Accurate identification of soil moisture in Mu Us Sandy Land based on multi-source remote sensing data

Sinan Wang; Wenjun Wang; Yingjie Wu; Fuqiang Wang; Bin Fu

doi:10.1117/12.3029081

16 May 2024 Accurate identification of soil moisture in Mu Us Sandy Land based on multi-source remote sensing data

Sinan Wang, Wenjun Wang, Yingjie Wu, Fuqiang Wang, Bin Fu

Proceedings Volume 13166, International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024); 131660C (2024) https://doi.org/10.1117/12.3029081
Event: International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024), 2024, Changchun, China

Abstract

Apart from being essential to the development of grasslands and crops, soil moisture has a significant impact on the water cycle and the global climate. Drawing from multispectral Landsat 8 OLI photos, Added to this study, soil moisture was simultaneously measured in the field based on topographic parameters and surface biophysical features. Soil moisture inversion models of multivariable linear regression (MLR) and random forest (RF) were constructed by empirical model method. And compared with inverse distance weighting (IDW). The findings demonstrated the dependence of surface soil moisture on surface biophysical characteristics was high, with the highest inversion accuracy of random forest, in which the R² of RF model was above 0.8 in both months, which was at least 8% higher than that of MLR model, and at least 11% higher than that of IDW model, and the corresponding RMSE was the smallest in both months. In various months, the main variables influencing the soil moisture content were elevation and surface temperature. The investigation's area had low overall soil moisture content, with most of it having less than 20. High, steep slopes in the north and southeast of the area had higher soil moisture content, whereas the region's flat areas in the north-central region had lower soil moisture content.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sinan Wang, Wenjun Wang, Yingjie Wu, Fuqiang Wang, and Bin Fu "Accurate identification of soil moisture in Mu Us Sandy Land based on multi-source remote sensing data", Proc. SPIE 13166, International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024), 131660C (16 May 2024); https://doi.org/10.1117/12.3029081

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Soil moisture

Soil science

Data modeling

Random forests

Remote sensing

Vegetation

Modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years