Recommended Practice: Radiometric intercalibration of multi-source nighttime light images at high resolution for disaster mapping


Image source: UNOSAT report, 2023 (https://unosat.org/products/3497).

 

Overview

High-resolution nighttime light (NTL) remote sensing has become an important tool for disaster assessment. Disasters such as earthquakes and conflicts can cause sudden changes in nighttime light. These changes can be observed by satellites and used to estimate the spatial extent and severity of disaster impacts. To improve the observation frequency, researchers have to combine NTL images from different satellites. However, images acquired by different sensors may have different radiometric characteristics, making their brightness values difficult to compare directly. Therefore, radiometric intercalibration is required to make multi-source NTL images comparable. 


This practice introduces an automatic radiometric intercalibration method for high-resolution NTL images in disaster scenarios. Using SDGSAT-1 and Yangwang-1 as an example, the method converts SDGSAT-1 images into Yangwang-1-like images. This approach helps integrate multi-source nighttime light data and improves the temporal availability of NTL observations for disaster monitoring. The workflow is general and can be easily adapted to the intercalibration of other high-resolution nighttime light datasets from different sensors.

 

 

https://www.un-spider.org/node/13572
https://www.un-spider.org/node/13573
https://www.un-spider.org/network/regional-support-offices/wuhan-university-whu
Python

The objective of this practice is to provide a radiometric intercalibration method for integrating multi-source nighttime light images in disaster scenarios. This method can be used by disaster management agencies and other stakeholders to improve comparability of nighttime light images, thereby supporting rapid disaster assessment, emergency response, and post-disaster recovery monitoring in affected areas.

Recovery & Reconstruction
Relief & Response
Earthquake
Flood
Severe Storm
Turkey (Kahramanmaraş Earthquake, 2023)

The Recommended Practice was initially applied to the Turkey–Syria Earthquake using nighttime light images from SDGSAT-1 and Yangwang-1 as examples. SDGSAT-1 is an Earth observation satellite developed by the International Research Center of Big Data for Sustainable Development Goals (CBAS) to support the United Nations Sustainable Development Goals, and its glimmer imager (GLI) provides multi-color nighttime light observations including RGB bands with a spatial resolution of approximately 40 meters. Yangwang-1 is developed by Origin Space Corporation in China that provides panchromatic nighttime light imagery with a spatial resolution of approximately 38 meters.

For the analysis, the SDGSAT-1 and Yangwang-1 images were clipped to the area of interest (AOI) covering the disaster region and resampled to the spatial resolution of Yangwang-1 to ensure spatial consistency between the datasets. Using these two sensors as an example, this practice demonstrates a technically robust radiometric intercalibration workflow that can generate radiometrically consistent nighttime light imagery. The workflow is designed to be transferable and can be applied to the intercalibration of other high-resolution nighttime light datasets from different sensors.

Related Data: 

  • SDGSAT-1 (CBAS)
  • Yangwang-1 (Origin Space Corporation in China)

 

Wuhan University contact:

Dr. Xi Li
Professor, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing , Wuhan University

Deputy Director, Office of International Affairs, Wuhan University
Research team Webpage: http://nightlight.whu.edu.cn/en 

Head, China Regional Support Office, UN-SPIDER

Lead of GEO Initiative - GEO Night Light, Group on Earth Observations

E-mail: li_rs [at] 163.com (li_rs[at]163[dot]com), lixi [at] whu.edu.cn (lixi[at]whu[dot]edu[dot]cn)  


 

This practice can be applied to disaster events anywhere in the world. 

Step by Step：Angular Normalization of Daily Night-time Light Data

This Recommended Practice utilizes Python for processing NASA's Black Marble daily night-time light (NTL) data. The procedure involves data filtering, angular normalization to remove viewing zenith angle (VZA) effects, gap-filling, and calculating disaster recovery indices.

1. Data Acquisition:
  • Download the daily at-sensor TOA night-time radiance (VNP46A1) and daily moonlight-adjusted NTL (VNP46A2) from NASA's Black Marble product suite for your study area and timeframe (e.g., 1 month pre-disaster to several months post-disaster).
  • Download the GlobeLand30 land-cover dataset (30m resolution) to identify and extract urban built-up areas (artificial surfaces), as these areas concentrate human activity and artificial light.
 
