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Estimating Cloud Base Height via Shadow-Based Remote Sensing.

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Title: Estimating Cloud Base Height via Shadow-Based Remote Sensing.
Authors: Mukherjee, Lipi1,2 (AUTHOR) lipi.mukherjee@nasa.gov, Wu, Dong L.2 (AUTHOR)
Source: Remote Sensing. Jan2026, Vol. 18 Issue 1, p147. 21p.
Subjects: Remote sensing, MODIS (Spectroradiometer), Environmental monitoring, LIDAR, Ceilometer, Remote-sensing images
Geographic Terms: Tonga
Abstract: Highlights: What are the main findings? A shadow-based geometric method accurately retrieves cloud and plume heights from single-view satellite imagery. The approach successfully captures both boundary-layer cloud base height and the vertical structure of the 2022 Hunga Tonga–Hunga Ha'apai eruption. What are the implication of the main findings? The method enables rapid, physically based height retrieval in regions lacking active or stereo sensors. It provides a scalable tool for atmospheric monitoring, volcanic hazard assessment, and planetary applications. Low clouds significantly impact weather, climate, and multiple environmental and economic sectors such as agriculture, fire risk management, aviation, and renewable energy. Accurate knowledge of cloud base height (CBH) is critical for optimizing crop yields, improving fire danger forecasts, enhancing flight safety, and increasing solar energy efficiency. This study evaluates a shadow-based CBH retrieval method using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite visible imagery and compares the results against collocated lidar measurements from the Micro-Pulse Lidar Network (MPLNET) ground stations. The shadow method leverages sun–sensor geometry to estimate CBH from the displacement of cloud shadows on the surface, offering a practical and high-resolution passive remote sensing technique, especially useful where active sensors are unavailable. The validation results show strong agreement, with a correlation coefficient (R) = 0.96 between shadow-based and lidar-derived CBH estimates, confirming the robustness of the approach for shallow, isolated cumulus clouds. The method's advantages include direct physical height estimation without reliance on cloud top heights or stereo imaging, applicability across archived datasets, and suitability for diurnal studies. This work highlights the potential of shadow-based retrievals as a reliable, cost-effective tool for global low cloud monitoring, with important implications for atmospheric research and operational forecasting. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Highlights: What are the main findings? A shadow-based geometric method accurately retrieves cloud and plume heights from single-view satellite imagery. The approach successfully captures both boundary-layer cloud base height and the vertical structure of the 2022 Hunga Tonga–Hunga Ha'apai eruption. What are the implication of the main findings? The method enables rapid, physically based height retrieval in regions lacking active or stereo sensors. It provides a scalable tool for atmospheric monitoring, volcanic hazard assessment, and planetary applications. Low clouds significantly impact weather, climate, and multiple environmental and economic sectors such as agriculture, fire risk management, aviation, and renewable energy. Accurate knowledge of cloud base height (CBH) is critical for optimizing crop yields, improving fire danger forecasts, enhancing flight safety, and increasing solar energy efficiency. This study evaluates a shadow-based CBH retrieval method using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite visible imagery and compares the results against collocated lidar measurements from the Micro-Pulse Lidar Network (MPLNET) ground stations. The shadow method leverages sun–sensor geometry to estimate CBH from the displacement of cloud shadows on the surface, offering a practical and high-resolution passive remote sensing technique, especially useful where active sensors are unavailable. The validation results show strong agreement, with a correlation coefficient (R) = 0.96 between shadow-based and lidar-derived CBH estimates, confirming the robustness of the approach for shallow, isolated cumulus clouds. The method's advantages include direct physical height estimation without reliance on cloud top heights or stereo imaging, applicability across archived datasets, and suitability for diurnal studies. This work highlights the potential of shadow-based retrievals as a reliable, cost-effective tool for global low cloud monitoring, with important implications for atmospheric research and operational forecasting. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jan2026
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              Y: 2026
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