Bare Soil Fields Research Articles

Agroindustrial landscapes and white-lipped peccary habitat use in the Cerrado of Brazil

The conversion of natural areas into agricultural land has increased human-wildlife interactions, often resulting in crop damage. This study focuses on the white-lipped peccary (Tayassu pecari), which thrives in landscapes where well-preserved, naturally-vegetated public and private lands are juxtaposed with agricultural fields. We investigated the habitat use and selection of four herds of white-lipped peccaries in a landscape mosaic along the southern border of Emas National Park in the Cerrado biome of Brazil. Our findings reveal that white-lipped peccaries prefer both corn plantations and gallery forests as habitats. Through high-frequency GPS telemetry, we observed a strong tendency to turn when herds were in agricultural areas and gallery forests, indicating feeding behavior. On the other hand, they typically move in straight lines for long distances when traversing bare soil fields and native Cerrado vegetation, suggesting they are merely passing through these habitats. The extensive feeding on corn crops in agricultural fields has resulted in significant financial losses for producers, leading to a conflict between white-lipped peccaries and landowners. Understanding the movement dynamics of the species and their intensive use of both private and public lands can aid in the development of management strategies that minimize or eliminate crop losses while ensuring the survival of this vulnerable species in landscapes comprising agricultural fields and native habitats.

Assessing soil moisture levels using visible UAV imagery and machine learning models

The estimation of soil moisture (SM) as an important variable in the hydrological cycle of nature is necessary for the optimal management of water and soil resources. One of the indirect methods to estimate SM is using visible imagery with unmanned aerial vehicles (UAVs). This study aims to evaluate the potential of visible UAV imagery for estimating SM in a bare soil field in Iran. In this study, M5 tree (M5P), random forest (RF), sequential minimal optimization regression (SMOreg), and multilayer perceptron (MLP) methods have been used for SM modeling from RGB (Red, Green and Blue) bands and brightness and intensity indices of aerial imagery. Three evaluation methods were used to assess the accuracy of the models, including the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Four different indices, including difference index (DI), ratio index (RI), normalized difference index (NDI), and perpendicular index (PI), were used to estimate SM. The green and red bands pair were found to be the optimal bands for SM estimation. The findings showed that the PI index provided the most accurate SM estimates (R2 = 0.51). The RF model predicted SM most accurately among the machine learning models tested (R2 = 0.67). However, all models underestimated SM content in high-moisture areas and overestimated it in low-moisture areas, with the MLP model showing the most significant overestimation. All the indices were saturated beyond 25% SM. In general, this study highlighted the potential of aerial RGB imagery and associated indices for assessing SM levels within bare soil fields. However, it should be noted that the use of individual bands and indices alone is not sufficient to make an accurate estimate of SM.

A Semi-Empirical Anisotropy Correction Model for UAS-Based Multispectral Images of Bare Soil

The recent developments in the performance and miniaturization of uncrewed aircraft systems (UAS) and multispectral imaging sensors provide new tools for the assessment of the spatial and temporal variability of soil properties at sub-meter resolution and at relatively low costs, in comparison to traditional chemical analysis. The accuracy of multispectral data is nevertheless influenced by the anisotropic behaviour of natural surfaces, framed in the general theory of the bidirectional reflectance distribution function (BRDF). Accounting for BRDF effects in multispectral data is paramount before formulating any scientific interpretation. This study presents a semi-empirical spectral normalization methodology for UAS-based multispectral imaging datasets of bare soils to account for the effects of the BRDF, based on the application of an anisotropy factor (ANIF). A dataset of images from 15 flights over bare soil fields in the Belgian loam belt was used to calibrate a model relating the ANIF to a wide range of illumination geometry conditions by using only two angles: relative sensor-pixel-sun zenith and relative sensor-pixel-sun azimuth. The employment of ANIF-corrected images for multispectral orthomosaic generation with photogrammetric software provided spectral maps free of anisotropic-related artefacts in most cases, as assessed by several ad hoc indexes, and was also tested on an independent validation set. Most notably, the standard deviation in the measured reflectance of the same georeferenced point by different pictures decreased from 0.032 to 0.023 (p < 0.05) in the calibration dataset and from 0.037 to 0.030 in the validation dataset. The validation dataset, however, showed the presence of some systematic errors, the causes of which require further investigation.

Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands

The spatial and temporal monitoring of soil organic carbon (SOC), and other soil properties related to soil erosion, is extremely important, both from the environmental and economic perspectives. Sentinel-2 (S2) and Landsat-8 (L8) time series increase the probability to observe bare soil fields in croplands, and thus, monitor soil properties over large regions. In this regard, this work suggests an automated pixel-based approach to select only pure soil pixels in S2 and L8 time series, and to make a synthetic bare soil image (SBSI). The SBSIs and the soil properties measured in the framework of the European LUCAS survey were used to calibrate SOC, clay, and CaCO3 prediction models. The results highlight a high correlation between laboratory soil spectra and the SBSIs median spectra, especially for the SBSI obtained by a three-year S2 collection, which provides satisfactory results in terms of SOC prediction accuracy (RPD: 1.74). The comparison between S2 and L8 results demonstrated the higher capability of the S2 sensor in terms of SOC prediction accuracy, mainly due to the greater spatial resolution of the bands in the visible region. Whereas, neither S2 nor L8 could accurately predict the clay and CaCO3 content. This is because of the low spectral and spatial resolution of their SWIR bands that prevent the exploitation of the narrow spectral features related to these two soil attributes. The results of this study prove that large S2 time series can estimate and monitor SOC in croplands using an automated pixel-based approach that selects pure soil pixels and retrieves reliable synthetic soil spectra.

Delineation of Bare Soil Field Areas from Unmanned Aircraft System Imagery with the Mean Shift Unsupervised Clustering and the Random Forest Supervised Classification

The use of aerial remote sensing platforms such as Unmanned Aircraft Systems (UAS) has been proven as a cost and time effective way to perform tasks related to precision agriculture and decision making. Two machine learning (ML) algorithms have been implemented on UAS blue and red band imagery to delineate field areas and extents of various bare soil fields: the Random Forest non-parametric supervised classifier and the unsupervised non-parametric Mean Shift clustering algorithm. Both ML algorithms perform exceptionally well. The mean Area Goodness of Fit (AGoF) for bare soil areas was greater than 99% and the mean Boundary Mean Positional Error (BMPE) was lower than 0.6 m for the 11 surveyed fields. Such precisions with ML algorithms will enable an easy automated field boundary delineation based on UAS imagery. Such information is needed by growers and crop insurance agencies for an accurate determination of the crop insurance premiums.

Large-Scale, High-Resolution Mapping of Soil Aggregate Stability in Croplands Using APEX Hyperspectral Imagery

Investigations into the spatial dynamics of soil aggregate stability (AS) are urgently needed to better target areas that have undergone soil degradation. However, due to the lack of efficient alternatives to the conventional labor-intensive methods to quantify AS, detailed information on its spatial structure across scales are scarce. The objective of this study was to explore the possibility of using hyperspectral remote sensing imagery to rapidly produce a high-resolution AS map at regional scale. Airborne Prism Experiment (APEX) hyperspectral images covering an area of 230 km2 in the Belgian loam belt were used together with a local topsoil dataset. Partial least squares regression (PLSR) models were developed for three AS indexes (i.e., mean weight diameter (MWD), microaggregate and macroaggregate fractions) and soil organic carbon (SOC), and evaluated against an independent validation dataset. The prediction models were then applied to more than 700 bare soil fields for the production of high resolution (2×2 m) MWD and SOC maps. The PLSR models had a satisfactory level of accuracy for all four variables (R2 >0.5, RPD > 1.4), and the predicted maps were capable of capturing the fine-scale as well as the between-field variabilities of soil properties. Variogram analysis on the spatial structure of MWD showed a clear spatial organization at the catchment scale (range: 1.3 km) that is possibly driven by erosion-induced soil redistribution processes. Further analysis in restricted areas displayed contrasting spatial structures where spatial auto-correlation of AS was only found at field scale, thus highlighting the potential of hyperspectral remote sensing as a promising technique to investigate the spatial variability of AS across multiple scales.

