2), the near infrared (B3 or PIR) and the middle infrared (B4 or MIR) bands of the electromagnetic spectrum. This integration results in a mixed value in regions occupied by a great number of land uses. To recover the spectral response of a specific land cover type, it is possible to apply the subpixel techniques. These techniques were described by several authors. In France, Fischer (1994) studied the temporal evolution of NDVI for pure cultures from mixed pixels while using for every land use an empiric model assuming a double logistical function for the NDVI evolution. From the mixed spectral values and land use, it is possible to recover every double logistics parameters. Later, Kerdiles and Grondona (1995) and Faivre and Fischer (1997) supposed that the spectral variability inside the mixed pixels was only due to land use. Using a linear model, every pure response was derived from the mixed reflectance considering that the reflectance of a mixed pixel is the linear combination of every component reflectance inside the pixel and multiplied by their respective proportions inside the pixel. In order to adopt this technique, we firstly calculated the proportion of every land cover type for every FR and pixel (cell) of one square kilometer using the Corine Land Cover database. To achieve the pixel decomposition data from VEGETATION sensor data (1 km · 1 km), we adapted the model developed by Faivre and Fischer (1997) with a specific programming format ( Matlab). This model requires two sort of inputs providing the necessary information to get pure spectral responses of every land cover type inside a pixel of 1 km2: (a) the proportion of each land cover type per pixel; (b) the reflectance values of the mixed pixels in the different bands corresponding to the window area. From these data, it is possible to generate, for every date and spectral band, the mean reflectance value of the different land uses inside the pixel and the variance of the reflectances of each land use over the considered area. As a result of subpixel techniques, we obtained ‘‘pure pasture’’ reflectance values for all spectral bands. It is well known that their combination in vegetation indices explains some structural and functional features of vegetation better as green biomass and LAI (for example
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Hatfield et al., 1985; Wiegand et al., 1985; Sellers, 1985; Baret and Guyot, 1990) as well as NPP (Law and Warning, 1994; Paruelo et al., 2000). For these reasons, we tested the performances of several among them. In the first place, we tested the most popular in the majority of the applications as the NDVI and others as SAVI (Soil Adjusted Vegetation Index, Huete, 1988) or PVI (Perpendicular Vegetation Index, Richardson and Wiegand, 1977) that take in account the advantages of the correction of the soil effects, and the SWVI (Middle Infrared based NDVI, Cayrol et al., 2000) that reduces the effects of the atmospheric absorption. 2.3. STICS-Prairie model and the ISOP system The ISOP system provides pasture production estimates at the FR scale using a simulation model (STICS-Prairie). This model simulates daily values of Leaf Area Index (LAI, in m2 leafs/m2 soil), Standing Dry Biomass (MSECj, in kg of Dry matter present every day) and Dry Matter Production (DMSEC – calculated as the difference between two successive MSEC values (MSECt MSECt 1) as growing variables of the system. Some stress indicators are also available as model outputs like the Available Soil Water Storage (RU, in mm), Water Stress Index (TURFAC – calculated as the ratio of actual evapotranspiration (ETR) to maximum evapotranspiration (ETM)) and Nitrogen Stress Index (INNS – calculated as the ratio of the actual to a reference nitrogen content of the aerial biomass (Lemaire and Salette, 1984)). The simulated value corresponding to each FR resulted from the weighting of all possible combinations of pasture management variables as number of cuts, levels of fertilization and the relative importance of each soil type into the FR.
3. Results Using the subpixel model (Faivre and Fischer, 1997), we obtained for every FR and date the spectral response corresponding to pure pastures conditions inside the pixel. Comparing differences in the different FR, it is possible to evaluate the influence of prairies proportion inside the pixel and the type of accompanying crop inside the pixel. In general, all RFs showed a good correspondence in the temporal evolution of the radiometric values of PIR between mean pixel values and those corresponding to the pure prairie estimations. However, depending on pasture proportion or the accompanying crop into the pixel, the subpixel estimations resulted in different seasonal spectral values. Once we obtained the pure pasture condition, and considering the set of productive variables obtained from STICS-Prairie simulation model inside ISOPSystem, we found that LAI and aerial biomass (MSEC)
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Table 1 Correlation coefficients between satellite and productive variables during 1999 and 2000 campaigns SAT./PROD.
DMLAI
DMMSEC
DMRU
DMTURFAC
DMINNS
DMDM
BO B2 B3 BMIR NDVI SWVI B3DB4 B2MB3 RVI TVI SAVI PVI ARVI
.1772 .1581 .4190 .0417 .0608 .5540 .4289 .4349 .0359 .0671 .3649 .4283 .0253
.0114 .0015 .2299 .1146 .1626 .4041 .3409 .2804 .0234 .1605 .2633 .2862 .1160
.0568 .1093 .0673 .0539 .1334 .0544 .0107 .0288 .0657 .1276 .0272 .0184 .1231
.0793 .1163 .0667 .1041 .1130 .0060 .0540 .0245 .0212 .1062 .0067 .0134 .1047
.0242 .0086 .1261 .0293 .0435 .1208 .1569 .1499 .0389 .0403 .1211 .1523 .0581
.0548 .0832 .0622 .0699 .1923 .1498 .0977 .1168 .0830 .1859 .1494 .1276 .1403
LAI ¼ 3:256 þ 6:141 SWVI; which allows to derive LAI values from VEGETATION data.
