Simulating spatial distribution of coastal soil carbon content using a comprehensive land surface factor system based on remote sensing

Soil heavy metal source apportionment and environmental differentiation study in Dulan County, Qinghai Province, using geodetector analysis

Elucidating material sources and investigating the impact of various environmental factors on material source accumulation are important for the environmental restoration of the Qinghai-Tibet Plateau. This study was conducted within the Borhan Buda Mountain Range of Dulan County, Qinghai Province, China, where 6274 surface soil samples were collected. The geoaccumulation index was employed to assess the levels of heavy metals, including As, Cr, Cu, Hg, Ni, Pb, Sb, Sn, and Zn, in the soil. A comprehensive approach combining principal component analysis (PCA) and geodetector analysis was employed to assess the spatial variation in soil heavy metal origins and the factors that influence them. The findings indicate that the mean concentrations of Pb exceed the background values for the soil in Qinghai Province, with Hg exhibiting low pollution, whereas the other elements showed no contamination. PCA indicated that the soil elements in the Borhan Buda Mountain Range were influenced by four sources, all attributed to the geological background. Geodetector analysis of the factor contributions suggested that geological factors had the strongest explanatory power for the four material sources. Furthermore, the risk detection results demonstrated significant variations in the material source contributions among most subregions when considering three environmental factors in pairs. Interaction detection revealed that the combined influence of two environmental factors on material source contributions was greater than that of the individual factors. Additionally, ecological detection demonstrated significant differences in material source contributions one, two, and three between land cover types and geological backgrounds, whereas no significant differences were observed in material source four between land cover types and geological backgrounds. This study offers valuable insights into the sources of soil elements in Dulan County and the influence of environmental factors on these sources, thereby contributing an essential knowledge base for the protection and management of soil heavy metals in the region.

Estimating soil salinity using Gaofen-2 imagery: A novel application of combined spectral and textural features

Gaofen-2 (GF-2) imagery data has been playing an important role in environmental monitoring. However, the scarcity of spectral bands makes GF-2 difficult to use in soil salinity estimation. In this paper, we combined spectral and textual features for soil salinity estimation from GF-2 imagery. The spectral features comprised five classes of predictors: spectral value, vegetation index, salinity index, brightness index, and intensity index. Four gray-level co-occurrence matrix (GLCM) indices were used as the textural features. The least absolute shrinkage and selection operator (LASSO) was applied to select features. Four methods, namely, Random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and partial least squares regression (PLSR) were applied and compared. To this end, 211 soil samples were collected in the Yellow River Delta through field investigation. The results showed that GF-2 imagery could successfully estimate soil salinity by integrating spectral and texture features, and among the four methods, the RF had the highest accuracy with the determination coefficient for cross-validation (R²_CV), a root mean square error for cross-validation (RMSE_CV), and the ratio of the standard deviation to the root mean square error of prediction (RPD) of 0.82, 2.36 g kg^-1, and 2.28, respectively. Especially, the impact of different scale features on the soil salinity estimation accuracy was evaluated. The optimal window size for features was 9 × 9 pixels, and increasing or decreasing the window size will decrease the estimation accuracy. The study provides a novel application to soil salinity estimation from remote sensing imagery.

Combining Different Transformations of Ground Hyperspectral Data with Unmanned Aerial Vehicle (UAV) Images for Anthocyanin Estimation in Tree Peony Leaves

To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra; commonly used spectral parameters were implemented to estimate Anth content using multiple stepwise regression (MSR), partial least squares (PLS), back-propagation neural network (BPNN), and random forest (RF) models. The spectral transformation highlighted the characteristics of spectral curves and improved the relationship between spectral reflectance and Anth, and the RF model based on the FD spectrum portrayed the best estimation accuracy (R2c = 0.91; R2v = 0.51). Then, the RGB (red-green-blue) gray vegetation index (VI) and the texture parameters were constructed using UAV images, and an Anth estimation model was constructed using UAV parameters. Finally, the UAV image was fused with the ground spectral data, and a multisource RS model of Anth estimation was constructed, based on PCA + UAV, FD + UAV, and CR + UAV, using MSR, PLS, BPNN, and RF methods. The RF model based on FD+UAV portrayed the best modeling and verification effect (R2c = 0.93; R2v = 0.76); compared with the FD-RF model, R2c increased only slightly, but R2v increased greatly from 0.51 to 0.76, indicating improved modeling and testing accuracy. The optimal spectral transformation for the Anth estimation of tree peony leaves was obtained, and a high-precision Anth multisource RS model was constructed. Our results can be used for the selection of ground-based HS transformation in future plant Anth estimation, and as a theoretical basis for plant growth monitoring based on ground and UAV multisource RS.

