Thomas Jarmer

Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: A feasibility study

Soil chemical properties from different locations in the Trier region, Rhineland-Palatinate, SW Germany were evaluated using VIS/NIR reflectance spectrometry (ASD FieldSpec-II spectrometer, 0.4–2.5 μm) and partial least-square regression (PLS). Generally, laboratory spectrometry performed better than field spectrometry probably due to strong interferences of soil structure. In a plot experiment reliable estimations were obtained for total amounts of Ca, Mg, Fe, Mn and K but not for organic carbon and nitrogen. In the landscape-scale context the estimations for organic carbon could be significantly improved but it was also concluded that the development of statistical prediction models is limited to geologically homogeneous areas. In both experiments CAL extractable nutrients could not be satisfactorily estimated. This excludes diffuse VIS/NIR spectrometry as a diagnosis tool of short- or medium-term changes of the soils nutrient status. However, the method can be used as a quick screening method in questions where the spatial distribution of organic carbon and total metal contents is addressed, as in soil development and soil degradation monitoring, and when time or laboratory costs are critical factors.

Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT+SAIL)

As permanent grassland is a large‐scale land‐use type in Central Europe, grassland inventories are relevant for ecological and agrarian issues. The objective of this study was to assess structural and biochemical grassland parameters (LAI, chlorophyll, water and dry matter contents) from field spectroradiometer data (ASD FieldSpec II) by radiative transfer modelling (PROSPECT+SAIL). Constraints were necessary to compensate the ill‐posed nature of model inversion for accurate parameter retrieval. In this context, we found the foliage moisture content to play an important role. After coupling the equivalent water thickness and the dry matter content in a ratio of 4:1, the estimation accuracy for the LAI clearly improved. In terms of LAI, the RMSE decreased from 0.86 to 0.74, and the range of LAI values measured in the field (min = 0.10, max = 5.88) was reproduced exactly with estimates ranging from 0.05 to 5.46. The spectra reconstructed by PROSPECT+SAIL using the inverted parameter estimates coincided well with the measured spectra. Nonetheless, obtained canopy chlorophyll contents tended to be too high. When using ground measured chlorophyll data for spectra generation, simulated reflectances were clearly higher in the visible domain than the measured ones. This is partly attributed to the chlorophyll absorption coefficients of PROSPECT that may not be true for the majority of grassland plant species in reality. Neverthelesss, the obtained results prove the potential of PROSPECT+SAIL for retrieving structural and biochemical grassland parameters; results may be appropriate for assimilation in the modelling of plant growth or carbon cycle, for example.

Estimation of Some Chemical Properties of an Agricultural Soil by Spectroradiometric Measurements

Abstract The contents of nitrogen and organic carbon in an agricultural soil were analyzed using reflectance measurements ( n = 52) performed with an ASD FieldSpec-II spectroradiometer. For parameter prediction, empirical models based on partial least squares (PLS) regression were defined from the measured reflectance spectra (0.4 to 2.4 μm). Here, reliable estimates were obtained for nitrogen content, but prediction accuracy was only moderate for organic carbon. For nitrogen, the real spatial pattern of within-field variability was reproduced with high accuracy. The results indicate the potential of this method as a quick screening tool for the spatial assessment of nitrogen and organic carbon, and therefore an appropriate alternative to time- and cost-intensive chemical analysis in the laboratory.

Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data

Leaf area index (LAI) is one of the most important plant parameters when observing agricultural crops and a decisive factor for yield estimates. Remote-sensing data provide spectral information on large areas and allow for a detailed quantitative assessment of LAI and other plant parameters. The present study compared support vector regression (SVR), random forest regression (RFR), and partial least-squares regression (PLSR) and their achieved model qualities for the assessment of LAI from wheat reflectance spectra. In this context, the validation technique used for verifying the accuracy of an empirical–statistical regression model was very important in order to allow the spatial transferability of models to unknown data. Thus, two different validation methods, leave-one-out cross-validation (cv) and independent validation (iv), were performed to determine model accuracy. The LAI and field reflectance spectra of 124 plots were collected from four fields during two stages of plant development in 2011 and 2012. In the case of cross-validation for the separate years, as well as the entire data set, SVR provided the best results (2011: R2cv = 0.739, 2012: R2cv = 0.85, 2011 and 2012: R2cv = 0.944). Independent validation of the data set from both years led to completely different results. The accuracy of PLSR (R2iv = 0.912) and RFR (R2iv = 0.770) remained almost at the same level as that of cross-validation, while SVR showed a clear decline in model performance (R2iv = 0.769). The results indicate that regression model robustness largely depends on the applied validation approach and the data range of the LAI used for model building.

