Eva Boegh

Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture

Abstract Airborne multispectral data were acquired by the Compact Airborne Spectral Imager (CASI) for an agricultural area in Denmark with the purpose of quantifying vegetation amount and variations in the physiological status of the vegetation. Spectral reflectances, vegetation indices, and red edge positions were calculated on the basis of the CASI data and compared to field measurements of green leaf area index (LAI; L) and canopy nitrogen concentrations (Nc) sampled at 16 sites. Because of the variety of the samples with respect to vegetation type, leaf age, and phenological developmental stage, the data of L and Nc were uncorrelated. The scattering effect of leaves effectuated a robust linear relationship between L and near-infrared (NIR) reflectance (r=.93), whereas the Nc (vegetative period) was significantly correlated with the spectral reflectance in the green (r=−.88) and far-red wavebands (r=−.94). The correlations between vegetation indices and L were also important, in particular, for the enhanced vegetation index (EVI; r=.88), whereas the red edge position correlated less significantly with Nc (r=.78). Assuming L and Nc to be responsible for most of the spatial variability in the CO2 assimilation rates, remote sensing-based maps of these variables were produced for use in a coupled sun/shade photosynthesis/transpiration model. The predicted rates of net photosynthesis and transpiration compared reasonably with eddy covariance measurements of CO2 and water vapour fluxes recorded at four different crop fields. The results allowed evaluation of the spatial variations in the photosynthetic light, nitrogen, and water use efficiencies. While photosynthesis was linearly related to the transpiration, the light use efficiency (LUE) was found to be dependent on nitrogen concentrations.

Evaluating evapotranspiration rates and surface conditions using Landsat TM to estimate atmospheric resistance and surface resistance

A new method for a composite evaluation of atmospheric resistance, surface resistance, and evapotranspiration rate (λE) is applied to Landsat-5 TM. The method uses three equations to solve for three variables: the atmospheric resistance between the surface and the air (rae); the surface resistance (rs); and the vapour pressure at the surface (es). The novelty of this approach is the estimation of es, which is assessed using the decoupling coefficient (Ω) by Jarvis and McNaughton [Adv. Ecol. Res. 15 (1986) 1]. The input parameters are: surface temperature (Ts), net radiation (Rn), soil heat flux (G), air temperature (Ta), and air humidity (ea). A time series (100 days) of field data collected for a wheat crop is used to illustrate the method, which is validated using latent heat fluxes recorded by the eddy covariance technique. The control of rs on λE is expressed through the Surface Control Coefficient (SCC=1−Ω), which is compared to soil moisture data. The application of the technique in a remote sensing monitoring context is demonstrated for a Danish agricultural landscape containing crops at different stages of development. For the satellite-based estimation of λE and SCC, the variables Ts, Rn, and G are calculated on the basis of Landsat-5 TM, which leaves solar irradiance (for computing Rn), Ta, and ea as the only field data required. The method is directly applicable without any calibration when the soil surface is moist or when the vegetation cover is dense. Only for a dry bare soil surface, where the effective source area of water vapour is below the surface, is the modification of a surface humidity parameter (hs,max) required.

A Remote Sensing Study of the NDVI–Ts Relationship and the Transpiration from Sparse Vegetation in the Sahel Based on High-Resolution Satellite Data

Abstract This article proposes a new approach for estimation of the transpiration rate in sparse canopies. The method relies on a combination of techniques; some of which having a successful background of solid experimental and theoretical justification, while others having only recently been introduced as promising tools for the extraction of environmental information from satellite data. The transpiration rate (λ E v ) is calculated by applying an energy balance approach to the vegetation component of the land surface: λ E v =R n v −H v , where R n v is the net radiation absorbed by the vegetation, and H v is the sensible heat flux between the leaves and the air within the canopy. R n v is calculated through the use of remote sensing and standard meteorological data by combining a conventional method for estimation of the land surface net radiation with a ground-calibrated function of NDVI (normalized differential vegetation index). H v is assessed as a linear function of the temperature difference between vegetation ( T v ) and the mean canopy air stream ( T 0 ). Because the surface temperature ( T s ) recorded by satellite contains combined information of both soil and vegetation, T v is evaluated on the basis of the linear NDVI– T s relationship for individual surface types. T 0 is assessed utilizing recent evidence that ( T s −T 0 ) is linearly related to the difference in surface temperature and air temperature above the canopy ( T s −T a ), with the slope coefficient depending only on canopy structure. The method is tested using remote sensing data ranging from ground-based, airborne, and satellite recordings. The modeled transpiration rates compared well to measurements of sapflow data and latent heat fluxes recorded for a wide range of surface types (agricultural crops, natural vegetation, forest vegetation).

