Dataset Information

Estimating salt content of vegetated soil at different depths with Sentinel-2 data.

ABSTRACT: The accurate and timely monitoring of the soil salt content (SSC) at different depths is the prerequisite for the solution to salinization in the arid and semiarid areas. Sentinel-2 has demonstrated significant superiority in SSC inversion for its higher temporal, spatial and spectral resolution, but previous research on SSC inversion with Sentinel-2 mainly focused on the unvegetated surface soil. Based on Sentinel-2 data, this study aimed to build four machine learning models at five depths (0?20 cm, 20?40 cm, 40?60 cm, 0?40 cm, and 0?60 cm) in the vegetated area, and evaluate the sensitivity of Sentinel-2 to SSC at different depths and the inversion capability of the models. Firstly, 117 soil samples were collected from Jiefangzha Irrigation Area (JIA) in Hetao Irrigation District (HID), Inner Mongolia, China during August, 2019. Then a set of independent variables (IVs, including 12 bands and 32 spectral indices) were obtained based on the Sentinel-2 data (released by the European Space Agency), and the full subset selection was used to select the optimal combination of IVs at five depths. Finally, four machine learning algorithms, back propagation neural network (BPNN), support vector machine (SVM), extreme learning machine (ELM) and random forest (RF), were used to build inversion models at each depth. The model performance was assessed using adjusted coefficient of determination (R² _adj ), root mean square error (RMSE) and mean absolute error (MAE). The results indicated that 20?40 cm was the optimal depth for SSC inversion. All the models at this depth demonstrated a good fitting (R² _adj ? 0.6) and a good control of the inversion errors (RMSE < 0.16%, MAE < 0.12%). At the depths of 40?60 cm and 0?20 cm the inversion performance showed a slight and a great decrease respectively. The sensitivity of Sentinel-2 to SSC at different depths was as follows: 20?40 cm > 40?60 cm > 0?40 cm > 0?60 cm > 0?20 cm. All four machine learning models demonstrated good inversion performance (R² _adj > 0.46). RF was the best model with high fitting and inversion accuracy. Its R² _adj at five depths were between 0.5 to 0.68. The SSC inversion capabilities of all the four models were as follows: RF model > ELM model > SVM model > BPNN model. This study can provide a reference for soil salinization monitoring in large vegetated area.

SUBMITTER: Chen Y

PROVIDER: S-EPMC7759139 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Estimating salt content of vegetated soil at different depths with Sentinel-2 data.

Chen Yinwen Y Qiu Yuanlin Y Zhang Zhitao Z Zhang Junrui J Chen Ce C Han Jia J Liu Dan D

PeerJ 20201221

The accurate and timely monitoring of the soil salt content (SSC) at different depths is the prerequisite for the solution to salinization in the arid and semiarid areas. Sentinel-2 has demonstrated significant superiority in SSC inversion for its higher temporal, spatial and spectral resolution, but previous research on SSC inversion with Sentinel-2 mainly focused on the unvegetated surface soil. Based on Sentinel-2 data, this study aimed to build four machine learning models at five depths (0∼ ...[more]

PMID: 33391883

Similar Datasets

Project description:Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Cl-, Mg2+ and SO4 2- was very high, that of CO3 2- was high and K+ was relatively lower, but HCO3 - failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision.

Project description:Numerous studies have shown that the continuous increase of atmosphere CO2 concentrations may have profound effects on the forest ecosystem and its functions. However, little is known about the response of belowground soil microbial communities under elevated atmospheric CO2 (eCO2) at different soil depth profiles in forest ecosystems. Here, we examined soil microbial communities at two soil depths (0 to 5 cm and 5 to 15 cm) after a 10-year eCO2 exposure using a high-throughput functional gene microarray (GeoChip). The results showed that eCO2 significantly shifted the compositions, including phylogenetic and functional gene structures, of soil microbial communities at both soil depths. Key functional genes, including those involved in carbon degradation and fixation, methane metabolism, denitrification, ammonification, and nitrogen fixation, were stimulated under eCO2 at both soil depths, although the stimulation effect of eCO2 on these functional markers was greater at the soil depth of 0 to 5 cm than of 5 to 15 cm. Moreover, a canonical correspondence analysis suggested that NO3-N, total nitrogen (TN), total carbon (TC), and leaf litter were significantly correlated with the composition of the whole microbial community. This study revealed a positive feedback of eCO2 in forest soil microbial communities, which may provide new insight for a further understanding of forest ecosystem responses to global CO2 increases.IMPORTANCE The concentration of atmospheric carbon dioxide (CO2) has continuously been increasing since the industrial revolution. Understanding the response of soil microbial communities to elevated atmospheric CO2 (eCO2) is important for predicting the contribution of the forest ecosystem to global atmospheric change. This study analyzed the effect of eCO2 on microbial communities at two soil depths (0 to 5 cm and 5 to 15 cm) in a forest ecosystem. Our findings suggest that the compositional and functional structures of microbial communities shifted under eCO2 at both soil depths. More functional genes involved in carbon, nitrogen, and phosphorus cycling were stimulated under eCO2 at the soil depth of 0 to 5 cm than at the depth of 5 to 15 cm.

Dataset Information

Estimating salt content of vegetated soil at different depths with Sentinel-2 data.

Publications

Estimating salt content of vegetated soil at different depths with Sentinel-2 data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets