A Deep-Learning Approach to Soil Moisture Estimation with GNSS-R

Roberts, Thomas M.; Colwell, Ian; Chew, Clara; Lowe, S.; Shah, Rashmi

doi:10.3390/rs14143299

Cited by 18 publications

(13 citation statements)

References 38 publications

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“…The SMAP satellite was launched by NASA in January 2015 to obtain global soil moisture information by measuring brightness, temperature, and geophysical inversion [32,33]. Its revisiting time is 2-3 d with good space-time coverage of the world [34]. The signals received by the SMAP satellite and GNSS signal are both L-band signals.…”

Section: Ancillary Datamentioning

confidence: 99%

Detection and Evaluation of Flood Inundation Using CYGNSS Data during Extreme Precipitation in 2022 in Guangdong Province, China

Wei

et al. 2023

Remote Sensing

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Floods are severe natural disasters that are harmful and frequently occur across the world. From May to July 2022, the strongest, broadest, and longest rainfall event in recent years occurred in Guangdong Province, China. The flooding caused by continuous precipitation and a typhoon resulted in severe losses to local people and property. During flood events, there is an urgent need for timely and detailed flood inundation mapping for areas that have been severely affected. However, current satellite missions cannot provide sufficient information at a high enough spatio-temporal resolution for flooding applications. In contrast, spaceborne Global Navigation Satellite System reflectometry technology can be used to observe the Earth’s surface at a high spatio-temporal resolution without being affected by clouds or surface vegetation, providing a feasible scheme for flood disaster research. In this study, Cyclone Global Navigation Satellite System (CYGNSS) L1 science data were processed to obtain the change in the delay-Doppler map and surface reflectivity (SR) during the flood event. Then, a flood inundation map of the extreme precipitation was drawn using the threshold method based on the CYGNSS SR. Additionally, the flooded areas that were calculated based on the soil moisture from the Soil Moisture Active Passive (SMAP) data were used as a reference. Furthermore, the daily Dry Wet Abrupt Alternation Index (DWAAI) was used to identify the occurrence of the flood events. The results showed good agreement between the flood inundation that was derived from the CYGNSS SR and SMAP soil moisture. Moreover, compared with the SMAP results, the CYGNSS SR can provide the daily flood inundation with higher accuracy due to its high spatio-temporal resolution. Furthermore, the DWAAI can identify the transformation from droughts to floods in a relatively short period. Consequently, the distributions of and variations in flood inundation under extreme weather conditions can be identified on a daily scale with good accuracy using the CYGNSS data.

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Section: Ancillary Datamentioning

confidence: 99%

Detection and Evaluation of Flood Inundation Using CYGNSS Data during Extreme Precipitation in 2022 in Guangdong Province, China

Wei

et al. 2023

Remote Sensing

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“…The dataset in this study is obtained by spatio-temporal matching of CYGNSS data and SMAP data ranging from May to September 2020, where CYGNSS data provide DDMs and seven features, as shown in Table 3, and SMAP data offer vegetation information and the target label derived from the classification results of inundated versus non-inundated areas classified by SMAP soil moisture [43]. In this study, 50,000 samples were randomly selected from the dataset as the sample set of the DBNN model, and the sample set was divided into training, validation, and testing subsets at a rate of 80%, 15%, and 5%, respectively [15]. These subsets are designed to provide sufficient data to train the network, evaluate its performance, and tune the hyper-parameters.…”

Section: Training Of Dbnn Modelmentioning

confidence: 99%

“…The recently developed Global Navigation Satellite System Reflectometry (GNSS-R) is a novel remote sensing technology for physical parameter inversion by means of GNSS signals reflected from the Earth's surface [9][10][11]. The Cyclone Global Navigation Satellite System (CYGNSS), launched by NASA, provides openly accessed GNSS-R data, which has been successfully employed in the inversion of sea surface wind speed [12][13][14], soil moisture estimation [15][16][17], flood dynamics monitoring [7,18,19], and other features. The CYGNSS constellation constitutes eight small satellites, on which the receivers are mounted to capture the direct and reflected signals from the navigation satellites.…”

