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Multi-Scale Assessment of Nighttime Heat Health Risk and Dominant Factors Using MODIS and SDGSAT-1 Observations.

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Title: Multi-Scale Assessment of Nighttime Heat Health Risk and Dominant Factors Using MODIS and SDGSAT-1 Observations.
Authors: Tan, Zhuang1 (AUTHOR), Man, Qixia1,2 (AUTHOR) qixiaman@sdnu.edu.cn, Zhang, Baolei1,3 (AUTHOR), Dong, Pinliang2,4 (AUTHOR), Sun, Haiying3,5 (AUTHOR), Sun, Zhongchang4,5,6 (AUTHOR), Lu, Linlin4,5,6,7 (AUTHOR), Dou, Changyong1,4,5,6 (AUTHOR), Yang, Xinming2,7 (AUTHOR), Han, Changyin1,3 (AUTHOR), Zhou, Cong1,4 (AUTHOR), Sun, Xiaoqi1,5 (AUTHOR), Wang, Jian1,6 (AUTHOR), Li, Zizhen1,7 (AUTHOR)
Source: Remote Sensing. Jul2026, Vol. 18 Issue 13, p2243. 24p.
Subjects: MODIS (Spectroradiometer), Risk assessment, Provinces, Urban climatology, Climate change adaptation, Remote-sensing images
Geographic Terms: China, Shandong Sheng (China)
Abstract: Highlights: What are the main findings? A multi-scale framework links MODIS-based provincial screening with SDGSAT-1-based block-level assessment of nighttime heat health risk. Higher-risk units generally involve more complex dominant factor configurations, and LCZ types are associated with distinct dominant-factor patterns. What are the implications of the main findings? The framework supports differentiated adaptation across spatial scales by identifying where nighttime heat health risk is concentrated and which risk components dominate. High-resolution SDGSAT-1 nighttime observations improve fine-scale identification of intra-urban heat health risk and its dominant factors. Climate change is amplifying heat-related health risks, making heat health risk assessment increasingly important for sustainable urban development. However, current studies still face three challenges: (1) macro-scale and fine-scale assessments remain weakly linked; (2) nighttime heat health risk has received limited attention; and (3) dominant factor identification remains insufficient, especially within the Local Climate Zone (LCZ) framework. To address these gaps, this study developed a hazard–exposure–vulnerability-based, multi-scale framework for assessing nighttime heat health risk in Shandong Province, China. At the macro scale, MODIS nighttime land surface temperature (1 km) was used to characterize heat hazard, and district-level risk was assessed by integrating socio-statistical data. Hotspot analysis was then applied to identify high-risk clusters and select cities for fine-scale assessment. At the fine scale, SDGSAT-1 thermal infrared data (30 m) were used to characterize intra-urban nighttime heat hazard, and block-level risk was assessed by integrating socioeconomic and urban infrastructure data. Dominant risk factors were further identified for each spatial unit. Results show that (1) at the macro scale, high nighttime heat health risk districts are concentrated mainly in southern Shandong and major urban cores; (2) at the fine scale, overall risk is higher in inland cities than in the coastal city, and the inland cities show similar spatial patterns; (3) at both district and block units, higher risk levels are associated with more complex dominant factor configurations; and (4) compact high- and mid-rise zones (LCZ 1–2) are mainly characterized by multiple dominant factors, whereas open low-rise/sparsely built zones (LCZ 6/9) are mainly characterized by no dominant factor. This study provides a multi-level perspective for heat-risk governance, offers a scientific basis for both macro-scale policy formulation and fine-scale intervention, and contributes to more sustainable and resilient cities. [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: Multi-Scale Assessment of Nighttime Heat Health Risk and Dominant Factors Using MODIS and SDGSAT-1 Observations.
