A Study of Sea Surface Rain Identification Based on HY-2A Scatterometer

Peng, Yihuan; Xie, Xuetong; Lin, Mingsen; Ran, Lishan; Yuan, Feng; Zhou, Yuan; Tang, Li

doi:10.3390/rs13173475

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Therefore, selecting an appropriate value for K plays a pivotal role in the model. The KNN model has been demonstrated to exhibit favorable performance in the rain identification of HY-2A [21]. The performance of KNN with K = 3 (KNN3) and KNN with K = 5 (KNN5) is compared to that of DBO-XGBoost models.…”

Section: Evaluation Of the Dbo-xgboost Model In Rain Identificationmentioning

confidence: 99%

“…However, the model cannot independently utilize scatterometer parameters for rain identification and relies on external collocated data sources, including parameters derived from numerical weather prediction (NWP) models: total precipable water (TPW), ground relative humidity (RH), wind speed (WS), and wind direction (WD). The HY2RRM model for rain identification of HY-2A data was developed based on the K-nearest Neighbor (KNN) [21], while employing the same set of rain-sensitive parameters as the MUDH rain flag. Meanwhile, MUDH has also been transplanted to HY-2A data.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Rain Identification and Rain Intensity Classification Method for the CFOSAT Scatterometer

Quan,

Zhang,

Zhang

2024

Remote Sensing

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The China–France oceanography satellite scatterometer (CSCAT) is a rotating fan-beam scanning observation scatterometer operating in the Ku-band, and its product quality is affected by rain contamination. The multiple azimuthal NRCS measurements provided by CSCAT L2A, the retrieved wind speed and wind direction provided by CSCAT L2B, as well as the rain data provided by GPM, are used to construct a new rain identification and rain intensity classification model for CSCAT. The EXtreme Gradient Boosting (XGBoost) model, optimized by the Dung Beetle Optimizer (DBO) algorithm, is developed and evaluated. The performance of the DBO-XGBoost exceeds that of the CSCAT rain flag in terms of rain identification ability. Also, compared with XGBoost without parameter optimization, K-nearest Neighbor with K = 5 (KNN5) and K-nearest Neighbor with K = 3 (KNN3), the performance of DBO-XGBoost is better. Its rain identification achieves an accuracy of about 90% and a precision of about 80%, which enhances the quality control of rain. DBO-XGBoost has also shown good results in the classification of rain intensity. This ability is not available in traditional rain flags. In the global regional and local regional tests, most of the accuracy and precision in rain intensity classification have reached more than 80%. This technology makes full use of the rich observed information of CSCAT, realizes rain identification, and can also classify the rain intensity so as to further evaluate the degree of rain contamination of CSCAT products.

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Section: Evaluation Of the Dbo-xgboost Model In Rain Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%