Digital mapping of soil texture classes for efficient land management in the Piedmont plain of Iran

Abstract

Accurate prediction of digital soil maps allows for the evaluation of larger areas with respect to the design of efficient land management plans at the regional scale. Nowadays, there is an increasing demand for high spatial resolution‐gridded soil data for crop planning and management because it saves time and costs. One of the most essential soil physical properties affecting water holding capacity, nutrient availability and crop growth is soil texture. It exhibits a high spatial variability, but accurate maps for larger scales are lacking. The aim of this research was to produce gridded maps of soil texture fractions (clay, silt and sand) using regression‐based approaches and to establish soil texture classes using classification‐based techniques for the semi‐arid Piedmont plain of Iran. To this end, a digital elevation model and derived topographic indices, vegetation and soil‐based indices generated from 4‐year timeseries of remote sensing products of Landsat 8 OLI were used as covariates. The decision tree (linear) and its improved version of random forest (nonlinear) algorithms were used for both the regression and classification analysis. For both algorithms, the topography‐based indices and remotely sensed products were the most important predictors for the soil particle fractions. For the estimation of the different textural classes with multiple algorithms, we recorded a moderate overall accuracy rate of 54% and a Kappa coefficient of 17% for the validation datasets. It was observed that the nonlinear classification method of the random forest was more effective, and this was also the case for the regression modelling. In general, the random forest algorithm produced a more useful gridded map to help to design regional management plans based on soil properties.

Digital assessments of soil organic carbon storage using digital maps provided by static and dynamic environmental covariates

“…The effectiveness of NDVI time series data extracted from Landsat‐8 for SOC mapping was reported by Zhang, Guo, et al (2019). Similar research was conducted in predicting soil particle fractions using a 4‐year time series RS data (Keshavarzi et al, 2022). Furthermore, combination of Landsat‐8 OLI and Sentinel‐2 MSI data for initial assessment of SOC may be an interesting topic (Velazquez et al, 2022).…”

Effects of sample mass and suspension salinity on particle size distribution in predominantly medium to heavy textured soils using the hydrometer method

“…Thus, 10 g may not be a representative sample mass as it may be overestimating the clay %. This finding is quite notable as many soil management decisions are often based on soil texture (Keshavarzi et al., 2022). We observed in all cases no difference between clay % recovery for 20 and 30 g. This suggests 20–30 g might be a suitable initial mass which aligns with the recommendations of the standard method for PSD analysis (Gee & Or, 2002).…”

Optimization and reflexivity in interdisciplinary agri-environmental scholarship

“…It also provides a complementary framework for digital soil mapping (Smith, 2020), i.e., "the creation, and population of spatial soil information systems by the use of field and laboratory observational methods coupled with spatial and non-spatial soil inference systems (Lagacherie and McBratney, 2006)." Active training and use of these tools is of increasing importance, as they take advantage of recent advances in machine learning, satellite imagery, precision agriculture, and other adjacent fields to improve analytical accuracy (see Smith, 2020;Kaya et al, 2022;Keshavarzi et al, 2022). Technical skills alone, however, are not enough to address global socioeconomic and environmental challenges.…”