Remote- and Ground-Based Sensor Techniques to Map Soil Properties

Abstract

Farm managers are becoming increasingly aware of the spatial variability in crop production with the growing availability of yield monitors. Often this variability can be related to differences in soil properties (e.g., texture, organic matter, salinity levels, and nutrient status) within the field. To develop management approaches to address this variability, high spatial resolution soil property maps are often needed. Some soil properties have been related directly to a soil spectral response, or inferred based on remotely sensed measurements of crop canopies, including soil texture, nitrogen level, organic matter content, and salinity status. While many studies have obtained promising results, several interfering factors can limit approaches solely based on spectral response, including tillage conditions and crop residue. A number of different ground-based sensors have been used to rapidly assess soil properties "on the go" (e.g., sensor mounted on a tractor and data mapped with coincident position information) and the data from these sensors compliment image-based data. On-the-go sensors have been developed to rapidly map soil organic matter content, electrical conductivity, nitrate content, and compaction. Model and statistical methods show promise to integrate these groundand image-based data sources to maximize the information from each source for soil property mapping.

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

“…In addition, SWIR bands (i.e., Bands 4, 7 and 8) were found suitable for predicting OM. These results are close to the results that were reported by Barnes et al [89]. In their study, they found wavebands between 425 nm and 695 nm to have a strong correlation with OM content, if soils had the same parental material, whereas the middle infrared region should be examined, if the soils were from different parental material.…”

Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

“…However, by the launch of the Landsat series of satellites in the 1970s and the subsequent availability of recurring landscape imagery and spectral reflectance characteristics, remote sensing for agriculture has truly emerged. It has been used for a wide variety of agricultural applications, such as crop yield estimation (Idso et al 1980;Hank et al 2015;Lobell et al 2015), biomass monitoring (Lu 2006;Ahamed et al 2011), soil parameter derivation (Barnes et al 2003;Ge et al 2011) and many others (Moran et al 1997;Atzberger 2013;Mulla 2013). A considerable number of studies related to crop growth and yield are based on satellite images within one growing season (Ren et al 2007;Song et al 2009).…”

Addressing Groundwater Declines with Precision Agriculture: An Economic Comparison of Monitoring Methods for Variable-Rate Irrigation

“…Hedley and Yule [6] demonstrated the potential to reduce irrigation water use by using soil-water balance to inform variable-rate irrigation of pre-mapped management zones at two sites in New Zealand. Stone et al [16] demonstrated the potential of similar methods in the humid Eastern U.S. Soil-based methods are thought to be superior because they more directly measure water-related stress as a function of plant available water content, whereas it can be more difficult with plant-based methods to separate the effect of the soil moisture property from that of other possible stresses such as nutrient deficiency [17,18]. Soil water measurements using soil moisture sensors, however, are only accurate for a small area, and soil water content is highly variable spatially and temporally [19].…”