Kirsten Schelde

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.

Particle leaching and particle-facilitated transport of phosphorus at field scale

Strongly sorbing compounds such as P, pesticides, and heavy metals can be transported through soils while being adsorbed to mobile colloidal particles. While the rapid leaching of nonadsorbing chemicals is relatively well understood, the particle-facilitated transport of highly sorbing chemicals such as P requires further investigation. The aim of this work was to study spatial variations in particle-facilitated transport of P at the field scale, and investigate which soil-physical or chemical parameters relate to the observed variations. Leaching experiments were performed in the laboratory on 42 undisturbed soil columns sampled in a grid covering 25 by 30 m of an agricultural field. The columns were equilibrated in the laboratory to a pressure head of −20 cm and irrigated at a rate of 10 mm h −1 with an artificial rainwater solution. The experiments exhibited considerable variation among the columns in the accumulated mass of particles and P leached during the 3.5 h of irrigation. Columns taken from the lower part of the field showed the highest mass of leached particles. These columns had higher clay contents and contained more continuous macropores. The mass of particles was negatively correlated to the average electrical conductivity of the effluent, and positively correlated to the macropore flow velocity. The accumulated masses of particulate organic and inorganic P were linearly related to the accumulated mass of particles leached. About 75% of the leached P was transported in a particle-facilitated manner. Overall, soil structure controlled to a large extent the leaching of particles and particle bound P.

Diffusion-limited mobilization and transport of natural colloids in macroporous soil

This study examines the dynamics of colloid mobilization and leaching from macroporous soil columns by means of laboratory experiments and numerical modeling. On the basis of a previous column study involving high and low water flow rates in structured soil, we designed a novel experiment emphasizing the time-dependence of the colloid release process. Intact macroporous soil columns were exposed to variable pauses in irrigation (flow interruption for 30 min, 1 d, or 7 d) followed by resumed infiltration. The experiments showed that (i) there was a seemingly unlimited source of in situ colloids even after prolonged leaching and (ii) the peak concentration of colloids in the effluent after the flow interruption increased with increasing length of the preceding pause. The results demonstrated that colloid mobilization is not controlled by hydrodynamic shear induced by the flowing water but is a time-dependent and possibly diffusion-limited process. We developed a simple, equivalent macropore model to investigate the hypothesis that colloid release to the flowing water is governed by two diffusion processes, one in a uniform water film lining the macropore and one in the crust of the macropore. The model was capable of reproducing and explaining the characteristic results of our soil column experiments and required no recalibration of exchange process parameters to simulate the particle mobilization after a flow interruption. However, model calibration yielded unexpected results with respect to the size of the diffusion coefficient of the crust and did not warrant accepting the dual diffusion model hypothesis. Using a simpler mass transfer concept to quantify the mobilization of colloids in 21 soil columns, we found mass transfer coefficients to be about 30 times higher in the water film than in the crust.

Modelling mean nitrate leaching from spatially variable fields using effective hydraulic parameters

When using simulation models for estimating the mean nitrate leaching on different soil types, the common approach is to interpret the field as a single equivalent soil column using effective hydraulic parameters, which are estimated from point measurements. The use of effective hydraulic parameters was evaluated on a coarse sandy soil and a sandy loam using the one-dimensional mechanistic model, DAISY. On each location, texture, soil water retention and hydraulic conductivity from 57 points were measured within an area of ca. 0.25 ha. The following approaches for estimation of effective hydraulic conductivity were examined: (1) geometric mean; (2) arithmetic mean; (3) estimated arithmetic mean from a lognormal distribution; and (4) mean estimated from a stochastic large-scale model for water flow, similar to the Richards equation in one dimension, but with large-scale effective parameters accounting for the local three-dimensional flow. The approach of interpreting the field as a number of non-interacting columns was examined by calculating the mean of the field as the mean of the 57 soil columns. The nitrate concentrations simulated by DAISY were compared with nitrate concentrations measured by ceramic suction cups at the 57 points at 25 cm and 80 cm depths during the winter period 1989/1990. At both locations, the nitrate concentrations simulated by the geometric mean, the stochastic approach and the mean of the 57 simulations matched the observed nitrate concentrations while the other approaches gave unreliable results on the coarse sand. Hence, to simplify the calculations the geometric mean can be used.

Comparing annual and perennial crops for bioenergy production - influence on nitrate leaching and energy balance.

