Michel C. Nolin

Anionic Exchange Membranes as a Soil Test for Nitrogen Availability

Abstract A better understanding of nitrogen (N) availability to crops remains an essential key for a productive and safe production system. The main objective of this study was to evaluate the potential of anionic exchange membranes (AEMs) as part of a soil‐testing procedure to predict in situ soil NO3‐N availability for forage and corn produced in eastern Canada. The AEMs were buried in the surface horizon (0–15 cm) at four experimental sites for forage and at one site for corn. Treatments consisted of five NH4NO3 rates (0, 60, 120, 180, and 240 kg N ha−1) in forage and of six anhydrous ammonia (0, 50, 100, 150, 200, and 250 kg N ha−1) in corn production. In all sites, NO3 − adsorbed on AEMs (NO3AEMs) increased significantly with N fertilizer rates, indicating the ability of the AEMs to detect differences between N fertilizer treatments and to predict the soil N availability to crops. The NO3AEMs fluxes were significantly related to soil NO3‐N concentration as extracted by water or KCl (0.66≤R2≤0.95). Significant relationships between crop N uptake and NO3AEMs were obtained (0.52≤R2≤0.94), suggesting that AEMs can be used as an index of soil N availability. Results indicated that AEMs provide a reasonably accurate evaluation of N availability to forage and corn. Because of their low cost, simplicity, and consistency over years, soils, and crops, AEMs could be efficiently used in soil N availability analysis.

Remote Sensing of Soil

Remote sensing has shown a high potential in soil characteristics retrieving in the last three decades. Different methodologies have been proposed for the estimation of soil parameters, based on different remote sensing sensors and techniques (passive and active).

Variability of seasonal CASI image data products and potential application for management zone delineation for precision agriculture

The delineation of management zones is an important step to implementing site-specific crop management practices. Remote sensing is a cost-effective way to acquire information needed for delineating management zones, since it has been successfully used for mapping soil properties and monitoring crop growth conditions. Remotely sensed hyperspectral data are particularly effective in deriving crop biophysical parameters in agricultural fields; therefore, the potential of hyperspectral data to contribute to management zone delineation needs to be assessed. In this study, the spatial variability of soil and crops in two agricultural fields was studied using seasonal compact airborne spectrographic imager (CASI) hyperspectral images. Different spectral features including soil brightness and colouration indices, principal components of soil reflectance data, and crop descriptors (leaf area index (LAI) and leaf chlorophyll content) were derived from CASI data and used to partition the fields into homogeneous zones using the fuzzy k means unsupervised classification method. The reduction of variances of soil electrical conductivity, LAI, leaf chlorophyll content, and yield was inspected to determine the appropriate number of zones for each field. The zones obtained were interpreted according to the soil survey map and field practices. Analysis of variance (ANOVA) was conducted to examine the effectiveness of the delineation. The study shows that the spatial patterns of the resulting soil zones faithfully represent the soil classes described by the soil survey maps, and the spatial patterns of the resulting crop classes discriminated the different crop growth conditions well. These results show that hyperspectral data provide important information on field variability for management zone delineation in precision agriculture.

Mapping Agricultural Frozen Soil on the Watershed Scale Using Remote Sensing Data

This paper presents an empirical model for classifying frozen/unfrozen soils in the entire Bras d’Henri River watershed (167 km2) near Quebec City (Quebec, Canada). It was developed to produce frozen soil maps under snow cover using RADARSAT-1 fine mode images and in situ data during three winters. Twelve RADARSAT-1 images were analyzed from fall 2003 to spring 2006 to discern the intra- and interannual variability of frozen soil characteristics. Regression models were developed for each soil group (parent material-drainage-soil type) and land cover to establish a threshold for frozen soil from the backscattering coefficients (HH polarization). Tilled fields showed higher backscattering signal (

Digital Mapping of Soil Drainage Classes Using Multitemporal RADARSAT-1 and ASTER Images and Soil Survey Data

Discriminant analysis classification (DAC) and decision tree classifiers (DTC) were used for digital mapping of soil drainage in the Bras-d’Henri watershed (QC, Canada) using earth observation data (RADARSAT-1 and ASTER) and soil survey dataset. Firstly, a forward stepwise selection was applied to each land use type identified by ASTER image in order to derive an optimal subset of soil drainage class predictors. The classification models were then applied to these subsets for each land use and merged to obtain a digital soil drainage map for the whole watershed. The DTC method provided better classification accuracies (29 to 92%) than the DAC method (33 to 79%) according to the land use type. A similarity measure (S) was used to compare the best digital soil drainage map (DTC) to the conventional soil drainage map. Medium to high similarities (0.6≤Sl0.9) were observed for 83% (187 km2) of the study area while 3% of the study area showed very good agreement (S≥0.9). Few soil polygons showed very weak similarities (Sl0.3). This study demonstrates the efficiency of combining radar and optical remote sensing data with a representative soil dataset for producing digital maps of soil drainage.

Caractérisation des Phases du Processus de Compostage Par Une Approche Multidimensionnelle: Application Au Cycle de L'Azote Characterization of the Composting Stages by a Multivariate Analysis: Application to the Nitrogen Cycle

Microbial composting stages were characterized by a multivariate analysis, a global approach instead of a classical univariate analysis of correlations, as part of a study of the organic nitrogen reorganization during a cow manure composting process. Two adiabatic composters were sampled at thirteen intervals during composting and nineteen physical, chemical and biological descriptors were retained for numerical analysis. A principal component analysis (PCA) brought out the most probable factors governing sample distribution. The first three axes of the PCA represented 71% of the total cumulated variance. Sample distribution was along three gradients: organic matter degradation, slow hydrolysable nitrogen, microbial activity. A cluster analysis by average linkage for each composter created five groups of days which are approximately the same size. The interpretation of these groups in terms of the evolution of the microbial composting stages utilized the superposition of clusters onto the first three axes...