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  Data: <searchLink fieldCode="AR" term="%22Tan%2C+Zhuang%22">Tan, Zhuang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Man%2C+Qixia%22">Man, Qixia</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> qixiaman@sdnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Baolei%22">Zhang, Baolei</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Pinliang%22">Dong, Pinliang</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Haiying%22">Sun, Haiying</searchLink><relatesTo>3,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Zhongchang%22">Sun, Zhongchang</searchLink><relatesTo>4,5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Linlin%22">Lu, Linlin</searchLink><relatesTo>4,5,6,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dou%2C+Changyong%22">Dou, Changyong</searchLink><relatesTo>1,4,5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Xinming%22">Yang, Xinming</searchLink><relatesTo>2,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Changyin%22">Han, Changyin</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Cong%22">Zhou, Cong</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Xiaoqi%22">Sun, Xiaoqi</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Jian%22">Wang, Jian</searchLink><relatesTo>1,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Zizhen%22">Li, Zizhen</searchLink><relatesTo>1,7</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jul2026, Vol. 18 Issue 13, p2243. 24p.
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  Data: <searchLink fieldCode="DE" term="%22MODIS+%28Spectroradiometer%29%22">MODIS (Spectroradiometer)</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Provinces%22">Provinces</searchLink><br /><searchLink fieldCode="DE" term="%22Urban+climatology%22">Urban climatology</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change+adaptation%22">Climate change adaptation</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink><br /><searchLink fieldCode="DE" term="%22Shandong+Sheng+%28China%29%22">Shandong Sheng (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? A multi-scale framework links MODIS-based provincial screening with SDGSAT-1-based block-level assessment of nighttime heat health risk. Higher-risk units generally involve more complex dominant factor configurations, and LCZ types are associated with distinct dominant-factor patterns. What are the implications of the main findings? The framework supports differentiated adaptation across spatial scales by identifying where nighttime heat health risk is concentrated and which risk components dominate. High-resolution SDGSAT-1 nighttime observations improve fine-scale identification of intra-urban heat health risk and its dominant factors. Climate change is amplifying heat-related health risks, making heat health risk assessment increasingly important for sustainable urban development. However, current studies still face three challenges: (1) macro-scale and fine-scale assessments remain weakly linked; (2) nighttime heat health risk has received limited attention; and (3) dominant factor identification remains insufficient, especially within the Local Climate Zone (LCZ) framework. To address these gaps, this study developed a hazard–exposure–vulnerability-based, multi-scale framework for assessing nighttime heat health risk in Shandong Province, China. At the macro scale, MODIS nighttime land surface temperature (1 km) was used to characterize heat hazard, and district-level risk was assessed by integrating socio-statistical data. Hotspot analysis was then applied to identify high-risk clusters and select cities for fine-scale assessment. At the fine scale, SDGSAT-1 thermal infrared data (30 m) were used to characterize intra-urban nighttime heat hazard, and block-level risk was assessed by integrating socioeconomic and urban infrastructure data. Dominant risk factors were further identified for each spatial unit. Results show that (1) at the macro scale, high nighttime heat health risk districts are concentrated mainly in southern Shandong and major urban cores; (2) at the fine scale, overall risk is higher in inland cities than in the coastal city, and the inland cities show similar spatial patterns; (3) at both district and block units, higher risk levels are associated with more complex dominant factor configurations; and (4) compact high- and mid-rise zones (LCZ 1–2) are mainly characterized by multiple dominant factors, whereas open low-rise/sparsely built zones (LCZ 6/9) are mainly characterized by no dominant factor. This study provides a multi-level perspective for heat-risk governance, offers a scientific basis for both macro-scale policy formulation and fine-scale intervention, and contributes to more sustainable and resilient cities. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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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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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/rs18132243
    Languages:
      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 2243
    Subjects:
      – SubjectFull: MODIS (Spectroradiometer)
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Provinces
        Type: general
      – SubjectFull: Urban climatology
        Type: general
      – SubjectFull: Climate change adaptation
        Type: general
      – SubjectFull: Remote-sensing images
        Type: general
      – SubjectFull: China
        Type: general
      – SubjectFull: Shandong Sheng (China)
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      – TitleFull: Multi-Scale Assessment of Nighttime Heat Health Risk and Dominant Factors Using MODIS and SDGSAT-1 Observations.
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              M: 07
              Text: Jul2026
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