Spatial prediction of soil salinity based on the Google Earth Engine platform with multitemporal synthetic remote sensing images

Ma, Shilong; He, Baozhong; Ge, Xiangyu; Luo, Xuefeng

doi:10.1016/j.ecoinf.2023.102111

Cited by 7 publications

(3 citation statements)

References 57 publications

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“…Although spectral indices for mapping soil salinity using remote sensing have been employed in the domain of soil science, it is important to consider new platforms such as the Google Earth Engine (GEE) to update these approaches. The GEE offers enhanced capabilities for analyzing and mapping soil salinity distributions, providing opportunities to improve the accuracy and efficiency of inventory soil maps [24]. The GEE operates on cloud computing, utilizing Google's computational infrastructure and a vast collection of freely available remote sensing imagery with different resolutions.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Spatio-Temporal Distribution of Soil Salinity Using Google Earth Engine for Detecting the Saline Areas Susceptible to Salt Storm Occurrence

Kazemi Garajeh

2024

Pollutants

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Recent droughts worldwide have significantly affected ecosystems in various regions. Among these affected areas, the Lake Urmia Basin (LUB) has experienced substantial effects from both drought and human activity in recent years. Lake Urmia, known as one of the hypersaline lakes globally, has been particularly influenced by these activities. The extraction of water since 1995 has resulted in an increase in the extent of salty land, leading to the frequent occurrence of salt storms. To address this issue, the current study utilized various machine learning algorithms within the Google Earth Engine (GEE) platform to map the probability of saline storm occurrences. Landsat time-series images spanning from 2000 to 2022 were employed. Soil salinity indices, Ground Points (GPs), and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products were utilized to prepare the training data, which served as input for constructing and running the models. The results demonstrated that the Support Vector Machine (SVM) performed effectively in identifying the probability of saline storm occurrence areas, achieving high R2 values of 91.12%, 90.45%, 91.78%, and 91.65% for the years 2000, 2010, 2015, and 2022, respectively. Additionally, the findings reveal an increase in areas exhibiting a very high probability of saline storm occurrences from 2000 to 2022. In summary, the results of this study indicate that the frequency of salt storms is expected to rise in the near future, owing to the increasing levels of soil salinity resources within the Lake Urmia Basin.

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Section: Introductionmentioning

confidence: 99%

Monitoring the Spatio-Temporal Distribution of Soil Salinity Using Google Earth Engine for Detecting the Saline Areas Susceptible to Salt Storm Occurrence

Kazemi Garajeh

2024

Pollutants

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“…Such models enhance the capability to monitor soil salinization. Moreover, scholars have used vegetation indices to construct models in GEE to study soil salinization [26]. Currently, the integration of GEE and machine learning is being employed for cropland mapping [27,28], surface temperature estimation [29], and classification monitoring [30].…”

Section: Introductionmentioning

confidence: 99%

Mapping the Soil Salinity Distribution and Analyzing Its Spatial and Temporal Changes in Bachu County, Xinjiang, Based on Google Earth Engine and Machine Learning

Zhang,

Wu,

Kang

et al. 2024

Agriculture

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Soil salinization has a significant impact on agricultural production and ecology. There is an urgent demand to establish an effective method that monitors the spatial and temporal distribution of soil salinity. In this study, a multi-indicator soil salinity monitoring model was proposed for monitoring soil salinity in Bachu County, Kashgar Region, Xinjiang, from 2002 to 2022. The model was established by combining multiple predictors (spectral, salinity, and composite indices and topographic factors) and the accuracy of the four models (Random Forest [RF], Partial Least Squares [PLS], Classification Regression Tree [CART], and Support Vector Machine [SVM]) was compared. The results reveal the high accuracy of the optimized prediction model, and the order of the accuracy is observed as RF > PLS > CART > SVM. The most accurate model, RF, exhibited an R2 of 0.723, a root mean square error (RMSE) of 2.604 g·kg−1, and a mean absolute error (MAE) of 1.95 g·kg−1 at a 0–20 cm depth. At a 20–40 cm depth, RF had an R2 value of 0.64, an RMSE of 3.62 g·kg−1, and an MAE of 2.728 g·kg−1. Spatial changes in soil salinity were observed throughout the study period, particularly increased salinization from 2002 to 2012 in the agricultural and mountainous areas within the central and western regions of the country. However, salinization declined from 2012 to 2022, with a decreasing trend in salinity observed in the top 0–20 cm of soil, followed by an increasing trend in salinity at a 20–40 cm depth. The proposed method can effectively extract large-scale soil salinity and provide a practical basis for simplifying the remote sensing monitoring and management of soil salinity. This study also provides constructive suggestions for the protection of agricultural areas and farmlands.

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“…As emerging tools, UAV have the advantages of portability, high spatial resolution, high flexibility, independent selection of flight time, and the ability to carry a variety of spectral cameras 17 . This can quickly and efficiently achieve remote sensing image acquisition in a specified area 18 .…”

Section: Introductionmentioning

confidence: 99%

Estimation and mapping of soil texture content based on unmanned aerial vehicle hyperspectral imaging

Song,

Gao,

Song

et al. 2023

Sci Rep

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Soil texture is one of the important physical and natural properties of soil. Much of the current research focuses on soil texture monitoring using non-imaging geophysical spectrometers. However there are fewer studies utilizing unmanned aerial vehicle (UAV) hyperspectral data for soil texture monitoring. UAV mounted hyperspectral cameras can be used for quickly and accurately obtaining high-resolution spatial information of soil texture. A foundation has been laid for the realization of rapid soil texture surveys using unmanned airborne hyperspectral data without field sampling. This study selected three typical farmland areas in Huangshui Basin of Qinghai as the study area, and a total of 296 soil samples were collected. Data calibration of UAV spectra using laboratory spectra and field in situ spectra to explore the feasibility of applying laboratory soil texture models directly to field conditions. This results show that UAV hyperspectral imagery combined with machine learning can obtain a set of ideal processing methods. The pre-processing of the spectral data can obtain high accuracy of soil texture estimation and good mapping effect. The results of this study can provide effective technical support and decision-making assistance for future agricultural land planning on the Tibetan Plateau. The main innovation of this study is to establish a set of processing procedures and methods applicable to UAV hyperspectral imagery to provide data reference for monitoring soil texture in agricultural fields on the Tibetan Plateau.

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Spatial prediction of soil salinity based on the Google Earth Engine platform with multitemporal synthetic remote sensing images

Cited by 7 publications

References 57 publications

Monitoring the Spatio-Temporal Distribution of Soil Salinity Using Google Earth Engine for Detecting the Saline Areas Susceptible to Salt Storm Occurrence

Monitoring the Spatio-Temporal Distribution of Soil Salinity Using Google Earth Engine for Detecting the Saline Areas Susceptible to Salt Storm Occurrence

Mapping the Soil Salinity Distribution and Analyzing Its Spatial and Temporal Changes in Bachu County, Xinjiang, Based on Google Earth Engine and Machine Learning

Estimation and mapping of soil texture content based on unmanned aerial vehicle hyperspectral imaging

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