Potential of C-Band Multi-polarized and Polarimetric SAR Data for Soil Drainage Classification and Mapping

Routine soil surveys determine many soil properties but soil drainage and soil moisture indicators (soil permeability, hydrologic soil group, etc.) are often the most relevant for agro-environmental purposes. They influence in-field crop yield and affect several environmental processes, i.e., soil erosion, nutrient and pesticide transport by runoff water and leaching, nitrification, and greenhouse gas production. Up-to-date information is needed to guide watershed and site-specific crop management but conventional soil survey procedures are time-consuming and expensive. New technologies, such as remote and proximal sensing, feature in many key soil science applications and are particularly effective for mapping soil drainage (Niang et al., 2007 and 2006; Bell et al., 1994 and 1992; Lee et al., 1988; Levine et al., 1994; Cialella et al., 1997; Campling et al., 2002; Liu et al., 2008). Since soil drainage is often related to other properties, such as soil water content and texture (Kravchenko et al., 2002), it can be mapped using both optical and radar remote sensing. When cloud-free optical imagery is not available, radar imaging is the best option. For most soil conditions, soil surface moisture and vegetative growth and development are considered to be indicators of soil drainage. However, these factors also affect radar backscatter. Knowledge of soil and crop types is needed to understand the relationship between radar backscatter and soil drainage (Smith et al., 2006). Soil drainage is a dynamic process that can by defined only by integrating several factors: water-holding capacity, hydraulic conductivity, and seasonal variation in water-table depth (Jennifer et al., 2001). Thus, radar remote sensing data must be acquired under appropriate environmental conditions to effectively determine soil drainage classes. Most studies on agricultural applications of radar remote sensing involve single frequency and single polarization data. Unless such data are combined in multi-temporal series, they are limited to a few values (McNairn and Brisco, 2004). Only a few studies suggest that multi-polarization radar could provide valuable and timely information by delineating homogenous soil zones (McNairn and Brisco, 2004; van der Sanden, 2004). One recent study showed that soil drainage could be mapped on an in-field scale by using high-resolution optical and C-band SAR data from 10

Object-based Classification of High Resolution SAR Images for Within Field Homogeneous Zone Delineation

Delineating management zones is important in agriculture for implementing site-specific practices. We delineated within-field homogeneous zones over a corn and a wheat field using high spatial resolution multi-temporal airborne C-band synthetic aperture radar (SAR) imagery with an object-based fuzzy k-means classification approach. Image objects were generated by a segmentation procedure implemented in eCognition® software, and were classified as basic processing units using SAR data. Results were evaluated using analysis of variance and variance reduction of soil electrical conductivity (EC), leaf area index (LAI), and crop yield. The object-based approach provided better results than a pixel-based approach. The variance reduction in LAI, and soil EC varied with SAR acquisition time and incidence angle. Although the variance reduction of yield was not as significant as that of LAI and EC, average yield among the delineated zones were different in most cases. The SAR data classification produced interpretable patterns of soil and crop spatial variability, which can be used to infer within-field management zones.

An Approach for Mapping Frozen Soil of Agricultural Land under Snow Cover using RADARSAT-1 and RADARSAT-2

A frozen soil map is a key tool to assess environmental impacts of agricultural practices on water quality, because the frost penetration in the ground has a direct impact on runoff and nutrient losses at spring melt in Eastern Canada. SAR images data have a great potential to provide this information due to there sensibility to the soil dielectric properties. The goal of this study is to develop a classification model by analyzing interactions between the different parameters resulting from in situ field data and SAR images under snow cover and to produce frozen soil maps at watershed scale. Two issues will be tackled, the mapping of frozen soil using RADARSAT-1 images and the polarimetric scattering mechanisms generated by frozen/unfrozen soil status. In this paper we present some initial results of polarimetric radar measurements using the C-band Convair-580 SAR. The analysis is addressing polarimetric signatures and entropy-alpha space distributions.

Détection du gel et non-gel du sol en utilisant le radar polarimétrique à synthèse d’ouverture

Radar polarimetry is a recent field, which offers increased detection possibilities compared with the monopolarisation radar sensors. The goal of this study is to evaluate the polarimetric data potential to monitor soil freezing in an agricultural environment. With this intention, three agricultural fields were selected, in two experimental stations of Agriculture and Agri-Food Canada: Chapais and Harlaka. Data on soil temperature at three depths (1, 5, and 15 cm), as well as certain characteristics of the snow cover (density, snow height, and temperature), were collected during polarimetric image acquisitions using polarimetric C-band synthetic aperture radar (SAR) system on board Convair-580 aircraft operated by Environment Canada. Several approaches of polarimetric treatments were explored and compared: Pauli decomposition, Cloude and Pottier decomposition, polarimetric signature, and copolarized phase difference. The results obtained show that some soil surface conditions (frozen–unfrozen) can be discriminated by radar polarimetry. Indeed, with the Cloude and Pottier decomposition, this difference appears, when the soil freezes, by a decrease of the entropy values (H) and the α angle, and an increase of the anisotropy (A). The copolarisation signatures comparison shows well the differences between the pedestal height and the signature shape in the case of a frozen soil and an unfrozen one. Even if SAR polarimetry appears as a mean to discriminate soil freezing, it seems necessary to acquire a fall image, in a period without freezing, so we can compare it with the values of the parameters obtained when the soil is frozen.