Deep Learning-Based Soil Moisture Retrieval in CONUS Using CYGNSS Delay–Doppler Maps

Nabi, M M; Senyurek, Volkan; Gürbüz, Ali Cafer; Kurum, Mehmet

doi:10.1109/jstars.2022.3196658

Cited by 26 publications

(9 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the combination of CNN and handmade features enhances image classification [69,70]. Similarly, theoretical remote sensing knowledge is increasingly combined with deep learning to further improve its performance [71]. The analysis in this paper also shows that traditional CNN models (such as CNN1 and CNN2) without full connection layer to combine auxiliary parameters and DDM result in an RMSE of 2.46 m/s.…”

Section: Discussionmentioning

confidence: 68%

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

Zuo

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth’s surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds.

show abstract

Section: Discussionmentioning

confidence: 68%

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

Zuo

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Apart from classical machine learning, researchers have accepted deep learning, enabling a robust framework by adding more layers. However, increasing more layers to the network may also increase the complexity and computational time [ 71 ]. The popular learning-based model used in the literature for detection is SVM, CNN, deep neural network, etc.…”

Section: Discussionmentioning

confidence: 99%

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Islam¹,

Nabi²,

Ball³

2022

Sensors

Self Cite

View full text Add to dashboard Cite

When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

show abstract

“…These studies prove that learning features by treating DDMs as images is indeed beneficial for the wind speed retrieval. Besides, the effectiveness of coupling DDMs with CNNs has been further demonstrated in other tasks using GNSS-R techniques, such as soil moisture estimation [27,28] and sea ice detection [29,30].…”

Section: Introductionmentioning

confidence: 99%

DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation

Zhao

Heidler

Asgarimehr

et al. 2023

Remote Sensing of Environment

View full text Add to dashboard Cite

Deep Learning-Based Soil Moisture Retrieval in CONUS Using CYGNSS Delay–Doppler Maps

Abstract: His research interests are remote sensing, biomedical signal processing, wearable sensors, pattern recognition, fiber optic sensors, and structural health monitoring.

Cited by 26 publications

References 42 publications

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation

Contact Info

Product

Resources

About