The comparison with LAI values produced by the ISOP model displayed an underestimation of the lower values and an overestimation of the higher ones (Fig. 2). In order to improve this first attempt, an exponential relation between vegetation indices and LAI was considered (e.g., Hatfield et al., 1985; Sellers, 1987; Running and Nemani, 1988). In a first approach, we defined a basic relation as SWVI ¼ SWVImax þ ðSWVImin SWVIx ÞekLAI with considering, in a first time, a k value of 0.550, SWVImin of 0.357 and SWVImax of 0.828. After using an iterative procedure to reduce the mean quadratic error for a total of 7005 cases during the period March–October 1999 and 2000, and keeping a maximal value of the LAIisop of 8 and SWVI between 0.828 and 0.357, we calculated the k value that minimizes the sum of quadratic errors between LAIVGT and LAIisop. The k value determined by this procedure of optimization (k = 0.215) has been kept therefore for the SWVI relation = f(LAI). The application of this relation allowed to improve the linear relationship, by increasing the internal data dynamics and decreasing the minimal 12 10
LAI (VGT)
were the best correlated variables to the most satellite variables (Table 1). These results, in agreement with Sellers et al. (1992) showed the potentiality of vegetation indices to describe vegetation functioning and mainly to join them to fluxes as photosynthesis or NPP. The SWVI vegetation index and the difference between NIR and MIR bands (B3MB4) were the satellite variables best correlated to productive estimations. The relation with the LAI presented a correlation coefficient of 0.55 for the SWVI and 0.42 for B3MB4. The relation with MSEC presented a coefficient of 0.40 for SWVI and 0.41 for B3MB4 for a total of 594 cases. It is necessary to note that these two indices are very close since the difference between B3 and B4 (B3MB4) is the numerator of the SWVI index. Although the relationship with the other productive variables is raised, these results showed the interest of MIR spectral band. This band not only corresponds to a sensible portion to leaves water content but also to dry matter content and leaf internal structure (Ceccato et al., 2002), resulting in SWVI less sensitive than NDVI to the atmospheric effects. Another explanation could be also the one described by Guerif et al. (1995): in that work, based-upon the PVI (Perpendicular Vegetation Index), the PVImir calculated from the MIR band resulted in a better estimation of the LAI in comparison with the PVI calculated from the visible band (PVIvis). The authors found that the PVIvis is considerably influenced by differences observed in the chlorophyll concentration in the leaves. After having established that the SWVI vegetation index is the best descriptor of the biophysical variables characterizing prairie production (LAI or MSEC), a general linear regression between SWVI and DMLAI allowed to establish the following linear function (SD = 1.85 and p < 0.005):
8 6 4 2 0
0
2
4
6
8
10
12
LAI (ISOP) Fig. 2. Linear relationship between leaf area index (LAI) estimated from remote sensing data (LAIVGT) and LAI simulated by STICSPrairie model.
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value of the LAI during the winter period for the satellite values. Looking in detail, it is also possible to observe, in the first place, that LAIVGT was different in the two growing seasons, which is not observed for the LAIISOP (Fig. 2). It is probably not a difference between
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the actual productions, but could result from the fact that the methods used to correct atmospheric and geometric effects have differed between these years. Both through studied years and different RF, the agreement between LAI estimations (Fig. 3) is highly
Fig. 3. Temporal evolution of LAI estimated from satellite data (LAIVGT – red, thick line) and LAI simulated by STICS-Prairie model (LAIISOP – green, dotted line), for 1999 and 2000.
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C. Di Bella et al. / Physics and Chemistry of the Earth 30 (2005) 3–9
variable. At beginning of the growth season, the LAI is underestimated by ISOP in Normandie (RF 2503) and Pie´mont Pyre´ne´en (RF 7315), overestimated in one small area of Massif Central (RF 8310). In the middle of spring (March–May), before the first cuts, the agreement is good. From june to september, cuts do not occur exactly at the same date with both methods. Finally, at the end of year, especially in 1999, ISOP values are higher than those derived from satellite data. Each difference can be explained either by the known defaults of the model or its input data. At the beginning of the growing season, the system ISOP assigned the same LAI values for all the areas, whatever the local climate. It was the simplest solution to fix an initial value, even if we knew that it is not the actual value. In this case, VEGETATION images can supply interesting information to solve this problem. They can also be useful to specify the dates of cuts, especially in areas smaller than the RF. In the system ISOP, these dates are only estimated from the kind of grass managements (hay, silage, etc.), and the heat sum between to successive cuts, without taking into account rain episods delaying the harvest or practice changes.
4. Conclusions For the objective of elaborating an evaluation tool of pasture production adapted to a regional scale, it has been confirmed that the use of low spatial resolution sensors, as VEGETATION, is promising. They deliver significant aggregated information with a high temporal resolution. The model developed by Faivre and Fischer (1997), and adapted for the objectives of this work, proved to be a very useful and accurate tool to obtain spectral responses, while taking in account the big spatial variability of information captured in the different wavelengths. Disaggregated information in the near infrared and middle infrared bands corresponding to pure pasture conditions showed a very good correspondence with the temporary evolution of some productive variables as the LAI and dry matter. This correspondence has been improved appreciably from the calculation of the vegetation indices, through bands combination and normalization of the strips that compose it (case of SWVI). These interrelationships have been improved significantly by applying an exponential function and selecting periods where temporary discordances do not exist with the model. Satellite information, through it relationship to biophysical variables as the LAI, give an unique measure of the true dynamics of plant cover. The evaluations from the satellite data are in agreement with the evaluations by the model; however some differences put in evi-
dence some limits of model pasture monitoring in a real time framework.
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