Airborne hyperspectral imaging of spatial soil organic carbon heterogeneity at the field-scale
Highlights
► Soil organic carbon (SOC) is heterogeneously distributed within agricultural fields. ► Accurate SOC prediction was done with airborne hyperspectral imaging at field-scale. ► A pixel-wise map of SOC content is more realistic than an interpolated map. ► Imaging spectroscopy is a useful tool for precision agriculture and soil C monitoring.
Introduction
Soil organic carbon (SOC) is a particularly important soil property affecting other soil parameters as well as crop growth. The spatial distribution of the SOC content is often heterogeneous within agricultural fields. A detailed knowledge on the variability of SOC at the field-scale is required to support applications such as precision agriculture (Gebbers and Adamchuk, 2010). Precision agriculture aims to match both agricultural input and practices to the spatial and temporal variability of soils, crops, pests and weeds within a field, instead of the uniform and suboptimal management of an entire field. The spatial distribution of the SOC content within a field influences various crop stand parameters such as crop nutrient status or crop yield and the related amount and distribution of fertilizer needed as well as the behaviour of pesticides in the soil (Ladoni et al., 2010, Patzold et al., 2008, Wauchope et al., 2002). In addition, SOC is closely related to soil quality, as it performs as an indicator of soil erosion and degradation (De Gryze et al., 2008). Depending on land-use and management, agricultural soils can be a major source or sink for carbon (Lal, 2010, Sleutel et al., 2007). A high sampling density is necessary to produce SOC maps with an adequate spatial resolution, as required in precision agriculture or for soil C monitoring (Chang et al., 2001, Kerry et al., 2010, Stevens et al., 2006). However, conventional soil analyses are often both laborious and expensive. Imaging spectroscopy in the visible (VIS) and near-infrared (NIR) region is a more efficient, rapid and less expensive technology which provides data with high spatial and temporal resolution (Gomez et al., 2008, Stevens et al., 2010).
VIS, NIR and also mid-infrared (MIR) techniques are reported to be highly sensitive to the organic components of a soil (Viscarra Rossel et al., 2006). Spectral signatures related to various components of soil organic matter (SOM) generally occur in the MIR range (2500–25,000 nm), but their overtones can be found in the VIS/NIR (400–1200 nm) and the shortwave-infrared (SWIR; 1200–2500 nm) ranges (Shepherd and Walsh, 2002). Several studies reveal that on the average MIR outperforms VIS/NIR, because MIR spectra consist of more defined peaks and thus are often described as performing better in estimating the SOC content (Chang et al., 2001, Ladoni et al., 2010, Patzold et al., 2008, Reeves, 2010, Stevens et al., 2006, Viscarra Rossel et al., 2006). Due to its increasing use and wide acceptance, MIR spectroscopy (MIRS) may be considered as a reference method, even if it's not yet an established laboratory standard. However, MIRS is to be used preferentially under laboratory conditions, while NIR spectroscopy (NIRS) can be easily applied in the field either as portable or as airborne sensors.
NIRS has been used for several years for the assessment of SOC (Dalal and Henry, 1986, Henderson et al., 1992). Ladoni et al. (2010) reviewed the satisfying ability to predict SOC via NIRS under laboratory conditions. However, the choice of spectroscopic techniques used in field studies depends mainly on the accuracy of prediction, the cost of the technology and the amount of sample preparation required (Viscarra Rossel et al., 2006). To enhance the efficacy of spectroscopic techniques for soil mapping and monitoring, airborne sensor applications are becoming more popular, though they remain in the testing phases (Patzold et al., 2008, Selige et al., 2006, Stevens et al., 2006, Stevens et al., 2008, Stevens et al., 2010). According to DeTar et al. (2008), soil properties can be accurately detected using airborne hyperspectral imaging due to its high spatial resolution and supplying a complete spectrum of data for every pixel location. Several authors successfully mapped SOC on large agricultural fields and regions via airborne hyperspectral sensors using multivariate calibration statistics (Selige et al., 2006, Stevens et al., 2006, Stevens et al., 2008, Stevens et al., 2010).
Multivariate calibrations, such as a partial least squares regression (PLSR), allow for a quantitative determination of several soil characteristics from spectral signatures (VIS, NIR, SWIR and MIR) and are a common tool used to derive soil properties from hyperspectral data (Selige et al., 2006, Stevens et al., 2010, Viscarra Rossel et al., 2006). However, airborne hyperspectral imaging still has several limitations, as reviewed by Ben-Dor et al. (2009) and Cécillon et al. (2009). These limitations can be atmospheric absorptions or sensor-based characteristics such as a low signal-to-noise ratio and a limiting spatial resolution, or can occur due to spatial and temporal soil surface conditions, such as variable moisture content, soil surface roughness, or green manure or crop residue covers. In consequence, local calibration outperforms regional calibration (Stevens et al., 2010). The airborne HyMap sensor has shown itself to be an appropriate tool used to monitor SOC at a regional scale (Selige et al., 2006). As recently reported by Ben-Dor et al. (2009), Cécillon et al. (2009) and Ladoni et al. (2010), more experience is required to accurately predict SOC via airborne hyperspectral imaging, as only few studies dealt with this topic by now. Former research focused primarily on the detection of SOC at large regional scales for entire landscapes. However, SOC can considerably vary within few metres. For effective site-specific management, which is especially required for precision agriculture, a detailed knowledge of the small-scale variability of SOC within individual agricultural fields is an essential requirement.
The objectives of this study were (i) to test the suitability of airborne hyperspectral imaging (HyMap sensor) for the characterisation of the spatial heterogeneity of the SOC content at the field-scale including (ii) investigations concerning different soil surface conditions (roughness, vegetation) and (iii) to produce SOC maps on a pixel-wise basis as required for precision agriculture.
Section snippets
Study sites
The spatial variability of the SOC content was investigated at four agricultural fields in the Lower Rhine Basin in North Rhine-Westphalia, Germany. This region is characterised by a mean annual precipitation of 600–800 mm and a mean annual temperature of approximately 10 °C. All test sites are covered by a loess layer (> 2 m) and have been under intensive and uniform cultivation for the last several decades. The distribution of SOC within the plough layer (0–30 cm) may be regarded as homogeneous
Prediction of SOC via airborne hyperspectral imaging
For the complete sample set, the SOC content ranged from 8.3 to 18.5 g SOC kg− 1 (Table 1), which is typical for arable fields in the study region (Preger et al., 2006). If data of all four test sites (n = 204) are combined, SOC can be predicted from the hyperspectral data with high accuracy. The coefficient of determination (R2 = 0.83) as well as RMSEP (1.10 g SOC kg− 1), corresponding RPD (2.32) and RPIQ (4.41) corroborates the high quality of the calibration model (Fig. 1). The modelling efficiency (EF)
Conclusions and perspectives
Airborne hyperspectral imaging is a promising tool to detect the variability of the SOC content even at a comparatively small concentration range on long-term uniformly cultivated fields at a small spatial scale with high accuracy. After the necessary local calibrations are made, more precise results can be achieved than with regional calibrations, especially with varying surface conditions. The pixel-wise prediction of SOC in field maps without the need of geostatistical treatment seems
Acknowledgements
This study has been conducted within the Research Training Group 722 ‘Information Techniques for Precision Crop Protection’, funded by the German Research Foundation (DFG). The authors are thankful to M. Güttler and M. Kasten for their great help in the laboratory and to T. Mewes and J. Franke (Centre for Remote Sensing of Land Surfaces (ZFL), University of Bonn, Germany) for organising the flight campaign and for technical assistance during the hyperspectral measurements. Gratefully
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