Clouds regulate the radiative balance of the Earth-Atmosphere system through reflection, absorption, and scattering of solar shortwave radiation as well as surface/atmosphere longwave radiation. They also influence weather and climate through interactions with aerosols and precipitation. Cloud base height (CBH) is one of the most important cloud properties, possessing significant scientific research and practical application value. The radiative effects of clouds at different heights exhibit considerable variation. While low clouds typically cause a cooling effect on the atmosphere, high clouds are more likely to induce a warming effect. Moreover, CBH is essential information for various applications, including aviation weather protection and artificial weather modification. During flight, clouds can obstruct the pilot’s vision, and potential lightning and ice accumulation within the clouds can pose serious threats to aircraft safety. Therefore, accurately characterizing CBH is crucial for ensuring flight safety. Active remote sensing instruments, including millimeter-wave cloud radar and ceilometers, can detect cloud vertical structure with high accuracy. However, due to construction and maintenance costs, the ground-based cloud radar measurements cannot cover regions such as oceans and deserts, making it challenging to meet the needs of weather system analysis and climate change research. The launch of spaceborne millimeter-wave cloud profiling radar (CPR) enables global detection of cloud vertical structure, significantly enhancing our understanding of global cloud distribution characteristics and improving cloud parameterization schemes. Nonetheless, CPR can only detect nadir clouds along the orbit track, and surface clutter affects the accuracy of its detection of near-surface clouds. As a passive remote sensing instrument, the observation range of satellite multi-spectral imagers is much larger than that of active instruments like CPR, making them the primary means of cloud remote sensing today. However, due to the limited penetration ability of visible and infrared radiation through clouds, retrieving CBH using visible and infrared observations from satellite multi-spectral imagers presents theoretical challenges. Currently, most meteorological satellites do not include CBH in their operational product systems. Thus, developing retrieval methods based on satellite multi-spectral imagers to achieve wide-ranging and high-precision monitoring of CBH has become a key scientific goal in the cloud remote sensing community. In recent years, China’s new-generation Fengyun-3 and Fengyun-4 series satellites have been successfully launched, and their instrumental performance generally reaches an advanced global level. However, none of the Fengyun meteorological satellites provide operational CBH products, limiting their applications in extreme weather monitoring, weather modification, and solar energy resource estimation. In this study, we analyze the main scientific challenges faced by passive remote sensing satellites in retrieving CBH, review the research progress of current CBH retrieval methods, and discuss the advantages and limitations of different approaches. Finally, we summarize our findings to guide future developments in this field.

Scientists have proposed various retrieval methods for deriving CBH from satellite multi-spectral imagers. Among them, the most typical method estimates cloud geometric thickness (CGT) from cloud water path (CWP) and then subtracts CGT from existing cloud top height (CTH) products to obtain the desired CBH. The relationship between CWP and CGT is primarily determined by cloud type, using empirical constants for six cloud types to retrieve CBH. However, validation against active CPR measurements shows that the results are highly biased. By correlating the statistical relationship between CWP and CGT to altitude, we present a segmented fitting approach that significantly improves CBH retrievals. To reduce retrieval errors caused by spatial and temporal variations in cloud properties, we compile and apply a systematic lookup table of effective cloud water content (ECWC) for different clouds and environmental conditions to the moderate resolution imaging spectroradiometer (MODIS) and advanced Himawari imager (AHI). In addition, advance machine learning techniques have been introduced in CBH retrievals. These theoretical and methodological advances demonstrate the feasibility of retrieving CBH from satellite multi-spectral imagers, enhancing our understanding of cloud vertical distribution globally.

Overcoming the technical bottleneck of continuous three-dimensional atmospheric observation, including clouds, and enhancing the quantitative application capability of meteorological satellites are key areas for development in China’s meteorological community. At present, there are still some shortcomings in characterizing the three-dimensional structure of clouds, especially CBH. However, with the robust development of satellite instruments and continuous innovation in remote sensing theories, the accuracy of CBH retrievals will improve, providing vital support for precision monitoring and accurate prediction.