Prediction of tempo-spatial patterns and exceedance probabilities of atmospheric corrosion of Q235 carbon steel across China

To reduce the losses caused by the atmospheric corrosion of carbon steels, it is important to establish a prediction model to determine the corrosion rate of carbon steels in natural environments. In this study, a prediction model of atmospheric corrosion of Q235 carbon steel (PMACC-Q235) in China was established by coupling the mean impact value algorithm and back propagation artificial neural network. Tempo-spatial patterns of corrosion rates in five long-exposure time categories across China were analyzed. Ten main factors affecting the atmospheric corrosion of Q235 were identified. The corrosion rates in a single year were similar (approximately 30 μm/a) and larger than those for 2 (25.30 μm/a) and 3 years (21.66 μm/a). The spatial corrosion rates in the northwestern areas were primarily lower than those in southeastern coastal areas. This could be influenced by climatic factors, such as temperature, humidity, and precipitation. All corrosion rates reached the C2 level (>1.3 μm/a), and there was some possibility that they reached higher corrosion levels. The largest probability for the C3 level in all periods was an average of 0.91, and that for the C4 level was 0.83. Spatially, higher probabilities were mainly located in the southern area, especially in Hainan, located in the south and surrounded by sea. Corrosion rates largely varied among climatic zones, and mean corrosion rates in the tropical monsoon climate zone were the largest (average of three periods 33.39 μm/a). SO₂ and soluble-dust fall had the largest impact on the variations in the corrosion rates among different climatic zones.

Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data

Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0–10 cm and 10–20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes.

Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects

The use of neural network (NN) models for remote sensing (RS) retrieval of landscape biophysical and biochemical properties has become popular in the last decade. Recently, the emergence of "big data" that can be generated from remotely sensed data and innovative machine learning (ML) approaches have provided a platform for novel analytical approaches. Specifically, the advent of deep learning (DL) frameworks developed from traditional neural networks (TNN) offer unprecedented opportunities to improve the accuracy of SOC retrievals from remotely sensed imagery. This review highlights the use of TNN models and their evolution into DL architectures in remote sensing of SOC estimation. The review also highlights the application of DL, with a specific focus on its development and adoption in remote sensing of SOC mapping. The review concludes by highlighting future opportunities for the use of DL frameworks for the retrieval of SOC from remotely sensed data.

Interactive effects of natural and anthropogenic factors on heterogenetic accumulations of heavy metals in surface soils through geodetector analysis

The rapid socioeconomic development has led to severe pollution of urban soils by heavy metals. It is vital to identify and quantify the factors that affect trace-element pollution for better preventing and managing soil pollution. In this study, we collected 179 surface soil samples from Zhangzhou City in a coastal area of south China to determine the concentration of seven heavy metals (As, Cr, Cu, Hg, Ni, Pb, and Zn) and used the Nemerow Pollution Index (P_n) to estimate the level of heavy metal pollution in soils. Eighteen environmental factors, including six natural factors (e.g. soil properties, surface topography) and twelve anthropogenic factors (e.g. industry, road network, land use types and landscape pattern), were evaluated with the geodetector statistical method. The results indicate that the heavy metal contamination of soils in Zhangzhou City was highly heterogeneous. We found that the primary influencing factors for heavy metal concentrations were soil organic matter content, agriculture activities, and landscape pattern. Furthermore, the nonlinear relationship between the primary factors and their interaction factors enhanced soil contamination by the heavy metals. Among the anthropogenic factors, landscape pattern enhanced P_n the most when interacting with natural factor. In addition, the buffer zone should be considered when evaluating the effects of factors such as land use and landscape pattern, because the interactions between landscape pattern and slope aspect produce a maximum effect, accounting for 31.0% of the P_n value on the scale of 800 m. Based on this analysis, we identified the key factors of heavy metal pollution in the soils of Zhangzhou City and proposed strategic procedures for effective soil pollution prevention and treatment in the future.

Predicting spatial distribution of soil organic matter using regression approaches at the regional scale (Eastern Azerbaijan, Iran)

Soil organic matter (SOM) is one of the important factors in arid and semiarid areas, which describes the soil quality. Spatial estimation of SOM is important to understand the SOM storage and the emphasis of the SOM in the global carbon cycle and environmental issues. Mapping of SOM content can have significant uses in environmental modeling. In the current study, various methods have been evaluated for estimating the SOM content through soil samples and using auxiliary variables in the west of Eastern Azerbaijan province, Iran. In this study, support vector machine (SVM), multi-factor regression (MFR), and multi-factor weighted regression model (MWRM) approaches have been suggested for predicting and investigating the spatial distribution of SOM. In total, 155 surface soil samples (from the depth of 0 to 30 cm) were obtained. These soil samples were randomly divided into training data set (105 soil samples) and testing data set (50 samples). According to the results, SOM is affected by soil properties as well as environmental factors (normalized difference vegetation index (NDVI)). In this study, clay, silt/sand, NDVI, and soil moisture were used as auxiliary variables in the estimation of SOM. Three methods were compared to determine a suitable method for spatial estimation of SOM, and results showed that SVM has the lowest estimation error (RMSE = 0.100, MAE = 0.07, and MRE = 3.32) and highest regression coefficient (R² = 0.719) during SOM estimation. The present results show the indirect effect of elevation by controlling auxiliary variables and confirm the importance of auxiliary variables in spatial distribution patterns of SOM.

Predicting soil nutrient contents using Landsat OLI satellite images in rain-fed agricultural lands, northwest of Iran

Soil nutrients are the key factors in soil fertility, which have important roles in plant growth. Determining soil nutrient contents, including macro and micronutrients, is of crucial importance in agricultural productions. Conventional laboratory techniques for determining soil nutrients are expensive and time-consuming. This research was aimed to develop linear regression (LR) models for remote sensing of total nitrogen (TN) (mg/kg), available phosphorous (AP) (mg/kg), available potassium (AK) (mg/kg), and micronutrients such as iron (Fe) (mg/kg), manganese (Mn) (mg/kg), zinc (Zn) (mg/kg), and copper (Cu) (mg/kg) extracted by DTPA in rain-fed agricultural lands in the northwest of Iran. First, 101 soil samples were collected from 0-30 cm of these lands and analyzed for selected nutrient contents. Then a linear regression along with principal component analysis was conducted to correlate soil nutrient contents with reflectance data of different Landsat OLI bands. Finally, the spatial distributions of soil nutrients were drawn. The results showed that there were linear relationships between soil nutrient contents and standardized PC₁ (Z_PC1). The highest significant determination coefficient with an R² value of 0.46 and the least relative error (%) value of 11.97% were observed between TN and Z_PC1. The accuracy of the other LR's developed among other soil nutrient contents and remotely sensed data was relatively lower than that obtained for TN. According to the results obtained from this study, although remote sensing techniques may quickly assess soil nutrients, new techniques, technologies, and models may be needed to have a more accurate prediction of soil nutrients.

Spatiotemporal Characteristics of Vegetation Net Primary Productivity on an Intensively-Used Estuarine Alluvial Island

Net Primary Productivity (NPP) can effectively reflect the characteristics and strength of the response to external disturbances on estuarine alluvial island ecosystems, which can provide evidence for regulating human development and utilization activities and improving blue carbon capacity. However, there are a few studies on NPP of estuarine alluvial islands. We established a model based on a Carnegie–Ames–Stanford Approach (CASA) to estimate NPP on Chongming Island, a typical estuarine alluvial island, by considering the actual ecological characteristics of the island. The NPP of different land-cover types and protected areas in different years and seasons were estimated using Remote Sensing and Geographic Information System as the main tools. Correlations between NPP and Remote Sensing-based spatially heterogeneous factors were then conducted. In the last 30 years, the mean NPP of Chongming Island initially increased and then slowly decreased, while total NPP gradually increased. In 2016–2017, Chongming Island total NPP was 422.32 Gg C·a−1, and mean NPP was 287.84 g C·m−2·a−1, showing significant seasonal differences. NPP showed obvious spatial differentiation in both land-cover and protected area types, resulting from joint influences of natural and human activities. Chongming Island vegetation growth status and cover were the main factors that positively affected NPP. Soil surface humidity increased NPP, while soil salinity, surface temperature, and surface aridity were important NPP limiting factors.

Evaluation system of coastal wetland ecological vulnerability under the synergetic influence of land and sea: A case study in the Yellow River Delta, China

A comprehensive evaluation system and model of Coastal Wetland Ecological Vulnerability (CWEV) was constructed and applied to reveal spatial heterogeneity of the ecological vulnerability of the Yellow River Delta Wetland (YRDW). The results showed that the score of the ecological vulnerability (EVS) of the YRDW was 0.49, which was generally at a medium vulnerability level. The wetland area of high vulnerability was up to 943km², accounting for 35.2% of the total area, followed by the medium vulnerable area with an area of 750km², accounting for 28.1% of the total area. From the coastline perpendicularly to the land, the "seaward" gradient effect gradually decreased, the vulnerability-increasing "hydrologic connectivity" effect increased with the distance from the river channel, and the "land source influence" effect gradually decayed along with the vulnerability of population and economy gathering areas.

Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China

Accurately mapping the spatial distribution information of soil organic carbon (SOC) stocks is a key premise for soil resource management and environment protection. Rapid development of satellite remote sensing provides a great opportunity for monitoring SOC stocks at a large scale. In this study, based on 12 environmental variables of multispectral remote sensing, topography and climate and 236 soil sampling data, three different boosted regression tree (BRT) models were compared to obtain the most accurate map of SOC stocks covering the forest area of Lvshun District in the Northeast China. Four validation indexes, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Lin’s concordance correlation coefficient (LCCC) were calculated to evaluate the performance of the three models. The results showed that the full variable model performed the best, except the model using multispectral remote sensing variables. In the full variable model, the regional SOC stocks are primarily determined by multispectral remote sensing variables, followed by topographic and climatic variables, with the relative importance of variables in the model being 63%, 28%, and 9%, respectively. The average prediction results of full variables model and only multispectral remote sensing variables model were 8.99 and 9.32 kg m−2, respectively. Our results indicated that there is a strong dependence of SOC stocks on multispectral remote sensing data when forest ecosystems have dense natural vegetation. Our study suggests that the multispectral remote sensing variables should be used to map SOC stocks of forest ecosystems in our study region.

Spatial estimation of surface ozone concentrations in Quito Ecuador with remote sensing data, air pollution measurements and meteorological variables

Surface ozone is problematic to air pollution. It influences respiratory health. The air quality monitoring stations measure pollutants as surface ozone, but they are sometimes insufficient or do not have an adequate distribution for understanding the spatial distribution of pollutants in an urban area. In recent years, some projects have found a connection between remote sensing, air quality and health data. In this study, we apply an empirical land use regression (LUR) model to retrieve surface ozone in Quito. The model considers remote sensing data, air pollution measurements and meteorological variables. The objective is to use all available Landsat 8 images from 2014 and the air quality monitoring station data during the same dates of image acquisition. Nineteen input variables were considered, selecting by a stepwise regression and modelling with a partial least square (PLS) regression to avoid multicollinearity. The final surface ozone model includes ten independent variables and presents a coefficient of determination (R²) of 0.768. The model proposed help to understand the spatial concentration of surface ozone in Quito with a better spatial resolution.

A novel index for assessment of riparian strip efficiency in agricultural landscapes using high spatial resolution satellite imagery

Riparian strips are used worldwide to protect riverbanks and water quality in agricultural zones because of their numerous environmental benefits. A metric called Riparian Strip Quality Index, which is based on the percentage area of riparian vegetation, is used to evaluate their ecological condition. This index measures the potential capacity of riparian strips to filter sediments, retain pollutants, and provide shelter for terrestrial and aquatic species. This research aims to improve this metric by integrating the ability of riparian strips to intercept surface runoff, which is the major cause of water pollution and erosion in productive areas. In Canada and the Nordic countries, rapid surface drainage from snow melt and spring rains is often practiced to avoid production delays and losses. This reduces the efficiency of riparian buffer strips by promoting soil erosion due to concentrated runoff. A new proposed metric called Riparian Strip Efficiency Index (RSEI), incorporates not only land cover information, but topographic and hydrologic variables to model the intensity and spatial distribution of runoff streamflow, and the capability of riparian strips to retain sediments and pollutants. The research is performed over the La Chevrotière River Basin in the Portneuf municipality in Québec (Canada) using hydrological modeling, land cover and topographic data extracted from very high spatial resolution WorldView-2 imagery as a unique source of inputs. The results show that RSEI provides a better characterization of the ecosystem services of riparian strips in terms of pollutants filtration and prevention of soil erosion in agricultural areas. RSEI will allow a better management of agricultural practices such as drainage and land leveling. Further, it will provide to land managers information to monitor environmental changes and to prioritize intervention areas, which ultimately targets to ensure optimal allocation of private or public funds toward the most inefficient and threatened riparian strips.