Mapping topsoil organic carbon in non-agricultural semi-arid and arid ecosystems of Israel.

Mapping of soil organic carbon (SOC) was accomplished with remote sensing methods to assess its spatial variability. The relationship between bi-directional reflectance measurements and SOC was investigated with respect to C.I.E. color coordinates. Empirical relationships were generated for the spectral detection of SOC of two semi-arid to arid study sites. These regression models allowed the prediction of SOC with a cross-validated r z = 0.910 (RMSE cv = 2.825) and r 2 of 0.795 (RMSE cv = 2.113), respectively. Because C.I.E. color coordinates were found to be appropriate parameters for predicting the SOC, reflectance values of Landsat TM bands were transformed into C.I.E. color coordinates. The C.I.E. based regression models were applied to a Landsat image to estimate SOC in the spatial domain. Concentrations predicted from satellite data corresponded well with concentration ranges derived from chemical analysis. Estimated concentrations reflect the geographic conditions and depend on annual rainfall, with a general trend to decreasing SOC with increasing aridity.

The Potential of Pan-Sharpened EnMAP Data for the Assessment of Wheat LAI

In modern agriculture, the spatially differentiated assessment of the leaf area index (LAI) is of utmost importance to allow an adapted field management. Current hyperspectral satellite systems provide information with a high spectral but only a medium spatial resolution. Due to the limited ground sampling distance (GSD), hyperspectral satellite images are often insufficient for precision agricultural applications. In the presented study, simulated hyperspectral data of the upcoming Environmental Mapping and Analysis Program (EnMAP) mission (30 m GSD) covering an agricultural region were pan-sharpened with higher resolution panchromatic aisaEAGLE (airborne imaging spectrometer for applications EAGLE) (3 m GSD) and simulated Sentinel-2 images (10 m GSD) using the spectral preserving Ehlers Fusion. As fusion evaluation criteria, the spectral angle (αspec) and the correlation coefficient (R) were calculated to determine the spectral preservation capability of the fusion results. Additionally, partial least squares regression (PLSR) models were built based on the EnMAP images, the fused datasets and the original aisaEAGLE hyperspectral data to spatially predict the LAI of two wheat fields. The aisaEAGLE model provided the best results (R2cv = 0.87) followed by the models built with the fused datasets (EnMAP–aisaEAGLE and EnMAP–Sentinel-2 fusion each with a R2cv of 0.75) and the simulated EnMAP data (R2cv = 0.68). The results showed the suitability of pan-sharpened EnMAP data for a reliable spatial prediction of LAI and underlined the potential of pan-sharpening to enhance spatial resolution as required for precision agriculture applications.

Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations

The determination of soil texture and organic carbon across agricultural areas provides important information to derive soil condition. Precise digital soil maps can help to till agricultural fields with more accuracy, greater cost-efficiency and better environmental protection. In the present study, the laboratory analysis of sand, silt, clay and soil organic carbon (SOC) content was combined with hyperspectral image data to estimate the distribution of soil texture and SOC across an agricultural area. The aim was to identify regions with similar soil properties and derive uniform soil regions based on this information. Soil parameter data and corresponding laboratory spectra were used to calibrate cross-validated (leave-one-out) partial least squares regression (PLSR) models, resulting in robust models for sand (R2 = 0.77, root-mean-square error (RMSE) = 5.37) and SOC (R2 = 0.89, RMSE = 0.27), as well as moderate models for silt (R2 = 0.62, RMSE = 5.46) and clay (R2 = 0.53, RMSE = 2.39). The regression models were applied to Airborne Imaging Spectrometer for Applications DUAL (aisaDUAL) hyperspectral image data to spatially estimate the concentration of these parameters. Afterwards, a decision tree, based on the Food and Agriculture Organization (FAO) soil texture classification scheme, was developed to determine the soil texture for each pixel of the hyperspectral airborne data. These soil texture regions were further refined with the spatial SOC estimations. The developed method is useful to identify spatial regions with similar soil properties, which can provide a vital information source for an adapted treatment of agricultural fields in terms of the necessary amount of fertilizers or water. The approach can also be adapted to wider regions with a larger sample size to create detailed digital soil maps (DSMs). Further, the presented method should be applied to future hyperspectral satellite missions like Environmental Mapping and Analysis Program (EnMap) and Hyperspectral Infrared Imager (HyspIRI) to cover larger areas in shorter time intervals. Updated DSMs on a regular basis could particularly support precision farming aspects.

