Developing geographic weighted regression (GWR) technique for monitoring soil salinity using sentinel-2 multispectral imagery

Taghadosi, Mohammad Mahdi; Hasanlou, Mahdi

doi:10.1007/s12665-020-09345-0

Cited by 14 publications

(8 citation statements)

References 35 publications

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“…Traditional methods to analyze EC are very accurate yet time-consuming, discontinuous, and costly. In the last two decades, remote sensing has been widely used to determine and monitor soil salinity characteristics at different scales (Taghadosi and Hasanlou, 2021;Dale et al, 1986;Dwivedi, 2001;Santra et al, 2015). Multi-spectral data, such as QuickBird, IKONOS, SPOT, Landsat, and Sentinel, are useful in identifying and monitoring soil salinity and environmental * Corresponding author hazards (Farifteh, 2007;Koshal, 2012;Ranjbar et al, 2021;Teggi et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Models for Soil Salinity Estimation Using Multi-Spectral Earth Observation Data

Zarei

Hasanlou

Mahdianpari

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Soil salinity, a significant environmental indicator, is considered one of the leading causes of land degradation, especially in arid and semi-arid regions. In many cases, this major threat leads to loss of arable land, reduces crop productivity, groundwater resources loss, increases economic costs for soil management, and ultimately increases the probability of soil erosion. Monitoring soil salinity distribution and degree of salinity and mapping the electrical conductivity (EC) using remote sensing techniques are crucial for land use management. Salt-effected soil is a predominant phenomenon in the Eshtehard Salt Lake located in Alborz, Iran. In this study, the potential of Sentinel-2 imagery was investigated for mapping and monitoring soil salinity. According to the satellite's pass, different salt properties were measured for 197 soil samples in the field data study. Therefore several spectral features, such as satellite band reflectance, salinity indices, and vegetation indices, were extracted from Sentinel-2 imagery. To build an optimum machine learning regression model for soil salinity estimation, three different regression models, including Gradient Boost Machine (GBM), Extreme Gradient Boost (XGBoost), and Random Forest (RF), were used. The XGBoostmethod outperformed GBM and RF with the coefficient of determination (R2) more than 76%, Root Mean Square Error (RMSE) about 0.84 dS m−1, and Normalized Root Mean Square Error (NRMSE) about 0.33 dS m−1. The results demonstrated that the integration of remote sensing data, field data, and using an appropriate machine learning model could provide high-precision salinity maps to monitor soil salinity as an environmental problem.

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Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Models for Soil Salinity Estimation Using Multi-Spectral Earth Observation Data

Zarei

Hasanlou

Mahdianpari

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…NDVI is known as an efficient index for density of vegetation covers. The land use/cover (LULC) map was derived from the Landsat satellite images obtained as part of our earlier research on monitoring the LULC changes in the ULB from 1990-2020 using an integrated approach of object-based image analysis and deep learning techniques [31,38,39]. We used 150 ground control data points and their respective laboratory analysis to validate the results.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…A review of the research literature indicated that earlier studies applied several methods for SMDM, such as the geostatistical visible and near-infrared spectroscopy index [7], multiple linear and random forest regression models [7], cubist and partial least square regression and electrical conductivity [34], ordinary kriging combined with back-propagation network [35], machine learning and particularly deep techniques [8,[35][36][37][38], the geographic weighted regression technique [39], deep neural network regression [38], and integrated fuzzy object-based image analysis [22]. In light of the global issues of soil salinization and soil degradation and under consideration of the recent advancements in earth observation technologies, including satellite images with improved spatial, spectral, and temporal resolution, and GIS methods (e.g., machine learning and advanced decision rules), there is a need to examine the efficiency of the different data-driven methods and decision rules for soil studies and, particularly, SMDM.…”

Section: Introductionmentioning

confidence: 99%

“…In light of the global issues of soil salinization and soil degradation and under consideration of the recent advancements in earth observation technologies, including satellite images with improved spatial, spectral, and temporal resolution, and GIS methods (e.g., machine learning and advanced decision rules), there is a need to examine the efficiency of the different data-driven methods and decision rules for soil studies and, particularly, SMDM. Several methods have been applied by earlier studies [33][34][35][36][37][38][39]. However, the efficiency of different methods has not been directly compared, nor a comprehensive, integrated approach proposed for monitoring and modeling the soil degradation risk.…”