Estimating evapotranspiration using METRIC model and Landsat data for better understandings of regional hydrology in the western Urmia Lake Basin

This study estimates the evapotranspiration (ET) from the western part of Urmia Lake Basin, Iran, to provide quality spatio-temporal evapotranspiration information for use in achieving regional sustainable water management. A “mapping evapotranspiration with internalized calibration” (METRIC) ET estimation model was adopted to estimate monthly ET for the period between 2014–2016 using Landsat 8 satellite imagery. Several refinements are made to adjust the model to the local environment. The refinements include estimating evaporation from the saline lake, correction of the underestimation of city surface temperature through the modification of thermal emissivity, as well as modifications of minor assumptions applied in METRIC. The estimation results for ET obtained using METRIC were compared with independent estimation of ET using the FAO-56 approach in order to assess the estimation accuracy. The comparison showed good correlation between the two types of estimation results for irrigated agriculture, implying successful estimation of ET in this region. ET estimated in non-irrigated bare soil fields was prone to overestimation. The novelty of this study lies in the fact that this is the first ET data for this basin with 100 m spatio-temporal resolution and accuracy information. Therefore, the estimation procedure and results are expected to contribute to a better understanding of the regional hydrology. This is necessary both for the restoration of Urmia Lake and for achieving sustainable water management in the region.

Modeling of runoff water and runoff pesticide concentrations in upland bare soil using improved SPEC model.

The SPEC model (Predicted Environmental Concentrations in agricultural Soils) was developed and improved for the simulation of pesticide runoff. The model was applied to the Sakaecho upland bare soil field (Tokyo, Japan) to predict runoff water, sediment concentration in runoff water, pesticide concentrations in runoff water, and runoff sediment (clothianidin and imidacloprid) under artificial rainfall conditions. The results showed that the simulated time to first runoff agreed very well with the observed data. The simulated cumulative runoff, sediment yield, and imidacloprid concentration in sediment agreed well with the observed data (Nash-Sutcliffe Efficiency (NSE)>0.75). The simulated runoff rate agreed reasonably well with the observed data (NSE >0.5). The predicted clothianidin concentrations in sediment and in runoff water had acceptable agreement with the observed data (NSE >0). The results implied the model's potential to predict runoff water, sediment yield, and pesticide runoff.

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Soil moisture (SM) is a highly relevant variable for agriculture, the emergence of floods and a key variable in the global energy and water cycle. In the last years, several satellite missions have been launched especially to derive large-scale products of the SM dynamics on the Earth. However, in situ validation data are often scarce. We developed a new method to retrieve SM of bare soil from measurements of low-cost GPS (Global Positioning System) sensors that receive the freely available GPS L1-band signals. The experimental setup of three GPS sensors was installed at a bare soil field at the German Weather Service (DWD) in Munich for almost 1.5 years. Two GPS antennas were installed within the soil column at a depth of 10 cm and one above the soil. SM was successfully retrieved based on GPS signal strength losses through the integral soil volume. The results show high agreement with measured and modelled SM validation data. Due to its non-destructive, cheap and low power setup, GPS sensor networks could also be used for potential applications in remote areas, aiming to serve as satellite validation data and to support the fields of agriculture, water supply, flood forecasting and climate change.