On the potential of Wireless Sensor Networks for the in-situ assessment of crop leaf area index

Design of a novel low-cost sensor modification for non-destructive LAI assessment.Maize field campaigns including a comparative analysis with a standard instrument.An impact evaluation showing high accuracy and robustness of our approach. A precise and continuous in-situ monitoring of bio-physical crop parameters is crucial for the efficiency and sustainability in modern agriculture. The leaf area index (LAI) is an important key parameter allowing to derive vital crop information. As it serves as a valuable indicator for yield-limiting processes, it contributes to situational awareness ranging from agricultural optimization to global economy. This paper presents a feasible, robust, and low-cost modification of commercial off-the-shelf photosynthetically active radiation (PAR) sensors, which significantly enhances the potential of Wireless Sensor Network (WSN) technology for non-destructive in-situ LAI assessment. In order to minimize environmental influences such as direct solar radiation and scattering effects, we upgrade such a sensor with a specific diffuser combined with an appropriate optical band-pass filter. We propose an implementation of a distributed WSN application based on a simplified model of light transmittance through the canopy and validate our approach in various field campaigns exemplarily conducted in maize cultivars. Since a ground truth LAI is very difficult to obtain, we use the LAI-2200, one of the most widely established standard instruments, as a reference. We evaluate the accuracy of LAI estimates derived from the analysis of PAR sensor data and the robustness of our sensor modification. As a result, an extensive comparative analysis emphasizes a strong linear correlation ( r 2 = 0.88 , RMSE=0.28) between both approaches. Hence, the proposed WSN-based approach enables a promising alternative for a flexible and continuous LAI monitoring.

On the potential of Wireless Sensor Networks for the in-field assessment of bio-physical crop parameters

The exploration of bio-physical crop parameters is fundamental for the efficiency of smart agriculture. The leaf area index (LAI) is one of the most important crop parameters and serves as a valuable indicator for yield-limiting processes. It contributes to situational awareness ranging from agricultural optimization to global economy. In this paper, we investigate the potential of Wireless Sensor Networks (WSNs) for the in-field assessment of bio-physical crop parameters. Our experiences using commercial off-the-shelf (COTS) sensor nodes for the indirect and nondestructive LAI estimation are described. Furthermore, we present the design of our measurement architecture and results of various in-field measurements. By directly comparing the results achieved by WSN technology with those of a conventional approach, represented by a widely used standard instrument, we analyze whether bio-physical crop characteristics can be derived from WSN data with a desired accuracy. Moreover, we propose a simple approach to significantly enhance the accuracy of COTS sensor nodes for LAI estimation while, at the same time, reveal open challenges.

Cloud Removal in Image Time Series through Sparse Reconstruction from Random Measurements

In this paper, we propose a cloud removal algorithm for scenes within a satellite image time series based on synthetization of the affected areas via sparse reconstruction. The high spectrotemporal dimensionality of time series allows applying pixel-based sparse reconstruction techniques efficiently, estimating the values below a cloudy area by observing the spectral evolution in time of pixels in cloud-free areas. The process implicitly compensates the overall atmospheric interactions affecting a given image, and it is possible even if only one acquisition is available for a given period of time. The dictionary, on the basis of which the data are reconstructed, is selected randomly from the available image elements in the time series. This increases the degree of automation of the process, if the area containing clouds and their shadows is given. Favorable comparisons with similar methods and applications to supervised classification and change detection show that the proposed algorithm restores images locally contaminated by clouds and their shadows in a satisfactory and efficient way.