Recently, the United States Geological Survey (USGS) has released a new dataset, called Landsat Analysis Ready Data (ARD), which is designed specifically for facilitating time series analysis. In this study, we evaluated the temporal consistency of this new dataset and recommended several processing streamlines for improving data consistency. Specifically, we examined the impacts of data resampling, cloud/cloud shadow detection, Bidirectional Reflectance Distribution Function (BRDF) correction, and topographic correction on the temporal consistency of the Landsat Time Series (LTS). We have four major observations. First, single-resampled data (ARD) are generally more consistent than double-resampled data (re-projected Collection 1 data), but the difference is very minor. Second, the improved cloud and cloud shadow detection approach (e.g., Fmask 4.0 vs. 3.3) moderately increased data consistency. Third, BRDF correction contributed the most in making LTS consistent. Finally, we corrected the topographic effects by using several widely used algorithms, including Sun-Canopy-Sensor (SCS), a semiempirical SCS (SCS+C), and Illumination Correction (IC) algorithms, however they were found to have very limited or even negative impacts on the consistency of LTS. Therefore, we recommend using Landsat ARD with the improved cloud and cloud shadow detection approach (Fmask 4.0), and with BRDF correction for routine time series analysis.

Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which is especially helpful for wildfire rescue and management. Historically, images obtained from sun-orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) were used to detect active fire and burned area at the large spatial scale. However, the daily revisit cycles make them inherently unable to extract FSR in near real­-time (hourly or less). We argue that the Himawari-8, a next generation geostationary satellite with a 10-min temporal resolution and 0.5–2 km spatial resolution, may have the potential for near real-time FSR extraction. To that end, we propose a novel method (named H8-FSR) for near real-time FSR extraction based on the Himawari-8 data. The method first defines the centroid of the burned area as the fire center and then the near real-time FSR is extracted by timely computing the movement rate of the fire center. As a case study, the method was applied to the Esperance bushfire that broke out on 17 November, 2015, in Western Australia. Compared with the estimated FSR using the Commonwealth Scientific and Industrial Research Organization (CSIRO) Grassland Fire Spread (GFS) model, H8-FSR achieved favorable performance with a coefficient of determination (R²) of 0.54, mean bias error of –0.75 m/s, mean absolute percent error of 33.20% and root mean square error of 1.17 m/s, respectively. These results demonstrated that the Himawari-8 data are valuable for near real-time FSR extraction, and also suggested that the proposed method could be potentially applicable to other next generation geostationary satellite data.