An Integrated Surface Parameter Inversion Scheme Over Agricultural Fields at Early Growing Stages by Means of C-Band Polarimetric RADARSAT-2 Imagery

Many research studies have investigated surface parameter inversion for bare soils. This paper attempts to take into account the agricultural fields with crop residues and fields under low vegetation cover in addition to bare soil fields. An integrated surface parameter inversion scheme (ISPIS) is proposed to invert surface parameters in these agricultural fields based on the analysis of H-α parameters at the early crop growing stages, in which the calibrated integral equation model (CIEM) is adopted to invert surface parameters for bare soils, and an adaptive two-component decomposition combined with the CIEM and a simplified adaptive volume scattering model is developed for fields with crop residues and under low vegetation cover. Fully polarimetric RADARSAT-2 data with ground truth collected on April 29 and May 9 in 2013 and from May to June in 2014 are used for validation. Compared with other methods, the derived volumetric soil moisture (MV) and surface roughness (KS) of all agricultural fields are consistent with verifiable observations with the lowest overall root mean square error: 6.12 [vol.%] and 0.48, respectively, when all sample sites are considered.

Remote sensing for mapping soil moisture and drainage potential in semi-arid regions: Applications to the Campidano plain of Sardinia, Italy

The aim of this study is to investigate the potential of radar (ENVISAT ASAR and RADARSAT-2) and LANDSAT data to generate reliable soil moisture maps to support water management and agricultural practice in Mediterranean regions, particularly during dry seasons. The study is based on extensive field surveys conducted from 2005 to 2009 in the Campidano plain of Sardinia, Italy. A total of 12 small bare soil fields were sampled for moisture, surface roughness, and texture values. From field scale analysis with ENVISAT ASAR (C-band, VV polarized, descending mode, incidence angle from 15.0° to 31.4°), an empirical model for estimating bare soil moisture was established, with a coefficient of determination (R2) of 0.85. LANDSAT TM5 images were also used for soil moisture estimation using the TVX slope (temperature/vegetation index), and in this case the best linear relationship had an R2 of 0.81. A cross-validation on the two empirical models demonstrated the potential of C-band SAR data for estimation of surface moisture, with and R2 of 0.76 (bias +0.3% and RMSE 7%) for ENVISAT ASAR and 0.54 (bias +1.3% and RMSE 5%) for LANDSAT TM5. The two models developed at plot level were then applied over the Campidano plain and assessed via multitemporal and spatial analyses, in the latter case against soil permeability data from a pedological map of Sardinia. Encouraging estimated soil moisture (ESM) maps were obtained for the SAR-based model, whereas the LANDSAT-based model would require a better field data set for validation, including ground data collected on vegetated fields. ESM maps showed sensitivity to soil drainage qualities or drainage potential, which could be useful in irrigation management and other agricultural applications.

An Improvement of the Radiative Transfer Model Component of a Land Data Assimilation System and Its Validation on Different Land Characteristics

The paper reports the recent progress in the radiative transfer model (RTM) development, which serves as the observation operator of a Land Data Assimilation System (LDAS), and its validation at two Planetary Boundary Layer (PBL) stations with different weather and land cover conditions: Wenjiang station of humid and cropped field and Gaize station of arid and bare soil field. In situ observed micrometeorological data were used as the driven data of LDAS, in which AMSR-E brightness temperatures (TB) were assimilated into a land surface model (LSM). Near surface soil moisture content output from LDAS, together with the one simulated by a LSM with default parameters, were compared to the in-situ soil moisture observation. The comparison results successfully validated the capability of LDAS with new RTM to simulate near surface soil moisture at various environments, supporting that LDAS can generally simulate soil moisture with a reasonable accuracy for both humid vegetated fields and arid bare soil fields while the LSM overestimates near surface soil moisture for humid vegetated fields and underestimates soil moisture for arid bare soil fields.