Carbon dioxide exchange over agricultural landscape using eddy correlation and footprint modelling

Abstract Within an agricultural landscape of western Denmark, the carbon dioxide exchange was studied throughout a year (April 1998–March 1999). During the growing season, five eddy correlation systems were operated in parallel over some of the more important crops (winter wheat, winter barley, spring barley, maize and grass). A sixth system was mounted on top of a 48xa0m mast to enable landscape-wide flux measurements both in summer and winter. The spatial distribution of the different crop types was mapped by use of satellite images (Landsat TM and SPOT). A very large diversity in carbon functioning is observed when comparing the carbon dioxide fluxes from the different fields. In the middle of the growing season, May–June, the daytime CO2 fluxes range from a net emission of 5xa0gxa0Cxa0m−2 per day to a carbon assimilation of 12xa0gxa0Cxa0m−2 per day. Due to differences in canopy development this range is maintained almost until the end of the growing season. It is demonstrated that daily CO2 fluxes can be simulated by a combined photosynthesis and soil respiration model. By this approach, it is concluded that the photosynthetic capacity is nearly equal for all the grain crops (120–140xa0μmolxa0m−2xa0s−1) which is moreover 30–40% higher than that of maize and grass. To estimate landscape CO2 fluxes, the measurements from the individual fields are weighted according to their areal contribution. These estimates are found to be in good agreement with the direct measurements conducted from the 48xa0m mast when using a three-dimensional (3-D) footprint to define the source area. Spatial integration is also used to calculate the annual net ecosystem exchange (NEE) which proves to be −31xa0gxa0Cxa0m−2 per year for the landscape as a whole, i.e. there is a net transport of CO2 from the atmosphere to the biosphere. This value holds, however, only for the specific year and for a measurement level close to the ground. The sensitivity of the annual NEE to temperature variations is discussed. By comparing flux measurements at two levels (48 and 2.5xa0m), it is demonstrated that the CO2 budget for an agricultural area may be affected by anthropogenic CO2 sources.

Effective Roughness Calculated from Satellite-Derived Land Cover Maps and Hedge-Information used in a Weather Forecasting Model

In numerical weather prediction, climate and hydrologicalmodelling, the grid cell size is typically larger than the horizontal length scales of variations in aerodynamicroughness, surface temperature and surface humidity. These local land cover variations give rise to sub-gridscale surface flux differences. Especially the roughness variations can give a significantly differentvalue between the equilibrium roughness in each of the patches as compared to the aggregated roughness value,the so-called effective roughness, for the grid cell. The effective roughness is a quantity that secures thephysics to be well-described in any large-scale model. A method of aggregating the roughness step changesin arbitrary real terrain has been applied in flat terrain (Denmark) where sub-grid scale vegetation-drivenroughness variations are a dominant characteristic of the landscape. The aggregation model is a physicaltwo-dimensional atmospheric flow model in the horizontal domain based on a linearized version of theNavier Stoke equation. The equations are solved by the Fast Fourier Transformation technique, hence the codeis very fast. The new effective roughness maps have been used in the HIgh Resolution Limited Area Model(HIRLAM) weather forecasting model and the weather prediction results are compared for a number of casesto synoptic and other observations with improved agreement above the predictions based on currentstandard input. Typical seasonal springtime bias on forecasted winds over land of +0.5 m s-1 and-0.2 m s-1 in coastal areas is reduced by use of the effective roughness maps.

Estimation of evapotranspiration from a millet crop in the Sahel combining sap flow, leaf area index and eddy correlation technique

Abstract Within the framework of HAPEX-II-Sahel, evapotranspiration from a millet crop has been measured during a 2 month period from the middle of the rainy season to the beginning of the dry season. The measurements comprise continuous recording of sap flow from a number of millet plants and of evapotranspiration using the eddy correlation technique. It is shown how the leaf area index may be used for transferring the sapflow rates into areal estimates of transpiration. Emphasis is put on the analysis of the sapflow measurements. The diurnal and seasonal variations are analysed in relation to leaf area, leaf temperature and stomatal resistance.

Carbon dioxide flux, transpiration and light response of millet in the Sahel

Within the framework of the HAPEX-Sahel experiment carried out in Niger during the rainy season of 1992, measurements of fluxes defining the vegetation-atmosphere interaction were conducted over a millet field, for a period of nearly 2 months. These measurements comprised continuous recording of solar radiation, atmospheric carbon dioxide fluxes using the eddy correlation technique, and sap flow through millet plants. Based on biometric measurements of the millet plants comprising height, spacing and leaf area index, the solar radiation is converted to absorbed photosynthetically active radiation (aPAR). The coupling between the three parameters is examined in pairs. The diurnal and seasonal variations are analysed in relation to plant development. A strong linear relationship between aPAR and carbon dioxide assimilation can be established from the measurements, giving a quantum yield of 0.03 mol CO2 mol−1 quanta. A comparison between CO2 flux and transpiration shows that this relationship is affected by the water vapour pressure deficit of the atmosphere, but corresponds to the results found for other drought-tolerant C4 crops.