Section: Introductionmentioning

confidence: 99%

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Impacts of the Urmia Lake Drought on Soil Salinity and Degradation Risk: An Integrated Geoinformatics Analysis and Monitoring Approach

et al. 2022

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Recent improvements in earth observation technologies and Geographical Information System (GIS) based spatial analysis methods require us to examine the efficiency of the different data-driven methods and decision rules for soil salinity monitoring and degradation mapping. The main objective of this study was to analyze the environmental impacts of the Lake Urmia drought on soil salinity and degradation risk in the plains surrounding the hyper-saline lake. We monitored the impacts of the lake drought on soil salinity by applying spatiotemporal indices to time-series satellite images (1990–2020) in Google Earth Engine environment. We also computed the soil salinity ratio to validate the results and determine the most efficient soil salinity monitoring techniques. We then mapped the soil degradation risk based on GIS spatial decision-making methods. Our results indicated that the Urmia Lake drought is leading to the formation of extensive salt lands, which impact the fertility of the farmlands. The land affected by soil salinity has increased from 2.86% in 1990 to 16.68% in 2020. The combined spectral response index, with a performance of 0.95, was the most efficient image processing method to assess soil salinity. The soil degradation risk map showed that 38.45% of the study area has a high or very high risk of degradation, which is a significant threat to food production. This study presents an integrated geoinformation approach for time-series soil salinity monitoring and degradation risk mapping that supports future studies by comparing the efficiency of different methods as state of the art. From a practical perspective, the results also provide key information for decision-makers, authorities, and local stakeholders in their efforts to mitigate the environmental impacts of lake drought and sustain the food production to sustain the 7.3 million residents.

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“…Some commonly used bandwidth determination methods include cross-validation (CV), the Akaike criterion (AIC) method and the Bayesian information criterion (BIC) method. In this study, the commonly used quadratic kernel function and the modified Akaike information criterion (AICc) are used to determine the optimal bandwidth [37].…”

Section: Spatial Local Models (Gwr Mgwr Twr and Gtwr)mentioning

confidence: 99%

Research on the Temporal and Spatial Distributions of Standing Wood Carbon Storage Based on Remote Sensing Images and Local Models

Zhang

Sun

Jia

et al. 2022

Forests

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Background and Objectives: It is important to understand the temporal and spatial distributions of standing wood carbon storage in forests to maintain ecological balance and forest dynamics. Such information can provide technical and data support for promoting ecological construction, formulating different afforestation policies, and implementing forest management strategies. Long-term series of Landsat 5 (Thematic Mapper, TM) and Landsat 8 (Operational Land Imager, OLI) remote sensing images and digital elevation models (DEM), as well as multiphase survey data, provide new opportunities for research on the temporal and spatial distributions of standing wood carbon storage in forests. Methods: The extracted remote sensing factors, terrain factors, and forest stand factors were analyzed with stepwise regression in relation to standing wood carbon storage to identify significant influential factors, build a global ordinary least squares (OLS) model and a linear mixed model (LMM), and construct a local geographically weighted regression (GWR), multiscale geographically weighted regression model (MGWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR). Model evaluation indicators were used to calculate residual Moran’s I values, and the optimal model was selected to explore the spatiotemporal dynamics of standing wood carbon storage in the Liangshui Nature Reserve. Results: Remote sensing factors, topographic factors (Slope), and stand factors (Age and DBH) were significantly correlated with standing wood carbon storage, and the constructed global models exhibited fitting effects inferior to those of the established local models. LMM is also used as a global model to add random effects on the basis of OLS, and R2 is increased to 0.52 compared with OLS. The local models based on geographically weighted regression, namely, GWR, MGWR, TWR, and GTWR, all have good performance. Compared with OLS, the R2 is increased to 0.572, 0.589, 0.643, and 0.734, and the fitting effect of GTWR is the best. GTWR can overcome spatial autocorrelation and temporal autocorrelation problems, with a higher R2 (0.734) and a more ideal model residual than other models. This study develops a model for carbon storage (CS) considering various influential factors in the Liangshui area and provides a possible solution for the estimation of long-term carbon storage distribution.

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Developing geographic weighted regression (GWR) technique for monitoring soil salinity using sentinel-2 multispectral imagery

Cited by 14 publications

References 35 publications

A Comparison of Machine Learning Models for Soil Salinity Estimation Using Multi-Spectral Earth Observation Data

A Comparison of Machine Learning Models for Soil Salinity Estimation Using Multi-Spectral Earth Observation Data

Impacts of the Urmia Lake Drought on Soil Salinity and Degradation Risk: An Integrated Geoinformatics Analysis and Monitoring Approach

Research on the Temporal and Spatial Distributions of Standing Wood Carbon Storage Based on Remote Sensing Images and Local Models

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