Advantages of pre-harvest temporal flooding in a catch crop field in relation to soil moisture and nutrient salt removal by root uptake

Catch crop cultivation coupled with subsequent flood activity is an environmental friendly method of removing nutrient salts from soil in greenhouse. However, in comparison with the usual fallow period in greenhouse horticulture in Japan, a longer time is required for cultivation and soil drying after flooding. To minimize such time while retaining catch crop performance, temporal flooding was conducted in an experimental catch crop field of corn before harvest (i.e., pre-harvest temporal flooding), when crops were growing well and most nutrient salts within the soil had been taken up by the roots. Results showed that pre-harvest temporal flooding enhanced crop growth and stomatal opening; hence, evapotranspiration (mostly transpiration) was increased to a high value (3.5 times that of bare soil plot in greenhouse). Therefore, compared with the bare soil field, there was a remarkable pronounced decrease in the soil water content due to evapotranspirational water loss in the catch crop field after temporal flooding. Furthermore, the total nutrient (nitrogen) uptake by crops was also significantly accelerated in relation to pre-harvest flooding owing to the increase in crop growth. It was also found that electrical conductivity and nitrate nitrogen concentration of soil solution (at a soil-water ratio of 1:5) decreased with time owing to root uptake, and were at a fairly low level when pre-harvest flooding was conducted. These results suggest that pre-harvest temporal flooding shortens the implementation time by accelerating soil drying, and increases salt removal by root uptake; thus, this method delivers considerable advantages for practical use in catch crop cultivation.

Assimilation of surface soil moisture into a multilayer soil model: design and evaluation at local scale

Abstract. Land surface models (LSM) have improved considerably in the last two decades. In this study, the Interactions between Surface, Biosphere, and Atmosphere (ISBA) LSM soil diffusion scheme is used (with 11 soil layers represented). A simplified extended Kalman filter (SEKF) allows ground observations of surface soil moisture (SSM) to be assimilated in the multilayer LSM in order to constrain deep soil moisture. In parallel, the same simulations are performed using the ISBA LSM with 2 soil layers (a thin surface layer and a bulk reservoir). Simulations are performed over a 3 yr period (2003–2005) for a bare soil field in southwestern France, at the SMOSREX (Surface Monitoring Of the Soil Reservoir Experiment) site. Analyzed soil moisture values correlate better with soil moisture observations when the ISBA LSM soil diffusion scheme is used. The Kalman gain is greater from the surface to 45 cm than below this limit. For dry periods, corrections introduced by the assimilation scheme mainly affect the first 15 cm of soil whereas weaker corrections impact the total soil column for wet periods. Such seasonal corrections cannot be described by the two-layer ISBA LSM. Sensitivity studies performed with the multilayer LSM show improved results when SSM (0–6 cm) is assimilated into the second layer (1–5 cm) than into the first layer (0–1 cm). The introduction of vertical correlations in the background error covariance matrix is also encouraging. Using a yearly cumulative distribution function (CDF)-matching scheme for bias correction instead of matching over the three years permits the seasonal variability of the soil moisture content to be better transcribed. An assimilation experiment has also been performed by forcing ISBA-DF (diffusion scheme) with a local forcing, setting precipitation to zero. This experiment shows the benefit of the SSM assimilation for correcting inaccurate atmospheric forcing.

An automated soil line identification method using relevance vector machine

The soil line is an important concept that describes the linear relationship between reflectance of bare soils in the near-infrared (NIR) and red (R) spectral bands. Bare soil line parameters (slope and intercept) are used in calculating several vegetation indices. Previous studies have proposed both manual and empirical procedures in estimating the bare soil parameters. Manual procedures introduce some amount of subjectivity in identifying the soil line. Empirical methods often suffer because of variations caused by soil type, moisture, and organic matter contents. The existence of non-bare soil pixels also affects these procedures. In this study, we proposed an automated supervised learning algorithm using relevance vector machine (RVM) for extracting the soil line from Landsat images. The 10-fold cross validation (10-fold CV) indicated 92% accuracy for distinguishing bare soil and other non-bare soil pixels from an image. The area under the receiver operating characteristic (ROC) curve reached a value of 0.98, indicating a significant predicting power of the proposed procedure. Additionally, this procedure was evaluated using data from 10 bare soil fields in the Texas High Plains region in 2008 and 2009. Statistical analysis indicated no significant difference between the observed and estimated bare soil line parameters. The proposed RVM-based procedure successfully incorporated machine-learning algorithms into agricultural remote sensing and eliminated the dependency on empiricism and minimized subjectivity.

