Anton Thomsen

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.

Comparison of methods for simulating effects of nitrogen on green area index and dry matter growth in winter wheat

Abstract Crop simulation models are increasingly being used to simulate the response of crop production to variation in input use. Current and widely used crop models differ strongly in the way in which green area index (GAI) and radiation use efficiency (RUE) is affected by nitrogen (N) supply. Three different methods of simulating effect of N on development of GAI were tested in combination with three different methods of simulating effects of N on RUE. The methods tested represent functions applied in three existing wheat simulation models: FASSET, Sirius and DAISY. GAI depends in FASSET on crop dry weight, temperature and N uptake, in Sirius on temperature and N uptake, and in DAISY GAI depends on dry weight and temperature. Sirius has no effect of N on RUE, DAISY uses a segmented linear response function, and FASSET uses a curvilinear response. The different methods were implemented in the FASSET model framework, and maximum RUE at optimal N supply was calibrated for each model combination using 4 years of growth analysis data from an experiment in winter wheat with three rates of mineral N fertiliser at Research Centre Foulum, Denmark. The model combinations were validated using 2 years of growth analysis data from an experiment at Research Centre Foulum with different timing of N application. The model combinations were tested against grain yield response to increasing N supply from a series of N fertiliser experiments in Denmark. The observed development of GAI and dry weight over time in the calibration and validation data sets could be reproduced by all combinations of GAI and RUE models. This shows that a large variation in N supply rates is more important than detailed sampling over time when validating and testing crop response to N supply. The observed response of grain yield to increasing rates of mineral N fertiliser could be reproduced by most of the model combinations. However, the yield increase was overestimated with the use of a segmented linear response of RUE to N supply, and the optimal N rate was underestimated when the N response of RUE was ignored.

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.

Comparing Predictive Abilities of Three Visible-Near Infrared Spectrophotometers for Soil Organic Carbon and Clay Determination:

Due to advances in optical technology, a wide range of spectrometers is available. Recent interests in soil global libraries and sensor fusion presents a challenge with respect to combining data from different instrumentation. Little research, however, has been done on the comparison of visible-near infrared (vis-NIR) spectrometers for soil characterisation. There is a need for more work on the effects of scanning strategies and use of different soil instrumentation. We compared three vis-NIR spectrometers with varying resolution, signal-to-noise ratios and spectral range. Their performance was evaluated based on spectra collected from 194 Danish top soils and used to determine soil organic carbon (SOC) and clay content. Scanning procedures for the three spectrophotometers where done according to uniform laboratory protocols. Soil organic carbon and clay calibrations were performed using PLS regression. One third of the data set was used as an independent test set. A range of spectral preprocessing methods was applied in search of model improvement. Validation for SOC content using an independent data set derived from all three spectrophotometers provided values of RMSEP between 0.45% and 0.52%, r2=0.42–0.59 and RPD = 1.2–1.4. Clay content was predicted with a higher precision resulting in RMSEP values between 2.6% and 2.9%, r2 = 0.71–0.77 and RPD values in the range from 2.2 to 2.5. No substantial differences in the prediction accuracy were found for the three spectrometers, although there was a tendency that, in the tradeoff between noise and resolution, low noise was the more important for SOC and clay predictions. The application of different spectral preprocessing procedures did not generate important improvements of the calibration models either. Additionally, data simulation analysis, including resampling to a coarser resolution and addition of noise, was performed. No, or very little, effect of sampling resolution and additional noise on the performance of the spectrophotometers was reported. The results from this study showed that, as long as strict laboratory scanning protocols were followed, no significant differences in constituent determination were found, despite differences in spectral range, spectral resolution, spectral sampling intervals and sample presentation methods. The differences in predictive abilities between the spectrometers were mostly due to differences in spectral range.

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.

Prediction of topsoil organic matter and clay content from measurements of spectral reflectance and electrical conductivity

Soil organic matter (SOM) and clay content of a soil characterized as a coarse sandy loam were modelled using hyperspectral reflectance data acquired with a spectrometer and soil electrical conductivity (SEC) data acquired with an EM38 instrument manufactured by Geonics Ltd. The partial least squares (PLS) regression method was applied and the results validated using cross validation. First, the models were calibrated using only spectral reflectance data; then EM38 data were included in the X-matrix of predictors. Although SEC is significantly correlated with clay content, the results showed that EM38 data did not improve model performance for the estimation of soil organic matter content and clay content, despite the fact that EM38 showed significant correlation with clay content.

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.

Integrating lysimeter drainage and eddy covariance flux measurements in a groundwater recharge model

Abstract Field-scale water balance is difficult to characterize because controls exerted by soils and vegetation are mostly inferred from local-scale measurements with relatively small support volumes. Eddy covariance flux and lysimeters have been used to infer and evaluate field-scale water balances because they have larger footprint areas than local soil moisture measurements. This study quantifies heterogeneity of soil deep drainage (D) in four 12.5-m2 repacked lysimeters, compares evapotranspiration from eddy covariance (ETEC) and mass balance residuals of lysimeters (ETwbLys), and models D to estimate groundwater recharge. Variation in measured D was attributed to redirection of snowmelt infiltration and differences in lysimeter hydraulic properties caused by surface soil treatment. During the growing seasons of 2010, 2011 and 2012, ETwbLys (278, 289 and 269 mm, respectively) was in good agreement with ETEC (298, 301 and 335 mm). Annual recharge estimated from modelled D was 486, 624 and 613 mm for three calendar years 2010, 2011 and 2012, respectively. In summary, lysimeter D and ETEC can be integrated to estimate and model groundwater recharge. Editor D. Koutsoyiannis

Soil moisture retrieval using the Danish L- & C-band polarimetric SAR

Danish polarimetric SAR data have been applied to estimate soil moisture (SM). The preliminary results evaluated with the L- and C-band SAR data acquired at the Danish test site Foulum during a number of missions in 1994 and 1995 are presented. In situ data have been collected during the SAR missions and the variations of SM at field scale are discussed. The integral equation method (IEM), the Dubois empirical model and the Oh polarized-ratio model have been used as algorithms to inverse SM from polarimetric SAR data under near bare field conditions. Comparisons between the inversions and the in situ measured data showed that the Dubois empirical model gave the best results for inversion of SM. The standard deviations between the inversed and the measured data at C-band are s(m/sub u/)=6.1% for SM and s/sub /spl sigma//=0.37 cm for soil surface rms height, respectively. C-band data are better than L-band data for estimation of surface roughness.

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.