Robust disaster impact assessment with synthetic control modeling framework and daily nighttime light time series images

Remotely sensed nighttime lights (NTL) are widely used as a proxy for human activity. A key application is assessing disaster impacts, but its potential has been limited by uncertainties in estimating baseline NTL intensity (the counterfactual without disasters) and challenges in isolating disaster impacts from other factors influencing NTL variation. To address these challenges, we used a synthetic control modeling framework with daily NTL images from NASA's Black Marble VIIRS product suite. We enhanced the traditional model by optimizing donor selection with the Dynamic Time Warping algorithm and incorporating random forest regression to better capture target-donor relationships. Testing on 20 severe disasters across diverse contexts, our model outperformed existing methods, achieving a correlation coefficient of 0.94 and a covariate difference of just 0.47%. It also excelled at detecting low-intensity and short-term disaster impacts often missed by other methods. The resulting metrics—impact duration, intensity, and severity—revealed significant regional variations in disaster resilience and coping capacity. This model provides valuable insights for disaster relief and supports broader climate resilience and sustainability efforts.