Benchmarking of L-band soil microwave emission models

A first step before assimilating Soil Moisture and Ocean Salinity (SMOS) L-band brightness temperatures (Tb) over land is to couple land surface models (LSMs) to microwave emission models. In this study, the Interactions between Soil Biosphere and Atmosphere (ISBA) LSM is coupled to the Community Microwave Emission Modelling platform (CMEM). Simulations of Tb are performed over a 3-yr period (2003–2005) for a bare soil field in southwestern France, at the Surface Monitoring Of the Soil Reservoir EXperiment (SMOSREX) experimental site. Both ISBA and CMEM present several options for the representation of the soil moisture and soil temperature profiles. Simplified 2-layer simulations are compared with more detailed multilayer simulations. In the 2-layer simulations, the soil is divided into two layers (a thin surface layer and a bulk reservoir), and Fresnel laws are used in CMEM to model the smooth surface emissivity. In the multilayer simulations, the ISBA soil diffusion scheme is used (with 11 soil layers represented) together with either the Wilheit or Fresnel option of CMEM. The Tb simulations are compared to the Tb ground observations available for the SMOSREX site, at H and V polarizations and at different angles, and the impact of soil roughness is assessed. It is shown that taking surface soil moisture into account in the calculation of soil roughness improves the representation of the seasonal cycle and increases the correlation for all the versions of CMEM. The Tb derived from the most complex multilayer simulations correlate slightly better to the observations than the Tb derived from the 2-layer model (r=0.84 and r=0.82, respectively, for the pooled dataset). This is partly due to a better representation of the soil moisture profile. Finally, the multilayer model is used to investigate the L-band sampling depth for contrasting soil texture profiles. For a variety of soil textures, it is found that Tb are mainly driven by the top 15cm soil layer.

Potential of hyperspectral remote sensing for field scale soil mapping and precision agriculture applications

Mapping within-field variation in soil properties opens up the possibility of employing variable agronomic management and precision farming technologies with potential environmental and economic benefits. However, the excessive cost of systematic direct soil sampling severely constrains the practical feasibility of site specific management based on soil variability information. Remote sensing offers a cost effective and efficient means for gathering a great deal of information on soil properties. The aim of the present work was to assess the potential of satellite hyperspectral imagery for the mapping of soil properties and the tilled layer of agricultural fields, in the context of precision agriculture applications. CHRIS-PROBA satellite images were acquired over two bare soil fields and their capability to provide estimates of soil texture and soil organic matter (SOM) at the field scale was assessed. Partial least squares regression (PLSR) models were developed on datasets spatially independent from those used for validation. Clay and sand could be estimated with intermediate accuracy, with values of RPD (ratio of performance to deviation) higher than 1.4. Root mean squared error (RMSE) values of 3.7 and 5.2 were obtained for clay in the two fields respectively. SOM estimates were not satisfactory, probably because of the limited range of spatial variation in the studied fields. Maps of uniform soil zones were obtained from measured and estimates soil texture data by means of fuzzy c-means classification. The resulting maps were then used for the parameterization of a simple water balance model, i.e. CropWat8.0, in order to simulate and compare uniform and variable-rate irrigation strategies. Simulation results suggest that site-specific irrigation allows to reduce significantly water losses by deep percolation, which occur when irrigation scheduling and volumes are calculated on the basis of average field soil properties. The present paper demonstrates the usefulness of satellite hyperspectral data for mapping soil spatial variability at the field scale, providing useful information for precision agriculture applications.