Section: Introductionmentioning

confidence: 99%

A Novel Dual-Branch Neural Network Model for Flood Monitoring in South Asia Based on CYGNSS Data

et al. 2022

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Microwave remote sensing is widely applied in flood monitoring due to its independence from severe weather conditions, which usually restrict the usage of optical sensors. However, it is challenging to track the variation process of flood events in a timely manner by traditional active and passive microwave techniques, since they cannot simultaneously provide measurements with high spatial and temporal resolution. The emerging Global Navigation Satellite System Reflectometry (GNSS-R) technique with high spatio-temporal resolution offers a new solution to the dynamic monitoring of flood inundation. Considering the high sensitivity of GNSS-R signals to flooding, this paper proposes a dual-branch neural network (DBNN) with a convolution neural network (CNN) and back propagation (BP) a neural network for flood monitoring. The CNN module is used to automatically extract the abstract features from delay-Doppler maps (DDMs), while the BP module is fed with GNSS-R typical features, such as surface reflectivity and power ratio, as well as vegetation information from Soil Moisture Active Passive satellite (SMAP) data. In the experiments, the superiority of the DBNN method is firstly demonstrated by comparing it with the surface reflectivity and power ratio methods. Then, the spatio-temporal variation process of the 2020 South Asian flood events is analyzed by the proposed method based on Cyclone Global Navigation Satellite System (CYGNSS) data. The understanding of flood change processes could help enhance the capacity for resisting flood disasters.

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“…This results in more than 99% of the data available from the delay-Doppler map (DDM) shown in Figure 2 being discarded due to the difficulty in incorporating them into traditional retrieval algorithms. There have been studies that propose to use the full DDM to extract additional information using deep learning models for soil moisture estimation [35] and wind speed retrieval [36]. Before moving further, it is worth providing more details and characteristics, motivating the use of the full DDM for biomass estimation.…”

Section: Introductionmentioning

confidence: 99%

Biomass Estimation with GNSS Reflectometry Using a Deep Learning Retrieval Model

Pilikos,

Clarizia,

Floury

2024

Remote Sensing

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GNSS Reflectometry (GNSS-R) is an emerging technique for the remote sensing of the environment. Traditional GNSS-R bio-geophysical parameter retrieval algorithms and deep learning models utilize observables derived from only the peak power of the delay-Doppler maps (DDMs), discarding the rest. This reduces the data available, which potentially hinders estimation accuracy. In addition, reflections from water bodies dominate the signal amplitude, and using only the peak power in those areas is challenging. Motivated by all the above, we propose a novel deep learning retrieval model for biomass estimation that uses the full DDM of surface reflectivity. Experiments using CYGNSS data have illustrated the improvements achieved when using the full DDM of surface reflectivity. Our proposed model was able to estimate biomass, trained using the ESA Climate Change Initiative (CCI) biomass map, outperforming the model that used peak reflectivity. Global and regional analysis is provided along with an illustration of how biomass estimation is achieved when using the full DDM around water bodies. GNSS-R could become an efficient method for biomass monitoring with fast revisit times. However, an elaborate calibration is necessary for the retrieval models, to associate GNSS-R data with bio-geophysical parameters on the ground. To achieve this, further developments with improved training data are required, as well as work using in situ validation data. Nevertheless, using GNSS-R and deep learning retrieval models has the potential to enable fast and persistent biomass monitoring and help us better understand our changing climate.

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A Deep-Learning Approach to Soil Moisture Estimation with GNSS-R

Cited by 18 publications

References 38 publications

Detection and Evaluation of Flood Inundation Using CYGNSS Data during Extreme Precipitation in 2022 in Guangdong Province, China

Detection and Evaluation of Flood Inundation Using CYGNSS Data during Extreme Precipitation in 2022 in Guangdong Province, China

A Novel Dual-Branch Neural Network Model for Flood Monitoring in South Asia Based on CYGNSS Data

Biomass Estimation with GNSS Reflectometry Using a Deep Learning Retrieval Model

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