Estimation of surface soil organic matter using a ground-based active sensor and aerial imagery

Abstract

Active canopy sensors are currently being studied as a tool to assess crop N status and direct in-season N applications. The objective of this study was to use a variety of strategies to evaluate the capability of an active sensor and a wide-band aerial image to estimate surface soil organic matter (OM). Grid soil samples, active sensor reflectance and bare soil aerial images were obtained from six fields in central Nebraska before the 2007 and 2008 growing seasons. Six different strategies to predict OM were developed and tested by dividing samples randomly into calibration and validation datasets. Strategies included uniform, interpolation, universal, field-specific, intercept-adjusted and multiplelayer prediction models. By adjusting regression intercept values for each field, OM was predicted using a single sensor or image data layer. Across all fields, the uniform and universal prediction models resulted in less accurate predictions of OM than any of the other methods tested. The most accurate predictions of OM were obtained using interpolation, field-specific and intercept-adjusted strategies. Increased accuracy in mapping soil OM using an active sensor or aerial image may be achieved by acquiring the data when there is minimal surface residue or where it has been excluded from the sensor's field-ofview. Alternatively, accuracy could be increased by accounting for soil moisture content with supplementary sensors at the time of data collection, by focusing on the relationship

Relationships between Soil‐Based Management Zones and Canopy Sensing for Corn Nitrogen Management

“…Active sensor soil measurements could potentially help to develop or fine‐tune MZ delineation and be used in conjunction with on‐the‐go crop sensing. Ground‐based proximal soil sensing has been shown to predict soil properties correlated with crop productivity (Johnson et al, 2001; Roberts et al, 2011). Schepers et al (2004), as well as others (Holland and Schepers, 2010; Shanahan et al, 2008; Solari et al, 2008), suggested that the combination of MZ and in‐season crop‐canopy sensing could produce a more efficient method to optimize N application rates.…”

Effect of Moisture Content on Prediction of Organic Carbon and pH Using Visible and Near‐Infrared Spectroscopy

“…These maps are very useful for precision agriculture, where recommendations on the application of different inputs, e.g., N, P, and K fertilization and lime, can be optimized for maximum yields with the least inputs while ensuring minimum environmental impacts. In recent years, there has been growing concern about the potential environmental hazards from excessive uniform fertilizer and herbicide application rates to spatially variable landscapes (Roberts et al, 2010). This might be attributed to the fact that data on soils are often collected by traditional laboratory analytical methods, which are complex, time consuming, and expensive.…”

Corn Hybrid Growth Stage Influence on Crop Reflectance Sensing

“…In our work, reflectance measurements on bare soil have resulted in varying ISR values in the range of 0.40 to 0.52 (unpublished data, 2010), with lighter color soils giving higher ISR values. Others using this same sensor have reported bare soil measurements (expressed as ISR values) in the range of 0.48 to 0.55 (Roberts et al, 2010).…”

“…These surface soil moisture values span the range that could be expected within a field with variable soil conditions. The ability of the Crop Circle sensor to discern soil differences has been previously used to delineate variations in soil organic matter and other properties within fields to help create management zones (Roberts et al, 2010).…”