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

2021 ◽  
Vol 80 (3) ◽  
Author(s):  
Mohammad Mahdi Taghadosi ◽  
Mahdi Hasanlou
Keyword(s):  
Soil Salinity ◽  
Weighted Regression ◽  
Multispectral Imagery ◽  
Geographic Weighted Regression ◽  
Sentinel 2

scholarly journals Retrieval of soil salinity from Sentinel-2 multispectral imagery

2019 ◽  
Vol 52 (1) ◽  
pp. 138-154 ◽  
Author(s):  
Mohammad Mahdi Taghadosi ◽  
Mahdi Hasanlou ◽  
Kamran Eftekhari
Keyword(s):  
Soil Salinity ◽  
Multispectral Imagery ◽  
Sentinel 2

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery

2021 ◽  
Vol 181 ◽  
pp. 148-166
Author(s):  
Lan Xun ◽  
Jiahua Zhang ◽  
Dan Cao ◽  
Shanshan Yang ◽  
Fengmei Yao
Keyword(s):  
Multispectral Imagery ◽  
Sentinel 2

scholarly journals Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China

Geoderma ◽  
2019 ◽  
Vol 353 ◽  
pp. 172-187 ◽  
Author(s):  
Jingzhe Wang ◽  
Jianli Ding ◽  
Danlin Yu ◽  
Xuankai Ma ◽  
Zipeng Zhang ◽  
...  
Keyword(s):  
Soil Salinity ◽  
Lake Region ◽  
Ebinur Lake ◽  
Sentinel 2

Geographic Weighted Regression (GWR)

2008 ◽  
pp. 364-364
Author(s):  
Shashi Shekhar ◽  
Hui Xiong
Keyword(s):  
Weighted Regression ◽  
Geographic Weighted Regression

scholarly journals Feature Selection on Sentinel-2 Multispectral Imagery for Mapping a Landscape Infested by Parthenium Weed

2019 ◽  
Vol 11 (16) ◽  
pp. 1892 ◽  
Author(s):  
Zolo Kiala ◽  
Onisimo Mutanga ◽  
John Odindi ◽  
Kabir Peerbhay
Keyword(s):  
Feature Selection ◽  
Invasive Plant ◽  
Feature Selection Method ◽  
Sparse Learning ◽  
High Temporal Resolution ◽  
Multispectral Imagery ◽  
Selection Methods ◽  
Multi Temporal ◽  
Parthenium Weed ◽  
Sentinel 2

In the recent past, the volume of spatial datasets has significantly increased. This is attributed to, among other factors, higher sensor temporal resolutions of the recently launched satellites. The increased data, combined with the computation and possible derivation of a large number of indices, may lead to high multi-collinearity and redundant features that compromise the performance of classifiers. Using dimension reduction algorithms, a subset of these features can be selected, hence increasing their predictive potential. In this regard, an investigation into the application of feature selection techniques on multi-temporal multispectral datasets such as Sentinel-2 is valuable in vegetation mapping. In this study, ten feature selection methods belonging to five groups (Similarity-based, statistical-based, Sparse learning based, Information theoretical based, and wrappers methods) were compared based on f-score and data size for mapping a landscape infested by the Parthenium weed (Parthenium hysterophorus). Overall, results showed that ReliefF (a Similarity-based approach) was the best performing feature selection method as demonstrated by the high f-score values of Parthenium weed and a small size of optimal features selected. Although svm-b (a wrapper method) yielded the highest accuracies, the size of optimal subset of selected features was quite large. Results also showed that data size affects the performance of feature selection algorithms, except for statistically-based methods such as Gini-index and F-score and svm-b. Findings in this study provide a guidance on the application of feature selection methods for accurate mapping of invasive plant species in general and Parthenium weed, in particular, using new multispectral imagery with high temporal resolution.

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