Models of CO2 and water vapour fluxes from a sparse millet crop in the Sahel

Abstract Canopy fluxes of water vapour and CO 2 from a sparse millet crop are simulated on the basis of a leaf scale model designed to predict stomatal conductance, leaf temperature, transpiration rate and photosynthetic rate for millet on a diurnal basis. The modelled leaf fluxes are extrapolated using two different big-leaf approaches. In the traditional big-leaf approach, all leaves are exposed to the same microenvironment which is different from the environmental conditions above the canopy, whereas in the modified big-leaf approach the canopy is regarded as a partly shaded big-leaf. In the sun/shade big-leaf model, soil reflection, diffuse radiation and separate evaluations of the radiation load on sunlit and shaded leaf surfaces are taken into account. Due to the low fraction of shaded leaves in the sparse canopy, the two types of big-leaf models predict both canopy fluxes equally well. The sensitivity of the modelled fluxes to the various input parameters was ranked for the identification of the most important parameters controlling photosynthesis and transpiration. This information is used for identification of more simple scaling models aimed at predicting daily canopy fluxes. The influx of sensible heat to the leaf was found to be an important energy source for transpiration. It was confirmed that daily transpiration can be parameterized by the air humidity gradient using only the leaf area index (LAI) for the evaluation of seasonal changes in bulk stomatal conductance. The photosynthetic rate was found to be most sensitive to radiation and leaf temperature. It is shown that the daily canopy photosynthesis can be estimated on the basis of LAI and midday values (1200xa0h) of incoming radiation density and leaf temperature.

Combining weather prediction and remote sensing data for the calculation of evapotranspiration rates: application to Denmark

Evapotranspiration rates in Denmark were estimated using Advanced Very High Resolution Radiometer (AVHRR) satellite data and weather conditions predicted by a high-resolution weather forecast model (HIRLAM). The predictions were used both for atmospheric correction of satellite data and for remote sensing based calculation of net radiation, sensible heat fluxes and evapotranspiration rates. Climate predictions at 12 GMT were used as proxies for the atmospheric conditions at the time of the afternoon satellite passage (12.30–14.30 GMT). The air temperature at the time of the satellite passage was retrieved with a rms error of 1.9°C, and the rms error of the retrieved air humidity was 204 Pa. The evapotranspiration results were significantly influenced by the spatial distribution of weather conditions. Due to the encirclement of Denmark by sea shorelines, sea breezes extending more than 30u2009km inland were responsible for the intrusion of cooler air temperatures which increased the sensible heat fluxes and suppressed the evapotranspiration rates. The predictions were linearly related to eddy-covariance flux measurements representing agricultural land, beech forest and conifer forest, but the relationships were also characterized by a large degree of scattering. The results are discussed in relation to inaccuracies and future perspectives.

Multi-scale remote sensing based estimation of leaf area index and nitrogen concentration for photosynthesis modelling

Leaf area index (LAI) and leaf nitrogen concentrations (N) are two important quantities controlling the photosynthetic rates of vegetation canopies. While the green LAI is closely related to the absorption of light used in photosynthesis, fertilization rates determine the leaf nitrogen concentrations which, in turn, govern the maximum photosynthetic capacity of the leaves. Using airborne multi-spectral images from mid-June in Denmark, it was found that nitrogen concentrations are strongly correlated with spectral reflectance in the green and far-red spectral bands. In contrast, the LAI correlates strongly with the near-infrared reflectance, the Enhanced Vegetation Index and the Normalized Difference Vegetation index. Because of the feasibility of the new generation of satellites, such as Terra-MODIS and Envisat-MERIS, to measure reflectance in a narrow green band, these results suggest that independent quantities of nitrogen concentrations and LAI may also be derived using data from such sensors. Due to the predominance of small fields in Denmark, the application of multi-scale resolution remote sensing data is in the present study used to transfer the regression equations established at field level to lower-resolution satellite data such as MODIS (500 m). Because of temporal variations in leaf specific weights, it is found that the satellite observations are related to the areal (rather than mass) nitrogen concentrations. The remote sensing based estimates of LAI and N are finally applied for photosynthesis modelling and compared with atmospheric CO/sub 2/ fluxes recorded by the eddy covariance technique.