An approach for broad‐scale predictive soil properties mapping in low‐relief areas based on responses to solar radiation

Abstract

In low‐relief areas, easily observed landscape features such as terrain and vegetation often do not spatially co‐vary with soil conditions to the level that they can be effectively used in predictive soil mapping. This paper proposes an approach to predicting soil spatial variation over such areas at a coarse resolution by constructing environmental covariates from the dynamic feedbacks of land surface in response to solar radiation. These feedbacks are captured by the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature observations as a time series of four temperatures per day (1:30 AM, 10:30 AM, 1:30 PM, 10:30 PM) converted into five covariates representing the features of the time series. The approach is demonstrated by mapping sand, silt, soil organic matter (SOM), and pH over a 12,000‐km2 low‐relief area of Jiangsu Province, China, using a random forest model trained on 144 soil observations. The model was built and evaluated for 13 single days. For the non‐winter days with relatively high solar radiation, the covariates were strongly correlated with the soil properties, so that their use in predictive mapping gives good results for soil texture and useful results for SOM and pH. We also present plausible physical interpretations for the action of the covariates based on thermal properties of soils. We conclude that, despite the coarse resolution of the MODIS‐derived covariates, the proposed approach is a feasible solution for predictive soil mapping over large areas of low relief and should be investigated at finer resolutions.

Enhanced Understanding of Key Soil Properties in Northern Xinjiang Using Water-Heat-Spectral Datasets Based on Bioclimatic Guidelines

“…The bioclimatic factors used in the World-Clim dataset were BIO01-BIO10, which included the annual mean temperature (BIO01), mean diurnal range (BIO02), isothermality (BIO03), temperature seasonality (BIO04), maximum temperature of the warmest month (BIO05), minimum temperature of the cold month (BIO06), temperature annual range (BIO07), mean temperature of the wettest quarter (BIO08), mean temperature of the driest quarter (BIO09), and mean temperature of the warmest quarter (BIO10) (Table 1). These factors were selected to examine the potential impact of climate on hydrothermal changes and capture feedback information through remote sensing [18,19], which could be subsequently utilized for deducing land features. Furthermore, the study considered the dry-wet transition features of the local land surface.…”

“…Liu et al [17] used Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to acquire short-term ground dynamic feedback (6-7 d) after heavy rains, constructed a suite of environmental covariates, and validated the effectiveness of these variables in discriminating soil texture patterns. Liu et al [18] constructed environmental covariates based on a dynamic feedback response to solar radiation. The covariates were derived from the time-series of temperatures acquired from MODIS at four periods (1:30 a.m., 10:30 a.m., 1:30 p.m., and 10:30 p.m.).…”

Remote sensing‐based biological and nonbiological indices for evaluating desertification in Iran: Image versus field indices

“…Therefore, the use of remote sensing data for mapping each of the indicators and indices of different models has been expanded. For example, remote sensing indices such as spectral vegetation index, soil property index, and precipitation estimation from remotely sensed information using artificial neural networks (PERSIAN) and rain se efficiency (RUE) have been widely used in vegetation (Venter, Scott, Desmet, & Hoffman, 2020), soil (Liu et al, 2020), climate (Satgé et al, 2017), and management (Del Barrio, Puigdefabregas, Sanjuan, Stellmes, & Ruiz, 2010) studies.…”