2. Data Filtering & Quality Control:

To remove low-quality data and non-artificial light, apply the following strict selection criteria to each pixel:

  • Remove pixels with a solar zenith angle less than 108 degrees to eliminate solar illumination interference.
  • Filter out cloud-contaminated pixels using the QF cloud mask from the VNP46A2 product.
  • Perform a secondary selection using the mandatory quality flag to remove abnormal values.
  • Remove pixels with a moon illumination fraction above 60% to precisely identify cloud-polluted pixels.

3. Spatial Smoothing:
  • Apply a 3 × 3 moving window to calculate the average radiance. This alleviates geometric errors and image noise in the night-time light images.
Step 1: Angular Normalization


Satellite-observed NTL radiance has a strong nonlinear relationship with the Viewing Zenith Angle (VZA), causing significant time-series fluctuations. This step normalizes the radiance as if the VZA is always zero.


1. Principle:


The angular normalization algorithm is designed to remove the variations in observed night-time light radiance derived from changes in the Viewing Zenith Angle (VZA). Previous research identified a strong nonlinear relationship between night-time light radiance and VZA, which can be expressed as:

Figure 1

 

Where Z denotes the VZA, R denotes the night-time light radiance, and a, b, and c represent the coefficients. This model is called the Zenith-Radiance Quadratic (ZRQ) model.
The purpose of the normalization algorithm is to estimate the radiance time series assuming the VZA is equal to zero over time. Based on previous studies, we assume that the anisotropy of night-time light radiance remains constant if the land use of an area does not change over a short period. Therefore, the radiance in all directions will change by the same percentage even if the total light emission of the region changes. Based on this basic hypothesis, the radiance of night-time light over a period is modeled as:

 

Figure 2

 

Where R (Z,t) epresents the night-time light radiance under the VZA of Z at moment t , c (t)  is the actual radiance at moment t assuming the VZA is zero, (α'Z2 + b Z + 1) is the function changing with VZA, and α'  and b'  are the coefficients. This model effectively decomposes the satellite-observed time series radiance dynamic into two components: the real light emission changes (represented by the radiance at a VZA of zero, c(t) ), and the VZA-change-derived radiance observation due to the anisotropy.


2. Implementation Steps:


1) Define the Objective Function: The algorithm assumes that if land use remains unchanged, the anisotropy of NTL radiance remains consistent over a short period. The goal is to estimate the time series radiance c(t) at a VZA of zero. The objective function minimizes the correlation (R2) between the angle-normalized time series and the VZA using a Zenith-Radiance Quadratic (ZRQ) model.


2) Optimize and Solve:

  • Utilize the Nelder-Mead algorithm to minimize the objective function and solve for the required coefficients.
  • In Python, this can be implemented using the scipy.optimize.fmin package.

 

Fig. 1. The pixels of night-time light time series curves before and after the angular normalization in two different regions: (a) Arecibo; (b) Bayamon. Image Source: Jia et al. 2023 https://doi.org/10.1016/j.jag.2023.103359
Fig. 1. The pixels of night-time light time series curves before and after the angular normalization in two different regions: (a) Arecibo; (b) Bayamon.
Image Source: Jia et al. 2023 https://doi.org/10.1016/j.jag.2023.103359 

 

Step 2: Time Series Gap-Filling


After obtaining the angle-normalized time series T, we need to use an additive time series model named Prophet to gap-filling the missing data which makes the time series more complete.


The Prophet model is a generalized time series model that can handle various types of patterns, including seasonal and non-seasonal characteristics, which mainly includes the trend term, seasonal term, and the error term. The three terms are optimized by the L-BFGS algorithm to obtain the fitted value. We use the real observation data to fit the time series, and only fill in the fitted values of night-time light radiance at the missing moments thereby completing the time series gap-filling.


Due to the strict filtering criteria in the pre-processing stage (e.g., removing cloud or moonlight contaminated pixels), the resulting time series will have missing data points.

  • Apply the Prophet additive time series model to fill in the missing gaps.
  • The Prophet model handles seasonal and non-seasonal characteristics by optimizing trend, seasonal, and error terms using the L-BFGS algorithm. Fill in the fitted values only at the missing moments to complete the time series.

