iSEAM: Improving the Blooming Effect Adjustment for DMSP-OLS Nighttime Light Images by Considering Spatial Heterogeneity of Blooming Distance

Abstract

The longest archive makes DMSP-OLS nighttime light (NTL) images unparalleled in relevant time series studies. However, these studies have been constrained by the blooming effect. The self-adjusting model (SEAM) proposed in 2019 solves this problem to some extent. However, SEAM assumed all pixels in NTL images with a constant blooming distance 3.5 km. In fact, the blooming distance is related to the land covers and the brightness of artificial lights. This assumption leads to large errors in cities that have blooming distance different from 3.5 km. To address this problem, this study proposed an improved SEAM model (iSEAM) by considering spatial heterogeneity of blooming distance. Specifically, iSEAM segmented the DMSP-OLS image to obtain light objects and then employed the random forest method to estimate the effective blooming distance for each light object, and then corrected the blooming effect of all pixels in each light object by a modified pixel brightness interactive model. The test in China shows that the blooming distance ranges from 0 to 12.55 km in China, with an average 3.36 km. The correlation coefficient (R) between the images corrected by iSEAM and the NPP-VIIRS images reaches 0.70 that is higher than other blooming effect correction methods. Moreover, the corrected images by iSEAM have higher spatial heterogeneity than other methods. These results suggest that by considering the spatial heterogeneity of effective blooming distance, iSEAM can serve as a more accurate and effective method to correct the blooming effect of DMSP-OLS NTL images.

A consistent and corrected nighttime light dataset (CCNL 1992–2013) from DMSP-OLS data

“…The final result was obtained by subtracting the brightness value caused by the blooming impact from the original brightness value. To alleviate saturation’s influence on blooming effect correction, we performed the saturation effect correction before blooming effect correction on DMSP-OLS NTL images 27 .…”

“…Zhuo et al . 27 proposed an improved SEAM model (iSEAM) considering spatial heterogeneity of effective blooming distance while introducing land cover data. Based on the above methods, some global or regional NTL data products were generated to overcome one or more of these problems 14 , 28 – 31 .…”

Research on correction method of saturation interference data of space heterodyne spectrometer

“…Using the interference data acquired by the InGaAs detector [5] applied to atmospheric CO2 concentration detection as the research object, unlike the "overflow phenomenon" observed when CCD pixels were saturated [6], the responses of the individual pixels of the InGaAs detector do not interfere with each other. Without considering the nonlinear response of pixels near saturation values, traditional detectors' saturation value corrections often use linear or nonlinear fitting methods [7]. These methods fit the non-saturation value changes near the saturation value to predict the saturation value but lack a physical explanation for the detection system.…”

A Novel Consistency Calibration Method for DMSP-OLS Nighttime Stable Light Time-Series Images

“…The success of DMSP-OLS also promoted the development of new generation nighttime light platforms and products, such as Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer (NPP-VIIRS), EROS-B, Aerocube-5, CUMULOS, Luojia 1-01, and Jilin-1. Although these new data have advantages in portraying urban spatial morphology and structures with high spatial and temporal resolutions [11]- [13], the DMSP-OLS nighttime light data, especially the nighttime stable light (NSL) products remain irreplaceable for many studies such as the study of the urbanization process, because it has the longest time span in currently available nighttime light data sources [14], [15]. However, the NSL time-series product suffers from inconsistency problem, i.e., the image data of different years cannot be compared with each other directly, due to shortcomings of the OLS sensors, such as lack of on-board calibration, the presence of the saturation effect [16]- [18], and being carried on different satellite platforms [19].…”