Colour choice behaviour in the pollen beetle Meligethes aeneus (Coleoptera: Nitidulidae)

The pollen beetle Meligethes aeneus Fabricius (Coleoptera, Nitidulidae), a pest of oilseed rape (Brassica napus), is known to respond to coloured stimuli; however, current understanding of the underlying mechanisms of colour choice in this species is limited. In the present study, physiological and behavioural experiments are conducted to determine the response of the pollen beetle to colours in the field. Spectral sensitivity is measured in 10 animals using the electroretinogram technique. Light flashes (100 ms) at varied wavelengths (340–650 nm, 10‐nm steps) and at different light intensities are applied to the eye after dark adaptation. In behavioural experiments in the field, 100 water traps of varying colours (from yellow to green to blue with varying amounts of white and black added, and with known spectral reflectance) are set out on a bare soil field in May 2008. The mean spectral sensitivity curve of M. aeneus peaks at 520 nm; however, a model template fitted to the long wavelength tail of the observed curve reveals a peak at approximately 540 nm (green). A secondary sensitivity peak is observed in the ultraviolet (UV) range (370 nm). A total of 2482 pollen beetles are captured in the coloured traps. The results show that the pollen beetles' preference for yellow over other colours can be modelled as a colour opponent mechanism (green versus blue); however, further experiments are needed to specify responses to colours with higher UV reflectance. These findings may be used to optimize trap colours for monitoring to help develop integrated pest management strategies for pollen beetle control.

Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis–NIR data

The potential of the visible–near infrared (Vis–NIR; 400–2500nm) laboratory spectroscopy for the estimation of soil properties has been previously demonstrated in the literature, and the Vis–NIR spatial spectroscopy is expected to provide direct estimates of these properties at the soil surface. The aim of this work was to examine whether Vis–NIR airborne spectroscopy could be used for mapping eight of the most common soil properties, including clay, sand, silt, calcium carbonate (CaCO3), free iron, cation-exchange capacity (CEC), organic carbon and pH, without mispredicting the local values of these properties and their spatial structures. Our study was based on 95 soil samples and a HyMap hyperspectral image available over 192 bare soil fields scattered within a 24.6km² area. Predictions of soil properties from HyMap spectra were computed for the eight soil properties using partial least squares regression (PLSR). The results showed that 1) four out of the eight soil properties (CaCO3, iron, clay and CEC) were suitable for mapping using hyperspectral data, and both accurate local predictions and good representations of spatial structures were observed and 2) the application of prediction models using hyperspectral data over the study area provided statistical characterizations within soilscape variations and variograms that describe in details the short range soil variations. All results were consistent with the previous pedological knowledge of the studied region. This study opens up the possibility of more extensive use of hyperspectral data for digital soil mapping of these successfully predicted soil properties.

Analyzing spatiotemporal variability of heterotrophic soil respiration at the field scale using orthogonal functions

Soil CO2 efflux was measured with a closed chamber system along a 180m transect on a bare soil field characterized by a gentle slope and a gradient in soil properties at 28days within a year. Principal component analysis (PCA) was used to extract the most important patterns (empirical orthogonal functions, EOFs) of the underlying spatiotemporal variability in CO2 efflux. These patterns were analyzed with respect to their geostatistical properties, their relation to soil parameters obtained from laboratory analysis, and the relation of their loading time series to temporal variability of soil temperature and moisture. A particular focus was set on the analysis of the overfitting behaviour of two statistical models describing the spatiotemporal efflux variability: i) a multiple regression model using the k first EOFs of soil properties to predict the n first EOFs of efflux, which were then used to obtain a prediction of efflux on all days and points; and ii) a modified multiple regression model based on re-sorting of the EOFs based on their expected predictive power. It was demonstrated that PCA helped to separate meaningful spatial correlation patterns and unexplained variability in datasets of soil CO2 efflux measurements. The two PCA analyses suggested that only about half of the total variance of efflux could be related to field-scale spatial variability of soil properties, while the other half was “noise” attributed to temporal fluctuations on the minute time scale and short-range spatial heterogeneity on the decimetre scale. The most important spatial pattern in CO2 efflux was clearly related to soil moisture and the driving soil physical properties. Temperature, on the other hand, was the most important factor controlling the temporal variability of the spatial average of soil respiration.