Step 3: Estimation of Power Restoration

Once the stable and continuous time series is generated, it can be used to assess disaster damage and track power recovery.


1. Power Supply Index (PSI): Calculate the PSI to quantify the current power supply relative to the pre-disaster baseline.

Figure 3
(Where TNLi  is the total night-time light radiance at time i, and TNLpre-disaster  is the stable total night-time light before the disaster).

 

2. Power Restoration Index (PRI): Calculate the PRI to measure resilience and the chronological progression of recovery from the maximum point of damage.

 

(Where represents the total night-time light at the most damaged moment).
(Where TNLdarkest  represents the total night-time light at the most damaged moment).

 

 

Fig. 2. Estimation of power supply index in Puerto Rico
Fig. 2. Estimation of power supply index in Puerto Rico: (a) non-angle-normalized time series estimation; (b) angle-normalized time series estimation.
Image Source: Jia et al. 2023 https://doi.org/10.1016/j.jag.2023.103359 
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In Detail: Angular Normalization of Daily Night-time Light Data

Satellite-observed night-time light (NTL) data is a widely utilized proxy for human activity and economic health. Following rapid-onset natural disasters such as hurricanes and earthquakes, disaster-affected regions often experience severe power outages, resulting in a sharp decline in NTL. While monthly or annual NTL composites obscure the chronological dynamics of disaster impact and recovery, daily NTL data offers the high temporal frequency necessary to track these rapid changes. However, daily data incorporates strong uncertainty—primarily the angular effect caused by variations in the satellite's viewing zenith angle (VZA)—which hinders accurate time-series analysis. This recommended practice introduces an angular normalization algorithm to generate a highly stable NTL time series, enabling highly accurate estimations of post-disaster power outages and their corresponding economic losses.

Background 

Evaluating the progress of United Nations Sustainable Development Goals (SDGs), particularly Goal 11 (sustainable cities and communities) and Goal 13 (climate action), requires accurate measurements of disaster-induced economic losses and infrastructure disruptions. Traditional in-situ investigations for statistical damage data are difficult, time-consuming, and sometimes impossible immediately following a severe disaster.
Compared to day-time remote sensing imagery, which struggles to directly capture socioeconomic dimensions like power outages and GDP , NTL remote sensing has a unique advantage in recording human activities. By utilizing the daily Black Marble product suite (VNP46A1 and VNP46A2) from the Suomi-NPP VIIRS sensor , this practice establishes a robust methodology to evaluate community resilience and recovery speeds based on electricity restoration.


Assessing Economic Impact via Night-time Light 

Disasters generate significant economic impacts extending far beyond physical, structural damage. Damaged electrical infrastructure forces residents to reduce or entirely halt industrial production and service activities, directly leading to a decline in Gross Domestic Product (GDP). This practice operates on the assumption that the loss rate of GDP in the industry and services sectors is strongly correlated to the loss rate of power supply. By quantifying the total night-time light loss rate, stakeholders can mathematically estimate the regional GDP loss rate.

This practice is globally applicable for monitoring power disruptions and tracking the recovery phases following major natural disasters, such as hurricanes, typhoons, and earthquakes. It provides vital decision-making evidence for authorities to allocate rescue resources prioritize infrastructure repairs in heavily affected municipalities, and evaluate a region's overall adaptive capacity to climate-related hazards.

Advantages

  • High Temporal Resolution: Utilizing daily NTL data captures precise and timely information on sudden electricity demand changes and the immediate impact of natural disasters, which monthly composites would obscure.
  • High Accuracy: The improved time series achieves a high Pearson correlation coefficient  with official power authority reports, proving it to be a highly reliable reflection of true power restoration.
  • Economic Proxy: Demonstrates a strong correlation between NTL loss and GDP loss in service and industry sectors.
     

Disadvantages

  • Assumption of Unchanged Land Cover: The angular normalization algorithm operates on the hypothesis that the region's land use does not change during the short observation period.
  • Resolution Limits: Currently evaluated at the regional/municipal scale; further optimization is needed to accurately assess disaster impacts at the micro/community scale.