Production of energy crops is promoted as a means to mitigate global warming by decreasing dependency on fossil energy. However, agricultural production of bioenergy can have various environmental effects depending on the crop and production system. In a field trial initiated in 2008, nitrate concentration in soil water was measured below winter wheat, grass‐clover and willow during three growing seasons. Crop water balances were modelled to estimate the amount of nitrate leached per hectare. In addition, dry matter yields and nitrogen (N) yields were measured, and N balances and energy balances were calculated. In willow, nitrate concentrations were up to approximately 20 mg l−1 nitrate‐N during the establishment year, but declined subsequently to <5 mg l−1 nitrate‐N, resulting in an annual N leaching loss of 18, 3 and 0.3 kg ha−1 yr−1 N in the first 3 years after planting. A similar trend was observed in grass‐clover where concentrations stabilized at 2–4 mg l−1 nitrate‐N from the beginning of the second growing season, corresponding to leaching of approximately 5 kg ha−1 yr−1 N. In winter wheat, an annual N leaching loss of 36–68 kg ha−1 yr−1 was observed. For comparison, nitrate leaching was also measured in an old willow crop established in 1996 from which N leaching ranged from 6 to 27 kg ha−1 yr−1. Dry matter yields ranged between 5.9 and 14.8 Mg yr−1 with lowest yield in the newly established willow and the highest yield harvested in grass‐clover. Grass‐clover gave the highest net energy yield of 244 GJ ha−1 yr−1, whereas old willow, winter wheat and first rotation willow gave net energy yields of 235, 180 and 105 GJ ha−1 yr−1. The study showed that perennial crops can provide high energy yields and significantly reduce N losses compared to annual crops.

Modification of DAISY SVAT model for potential use of remotely sensed data

The SVAT model DAISY is modified to be able to utilize remote sensing (RS) data in order to improve prediction of evapotranspiration and photosynthesis at plot scale. The link between RS data and the DAISY model is the development of the minimum, unstressed, canopy resistance r min c during the growing season. Energy balance processes are simulated by applying resistance networks and a two-source model. Modeled data is validated against measurements performed for a winter wheat plot. Soil water content is measured by time domain reflectometry. Crop dry matter content and leaf area index are modeled adequately. Modeled soil water content, based on a Brooks and Corey [Brooks, R.H., Corey, A.T., 1964. Hydraulic properties of porous media. Hydrology Paper no. 3, Colorado University, Fort Collins, CO, 27 pp.] parameterization, from 0 to 20, 0 to 50 and 0 to 100 cm is calibrated satisfactorily against measured TDR values. Simulated and observed energy fluxes are generally in good agreement when water supply in the root zone is not limiting. With decreasing soil moisture content during a longer drought period, modeled latent heat flux is lower than observed, which calls for both improved parameterizations for environmental controls and for a improved estimation of ther min c parameter.

Estimating sensible and latent heat fluxes from a temperate broad-leaved forest using the Simple Biosphere (SiB) model

Sensible (H) and latent heat (λE) flux densities from a well-watered, broad-leaved forest of Nothofagus trees were estimated using the simple biosphere (SiB) model. Model inputs included micrometeorological measurements made at a reference height (36 m) just above the canopy and site parameters such as the tree canopy leaf area index of seven. Half-hourly diurnal courses of modelled H and λE were generally in good agreement with eddy covariance flux measurements (±30 Wm−2 on average) over six late-summer days of variable weather conditions. The most important model variables determining these fluxes were the bulk leaf boundary-layer resistance (rb), proportional to leaf size, for H and canopy (stomatal) resistance (rc), regulated by radiation interception and air saturation deficit, for λE. Recent developments in the modelling of rc for different vegetation types are discussed. Maximum daily ground (forest floor) evaporation rate (Eg) was 0.5 mm day−1, accounting for up to 20% of forest evaporation. Initial model estimates of Eg in the forest were nearly 50% less than those of lysimeter measurements. However, agreement between measured and modelled Eg was only about ± 0.05 mm day−1 after reduction of the trunk space eddy diffusive resistance (rd) based on a comparison with other values in the literature.

Quantification of SOC and Clay Content Using Visible Near-Infrared Reflectance–Mid-Infrared Reflectance Spectroscopy With Jack-Knifing Partial Least Squares Regression

