Fengyun-3D/MERSI-II Cloud Thermodynamic Phase Determination Using a Machine-Learning Approach

Dexin Zhao,Lin Zhu,Jun Li,Hongfu Sun,Weishi Wang

doi:10.3390/rs13122251

Abstract

Global cloud thermodynamic phase (CP) is normally derived from polar-orbiting satellite imaging data with high spatial resolution. However, constraining conditions and empirical thresholds used in the MODIS (Moderate Resolution Imaging Spectroradiometer) CP algorithm are closely associated with spectral properties of the MODIS infrared (IR) spectral bands, with obvious deviations and incompatibility induced when the algorithm is applied to data from other similar space-based sensors. To reduce the algorithm dependence on spectral properties and empirical thresholds for CP retrieval, a machine learning (ML)-based methodology was developed for retrieving CP data from China’s new-generation polar-orbiting satellite, FY-3D/MERSI-II (Fengyun-3D/Moderate Resolution Spectral Imager-II). Five machine learning algorithms were used, namely, k-nearest-neighbor (KNN), support vector machine (SVM), random forest (RF), Stacking and gradient boosting decision tree (GBDT). The RF algorithm gave the best performance. One year of EOS (Earth Observation System) MODIS CP products (July 2018 to June 2019) were used as reference labels to train the relationship between MODIS CP (MYD06 IR) and six IR bands of MERSI-II. CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), MODIS, and FY-3D/MERSI-II CP products were used together for cross-validation. Results indicate strong spatial consistency between ML-based MERSI-II and MODIS CP products. The hit rate (HR) of random forest (RF) CP product could reach 0.85 compared with MYD06 IR CP products. In addition, when compared with the operational FY-3D/MERSI CP product, the RF-based CP product had higher HRs. Using the CALIOP cloud product as an independent reference, the liquid-phase accuracy of the RF CP product was higher than that of operational FY-3D/MERSI-II and MYD06 IR CP products. This study aimed to establish a robust algorithm for deriving FY-3D/MERSI-II CP climate data record (CDR) for research and applications.

Highlights

Clouds are the important factors for regulating the global energy exchange and water cycle, reflecting and absorbing incident solar radiation and Earth’s outgoing long-wave radiation [1]
This study aimed to establish a robust algorithm for deriving FY-3D/MERSI-II cloud thermodynamic phase (CP) climate data record (CDR) for research and applications
An algorithm using three IR bands was developed for official MODIS CP product [7] (MODIS v. 6), while four IR bands

Summary

Introduction

Clouds are the important factors for regulating the global energy exchange and water cycle, reflecting and absorbing incident solar radiation and Earth’s outgoing long-wave radiation [1]. The CP products derived from measurements of satellite imaging sensors [2,3,4] provide a priori and crucial knowledge on cloud-top height (CTH), cloud optical thickness (COT), and cloud-top effective particle size (CPS). Various retrieval methods for space-based imaging sensors have been developed in the past 20 years to improve the understanding of the natural characteristics of CP. Method [5], the three-band IR method [6,7], the effective absorption of optical thickness ratio or β index method [8,9], the visible (VIS) and near infrared (NIR) method [10,11], and a joint method using VIS, NIR, and IR bands [12,13], have been developed for deriving CP from polar-orbiting satellite imaging measurements. Thresholds for a given satellite sensor can cause noticeable deviations and incompatibilities when it is applied directly to other similar space-based sensors [14,15,16]

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