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Tytuł pozycji:

An Effective Land Type Labeling Approach for Independently Exploiting High-Resolution Soil Moisture Products Based on CYGNSS Data

Tytuł:
An Effective Land Type Labeling Approach for Independently Exploiting High-Resolution Soil Moisture Products Based on CYGNSS Data
Autorzy:
Yan Jia
Shuanggen Jin
Qingyun Yan
Patrizia Savi
Rongchun Zhang
Wenmei Li
Temat:
Cyclone-GNSS (CYGNSS)
global navigation satellite system-reflectometry (GNSS-R)
machine learning (ML)
soil moisture (SM)
soil moisture active passive (SMAP)
Ocean engineering
TC1501-1800
Geophysics. Cosmic physics
QC801-809
Źródło:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 4234-4247 (2022)
Wydawca:
IEEE, 2022.
Rok publikacji:
2022
Kolekcja:
LCC:Ocean engineering
LCC:Geophysics. Cosmic physics
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
2151-1535
Relacje:
https://ieeexplore.ieee.org/document/9779408/; https://doaj.org/toc/2151-1535
DOI:
10.1109/JSTARS.2022.3176031
Dostęp URL:
https://doaj.org/article/32bfe2869bf1424cbb54f9a9e06f4ca8  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.32bfe2869bf1424cbb54f9a9e06f4ca8
Czasopismo naukowe
Recently, soil moisture (SM) has been estimated using Cyclone Global Navigation Satellite System (CYGNSS) data. Machine learning (ML) algorithms for CYGNSS SM estimation can minimize unpredictable influences and help improve the accuracy of SM retrieval. However, ML-based CYGNSS SM estimation requires ancillary data from other sources, and thus, the uncertainty, internal errors, and even dependence on external parameters of this process may complicate and limit SM estimation. In this article, a simple land type (LT) digitization strategy that incorporates the idea of classification is proposed with feature optimization to achieve an effective and independent SM retrieval without any other auxiliary data. The input features are chosen from the CYGNSS data themselves, and the corresponding labels (digitized stable LTs) are used in the training stage of the SM estimation model. During the fine-tuning stage, several input features (such as the dielectric constant and incident angle) are compared and selected after optimization to achieve better results. Moreover, the CYGNSS data are gridded at 9 × 9 km to validate the enhanced soil moisture active passive mission SM products at a resolution of 9 km. Only three input variables are adopted for the SM learning model, which are directly derived from the CYGNSS data for independently estimating SM at a high spatial resolution. Powerful performance is achieved by extreme gradient boosting based on a LT digitalization strategy, with root-mean-square error (RMSE) and unbiased RMSE (ubRMSE) values of 0.063 cm3/cm3 and a correlation coefficient (R) of 0.71 for the entire dataset. The performances of different ML learning models for various LTs are presented. The mean ubRMSE and RMSE are 0.041 cm3/cm3 and 0.057 cm3/cm3, respectively. The results demonstrate the effectiveness of the proposed LT digitization strategy for retrieving SM from CYGNSS data with various ML methods and the capability of SM estimation using the CYGNSS product as a new independent source.

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