Abstract A total of 125 soil samples were collected from a Danish field varying in soil texture from sandy to loamy. Visible near-infrared reflectance (Vis-NIR) and mid-infrared reflectance (MIR) spectroscopy combined with chemometric methods were used to predict soil organic carbon (SOC) and clay contents. The main objective of this study was to find the best model for predicting SOC and clay content in the sampled field using Vis-NIR, MIR, and the combination of Vis-NIR and MIR and using different model development techniques. The secondary objectives were (i) to use iterations of calculation to find the optimal number of replicates for MIR measurements based on the root mean square error of cross validation (RMSECV) and (ii) to apply partial least squares regression in combination with jack-knifing (JK) to identify the most important part of spectral variables and the best model for predicting SOC and clay content. The study showed that with repeated MIR measurements it was possible to improve RMSECV by 20%. The optimal number of repeated MIR measurements was between 3 and 4 for SOC and clay content. Comparing all the prediction results, the combination of MIR and Vis-NIR with the partial least squares regression–JK technique resulted in the lowest prediction errors (RMSECVsoc of 0.35% and RMSECVclay of 1.05%). The average uncertainties of laboratory measurements were 0.39% and 1.86% for SOC and clay contents, respectively. All models had acceptable and—to a large extent—comparable margins of error. Partial least squares regression with JK simplified and enhanced the interpretation of the developed models because of a reduction in the number of variables used in the models.

Estimating transpiration rates in a Danish agricultural area using landsat thermal mapper data

Abstract One important step towards an integrated understanding of water balance components on plant productivity is the detection and evaluation of the plant water uptake (i.e. the transpiration rate). In this study, the transpiration rates from agricultural fields (LEv) are calculated using Landsat thermal mapper data through applying an energy balance approach to the vegetation component of the land surface: LEv = Rnv−Hv, where Rnv is the net radiation absorbed by the vegetation, and Hv is the sensible heat flux between the leaves and the air. The method requires field data of incoming solar radiation, air temperature and windspeed. The surface albedo is evaluated using the satellite data. The fraction of net radiation absorbed by the begetation ( ƒ Rn ) is related to the NDVI, and the vegetation temperature is determined on the basis of the relationship between NDVI and surface temperature (Ts). The methodology is validated using field data. For successful application to remote sensing data, correct assessment of the empirical relationship between NDVI and ƒ Rn is required.

Soil organic carbon and particle sizes mapping using vis-NIR, EC and temperature mobile sensor platform

Mobile sensor platform was used to predict and map SOC and particle sizes.Vis-NIRS, EC and temperature sensory data was used individually and fused.Successful calibration models were obtained for all soil properties.Optimal combination of sensor data was field and property dependent.Detailed maps of soil properties were generated using sensor data. Soil organic carbon (SOC) is an important parameter in the climate change mitigation strategies and it is crucial for the function of ecosystems and agriculture. Particle size fractions affect strongly the physical and chemical properties of soil and thus also SOC. Conventional analyses of SOC and particle sizes are costly limiting the detailed characterization of soil spatial variability and fine resolution mapping. Mobile sensors provide an alternative approach to soil analysis. They offer densely spaced georeferenced data in a cost-effective manner. In this study, two agricultural fields (Voulund1 and Voulund2) in Denmark were mapped with the Veris mobile sensor platform (MSP). MSP collected simultaneously visible near infrared spectra (vis-NIR; 350-2200nm), electrical conductivity (EC: shallow; 0-30cm, deep; 0-90cm), and temperature measurements. Fuzzy k-means clustering was applied to the obtained spectra to partition the fields and to select representative samples for calibration purposes. Calibration samples were analyzed for SOC and particle sizes (clay, silt and sand) using conventional wet chemistry analysis. The objectives of this study were to determine whether it is the single sensors or the fusion of sensor data that provides the best predictive ability of the soil properties in question. Using partial least square regression (PLS) excellent calibration results were generated for all soil properties with a ratio of performance to deviation (RPD) values above 2. The best predictive ability for SOC was obtained using a fusion of sensor data. The calibration models based on vis-NIR spectra and temperature resulted in RMSECV=0.14% and R2=0.94 in Voulund1. In Voulund2, the combination of EC, temperature and spectral data generated a SOC model with RMSECV=0.17% and R2=0.93. The highest predictive ability for clay was obtained using spectral data only in Voulund1 (RMSECV=0.34% and R2=0.76). Whereas in Voulund2, improved results were obtained after combining spectral and temperature data RMSECV=0.20% and R2=0.92. The best predictions of silt and sand were obtained when using spectral data only and resulted in RMSECV=0.35%, R2=0.82 and RMSECV=0.85%, R2=0.81, respectively, in Voulund1 and RMSECV=0.31%, R2=0.86 and RMSECV=0.74%, R2=0.92, respectively, in Voulund2.The best models were used to predict soil properties from the field spectra collected by the MSP. Maps of predicted soil properties were generated using ordinary kriging. Results from this study indicate that robust calibration models can be developed on the basis of the MSP and that high resolution field maps of soil properties can be compiled in a cost-effective manner.