Modeling the Spatial Dynamics of Soil Organic Carbon Using Remotely-Sensed Predictors in Fuzhou City, China

Abstract

Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit of an optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the random forest (RF) and classification and regression tree (CART). The results indicated that the RF model has good prediction performance with corresponding R2 and root-mean-square error (RMSE) values of 0.96 and 0.91 mg·g−1, respectively. The distribution of SOC content showed variability across landforms (CV = 78.67%), land use (CV = 93%), and lithology (CV = 64.67%). Forestland had the highest SOC (13.60 mg·g−1) followed by agriculture (10.43 mg·g−1), urban (9.74 mg·g−1), and water body (4.55 mg·g−1) land uses. Furthermore, soils developed in bauxite and laterite lithology had the highest SOC content (14.69 mg·g−1). The SOC content was remarkably lower in soils developed in sandstones; however, the values obtained in soils from the rest of the lithologies could not be significantly differentiated. The mean SOC concentration was 11.70 mg·g−1, where the majority of soils in the study area were classified as highly humus and extremely humus. The soils with the highest SOC content (extremely humus) were distributed in the mountainous regions of the study area. The biophysical land surface indices, brightness removed vegetation indices, topographic indices, and soil spectral bands were the most influential predictors of SOC in the study area. The spatial variability of SOC may be influenced by landform, land use, and lithology of the study area. Remotely sensed predictors including land moisture, land surface temperature, and built-up indices added valuable information for the prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm subtropical urban regions.

Synergistic Use of Multi-Temporal Radar and Optical Remote Sensing for Soil Organic Carbon Prediction

“…Similarly, SOC affects soil physical and chemical properties, including color, texture, and moisture retention capacity. These properties can influence soil reflectance characteristics in different spectral bands, including green and SWIR bands, and may indirectly highlight the importance of soil moisture parameters in SOC prediction [57][58][59], as moisture-rich environments can facilitate the preservation and accumulation of organic carbon in soil [1,60,61]. Furthermore, our results align with those of Lu et al [62], who highlighted the importance of MNDWI alongside other soil moisture indices such as the Topographic Wetness Index (TWI) for SOC prediction.…”

Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions

“…Cluster #3 (C3) and Cluster #4 (C4) are within the emerging or declining themes. Some of the recent articles in C3 discuss mapping land suitability (Carlier et al, 2021), soil prediction using AI (Nguyen et al, 2022), and modeling spatial dynamics (Sodango et al, 2021). While some articles in C4 are of emerging nature, including the combined use of machine learning algorithms and UAV (Ndlovu et al, 2021;Onishi and Ise, 2021;Qiu et al, 2021), machine vision yield monitoring (Dolata et al, 2021), artificial neural networks for crop evapotranspiration estimation (Gao et al, 2021), and usage of machine learning for crop prediction (Almeida et al, 2021).…”

Spatial Pattern Analysis of a Water-Related Ecosystem Service and Evaluation of the Grassland-Carrying Capacity of the Heihe River Basin under Land Use Change

“…The ecological environment of arid inland river basins is worsening in northwest China, including the case of HRB [17], concentrated in the lower reaches of the river basin, where forest vegetation and grassland vegetation have fallen sharply, soil erosion has increased, livestock overload has intensified, land desertification has increased, and pollution problems have become prominent. Such a common feature of the deterioration of the ecological environment has always been the focus of attention [18]. The stable development of the economy and society in arid regions has become a key factor in determining the construction of an ecological civilization and the high-quality development of the region [19].…”