GitHub repository

https://github.com/UN-SPIDER-Wuhan/ntl_angle_normalization.git 

  • Li, X., Ma, R., Zhang, Q., Li, D., Liu, S., He, T., Zhao, L., 2019. Anisotropic characteristic of artificial light at night—Systematic investigation with VIIRS DNB multi-temporal observations. Remote Sens. Environ. 233, 111357.
  • Román, M.O., Stokes, E.C., Shrestha, R., Wang, Z., ... & Enenkel, M., 2019. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One 14 (6), e218883.
  • Wang, Z., Román, M.O., Kalb, V.L., Miller, S.D., Zhang, J., Shrestha, R.M., 2021. Quantifying uncertainties in nighttime light retrievals from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data. Remote Sens. Environ. 263, 112557.
  • Jia, M., Li, X., Gong, Y., Belabbes, S., Dell'Oro, L., 2023. Estimating natural disaster loss using improved daily night-time light data. International Journal of Applied Earth Observation and Geoinformation, 120, 103359.
https://www.un-spider.org/node/13568
https://www.un-spider.org/advisory-support/recommended-practices/recommended-pr…

Recommended Practice: Angular Normalization of Daily Night-time Light Data

Image Source: Jia et al. 2023 https://doi.org/10.1016/j.jag.2023.103359 

Summary 

Natural disasters often cause severe damage to critical infrastructure, resulting in widespread power outages. Satellite-observed night-time light data has become a crucial tool to evaluate these disruptions and assess progress toward Sustainable Development Goals (SDGs) 11 and 13. However, daily night-time light time series often suffer from high uncertainty and fluctuations caused by the satellite's viewing zenith angle (VZA).


To address this, this Recommended Practice introduces a highly generalizable angular normalization algorithm to process VIIRS daily night-time light data. A key advantage of this universal method is its ability to effectively remove the angular effect during various disaster assessments, regardless of the specific hazard or geographic location. By mitigating these viewing angle fluctuations, the method generates a highly stable time series that accurately captures the abrupt decline in lighting after a disaster and the subsequent recovery process, allowing for a reliable estimation of power outages and proxy economic losses.

https://www.un-spider.org/node/13569
https://www.un-spider.org/node/13568
Python

The objective of this practice is to accurately identify the extent of power loss and monitor the chronological power restoration process in disaster-affected regions. This information can be used by government authorities, disaster management agencies, and humanitarian organizations to guide timely rescue efforts, allocate relief resources, and evaluate long-term infrastructure resilience.

Recovery & Reconstruction
Relief & Response
Earthquake
Flood
Severe Storm
Puerto Rico (Hurricane Maria, 2017). Turkey (Kahramanmaraş Earthquake, 2023). Pakistan (Monsoon Floods, 2023).

The Recommended Practice was initially applied to power outages caused by Hurricane Maria in Puerto Rico (2017) using daily night-time light data, and was subsequently utilized to assess the 2023 Kahramanmaraş Earthquake in Turkey and the 2023 Monsoon Floods in Pakistan. The disaster impacts were extracted from VIIRS Black Marble products (VNP46A1/A2) by utilizing an angular normalization algorithm and the Prophet model to ensure temporal continuity. Power disruption and recovery trajectories were assessed by calculating the Power Supply Index (PSI) and Power Restoration Index (PRI), which were further correlated to estimate regional GDP loss rates. The analysis specifically targets urban built-up areas extracted from 30-meter GlobeLand30 data and works best for regions with established electrical infrastructure.

 

Related Data: 

  • VIIRS Daily Black Marble Product Suite (NASA)
  • GlobeLand30 Land Cover Dataset (National Geomatics Center of China, NGCC)

 

Wuhan University contact:

Dr. Xi Li
Professor, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing , Wuhan University

Deputy Director, Office of International Affairs, Wuhan University
Research team Webpage: http://nightlight.whu.edu.cn/en 

Head, China Regional Support Office, UN-SPIDER

Lead of GEO Initiative - GEO Night Light, Group on Earth Observations

E-mail: li_rs [at] 163.com (li_rs[at]163[dot]com), lixi [at] whu.edu.cn (lixi[at]whu[dot]edu[dot]cn)  

This practice can be applied to evaluate power outages caused by sudden natural disasters globally. Because the methodology relies on tracking changes in artificial light emissions, it works best in urban built-up areas with concentrated populations and established electrical infrastructure.

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