2021
DOI: 10.1016/j.still.2021.105134
|View full text |Cite
|
Sign up to set email alerts
|

Estimation of soil texture using Sentinel-2 multispectral imaging data: An ensemble modeling approach

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
5
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 12 publications
(5 citation statements)
references
References 45 publications
0
5
0
Order By: Relevance
“…The findings demonstrated that clay fraction could be predicted from Sentinel-2 images for mapping larger geographic areas [10]. The study by Swain et al [16] was based on 295 soil samples, with an ensemble modeling approach of the surface soil particle-size fractions in the western catchment of Chilika lagoon using the ten bands of Sentinel-2 images, Band2, Band3, Band4, Band5, Band6, Band7, Band8, Band8a, Band11, and Band12, and the results were comparable to hyperspectral data observations measured in the laboratory. Gomez et al [14] used 130 soil surface samples from a cultivated region in India to map soil texture classification through the ten bands of Sentinel-2 images, Band2, Band3, Band4, Band5, Band6, Band7, Band8, Band8a, Band11, and Band12, and demonstrated that Sentinel-2 images provided the potential for inputs of environmental process and crop management modeling.…”
Section: Introductionmentioning
confidence: 76%
See 1 more Smart Citation
“…The findings demonstrated that clay fraction could be predicted from Sentinel-2 images for mapping larger geographic areas [10]. The study by Swain et al [16] was based on 295 soil samples, with an ensemble modeling approach of the surface soil particle-size fractions in the western catchment of Chilika lagoon using the ten bands of Sentinel-2 images, Band2, Band3, Band4, Band5, Band6, Band7, Band8, Band8a, Band11, and Band12, and the results were comparable to hyperspectral data observations measured in the laboratory. Gomez et al [14] used 130 soil surface samples from a cultivated region in India to map soil texture classification through the ten bands of Sentinel-2 images, Band2, Band3, Band4, Band5, Band6, Band7, Band8, Band8a, Band11, and Band12, and demonstrated that Sentinel-2 images provided the potential for inputs of environmental process and crop management modeling.…”
Section: Introductionmentioning
confidence: 76%
“…In the spectral indices constructed by Sentinel-2 images, SATVI (composed of Short-Wave InfraRed and Near-InfraRed) revealed the highest correlation with sand, silt, and clay fractions. Sand, silt, and clay fractions in the study area were correlated to the amount of visible, Near-InfraRed and Short-Wave InfraRed absorbed and/or reflected, according to the high correlations of Band11, Band12, and SATVI [16].…”
Section: Correlation Of Covariatesmentioning
confidence: 94%
“…According to Swain et al [37], PLSR, SVR, and RF models were utilized to predict the silt, clay, and sand contents based on both S2 spectra and laboratory measurements. The results described that the ensemble modeling approach estimated the soil content with high R 2 values of sand, silt, and clay (0.62, 0.54, 0.54).…”
Section: Introductionmentioning
confidence: 99%
“…It is an important index in the field of cultivated land quality and in the evaluation of crop suitability 4 . The estimation and mapping of the spatial distribution of soil texture can not only enrich and improve the soil digital database, but also provide a basis and data support for research on the spatial distribution of soil attributes and agricultural production planning 5 . Therefore, precision agricultural management is urgently required for the high-resolution and high-precision quantification and monitoring of the spatial and temporal distribution characteristics of soil texture at the field scale.…”
Section: Introductionmentioning
confidence: 99%