Bayesian Temporal Tensor Factorization-Based Interpolation for Time Series Remote Sensing Data with Large-Area Missing Observations

2022 ◽  
pp. 1-1
Author(s):  
Haixu He ◽  
Jining Yan ◽  
Lizhe Wang ◽  
Dong Liang ◽  
Jianyi Peng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Remote Sensing Data ◽  
Missing Observations ◽  
Tensor Factorization ◽  
Large Area ◽  
Sensing Data

scholarly journals STUDY ON REMOTE SENSING MONITORING MODEL OF AGRICULTURAL DROUGHT BASED ON RANDOM FOREST DEVIATION CORRECTION

2021 ◽  
pp. 413-422
Author(s):  
Shao Li ◽  
Xia Xu
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Data Sources ◽  
Drought Monitoring ◽  
Monitoring Methods ◽  
Large Area ◽  
Sensing Data ◽  
Monitoring Model

Using remote sensing data to monitor large area drought is one of the important methods of drought monitoring at present. However, the traditional remote sensing drought monitoring methods mainly focus on monitoring single drought response factors such as soil moisture or vegetation status, and the research on comprehensive multi-factor drought monitoring is limited. In order to improve the ability to resist drought events, this paper takes Henan Province of China as an example, takes multi-source remote sensing data as data sources, considers various disaster-causing factors, adopts random forest method to model, and explores the method of regional remote sensing comprehensive drought monitoring using various remote sensing data sources. Compared with neural network, classification regression tree and linear regression, the performance of random forest is more stable and tolerant to noise and outliers. In order to provide a new method for comprehensive assessment of regional drought, a comprehensive drought monitoring model was established based on multi-source remote sensing data, which comprehensively considered the drought factors such as soil water stress, vegetation growth status and meteorological precipitation profit and loss in the process of drought occurrence and development.

Time-series processing of large scale remote sensing data with extreme learning machine

2014 ◽  
Vol 128 ◽  
pp. 199-206 ◽  
Author(s):  
Jiaoyan Chen ◽  
Guozhou Zheng ◽  
Cong Fang ◽  
Ningyu Zhang ◽  
Huajun Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Learning Machine
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