The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite incorporates fire‐sensitive channels, including a dual‐gain high‐saturation temperature 4 µm channel, enabling active fire detection and characterization. The active fire product, based on the 750 m moderate resolution “M” bands of VIIRS, is one of the standard operational products generated by the Interface Data Processing Segment of the S‐NPP ground system. The product builds on an earlier “Collection 4” version of the algorithm used for processing Moderate Resolution Imaging Spectroradiometer (MODIS) data. Following postlaunch quality assessments and corrections in the input VIIRS Sensor Data Record data processing, an initial low detection bias was removed and the product achieved Beta quality in April 2012. Daily spurious detections along‐scan lines were also significantly reduced as a result of further processing improvements in October 2012. Direct product comparison with MODIS over 4 months of data in 2013 has shown that VIIRS produces approximately 26% more detections than MODIS within the central 3 pixel VIIRS aggregation zone of approximately ±31° scan angle range and 70% more detections outside of that zone, mainly as a result of the superior VIIRS scanning and sampling characteristics. Further development is in progress to ensure high‐quality VIIRS fire products that continue the MODIS data record and better serve the user community by delivering a full image classification product and fire radiative power retrievals. Research is also underway to take advantage of the radiometric signal from the 375 m VIIRS imager “I” bands.

We assessed the fire trends from Moderate Resolution Imaging Spectroradiometer (MODIS) (2003–2016) and Visible Infrared Imaging Radiometer Suite (VIIRS) (2012–2016) in South/Southeast Asia (S/SEA) at a country level and vegetation types. We also quantified the fire frequencies, anomalies and climate drivers. MODIS data suggested India, Pakistan, Indonesia and Myanmar as having the most fires. Also, the VIIRS-detected fires were higher than MODIS (AQUA and TERRA) by a factor of 7 and 5 in S/SEA. Thirty percent of S/SEA had recurrent fires with the most in Laos, Cambodia, Thailand, and Myanmar. Statistically-significant increasing fire trends were found for India (p = 0.004), Cambodia (p = 0.001), and Vietnam (p = 0.050) whereas Timor Leste (p = 0.004) had a decreasing trend. An increasing trend in fire radiative power (FRP) were found for Cambodia (p = 0.005), India (0.039), and Pakistan (0.06) and declining trend in Afghanistan (0.041). Fire trends from VIIRS were not significant due to limited duration of data. In S/SEA, fires in croplands were equally frequent as in forests, with increasing fires in India, Pakistan, and Vietnam. Specific to climate drivers, precipitation could explain more variations in fires than the temperature with stronger correlations in Southeast Asia than South Asia. Our results on fire statistics including spatial geography, variations, frequencies, anomalies, trends, and climate drivers can be useful for fire management in S/SEA countries.

Paddy Rice is the prevalent land cover in the mosaicked landscape of the Hanoi Capital Region, Vietnam. In this study, we map double and single crop rice in Hanoi using a random forest algorithm and a time-series of Sentinel-1 SAR imagery at 10 and 20 m resolution using VV-only, VH-only, and both polarizations. We compare spatial and areal variation and quantify input band importance, estimate crop growth stages, estimate rice field/collective metrics using Fragstats with image segmentation, and highlight the importance of the results for land use and land cover. Results suggest double crop rice ranged from 208 000 to 220 000 ha with 20-m resolution imagery accounting for the most area in all polarizations. Based on accuracy assessment, we found 10 m data for VV/VH to have highest overall accuracy (93.5%, ±1.33%), while VV at 10 and 20 m had lowest overall accuracies (90.9%, ±1.57; 91.0%, ±2.75). Mean decrease in accuracy suggests for all but VV at 10 m, data from harvest and flooding stages are most critical for classification. Results suggest 20 m data for both VV and VH overestimates rice land cover, however 20 m data may be indicative of rice land use. Analysis of growing season suggests average estimated length of 93-104 days for each season. Commune-level results suggest up to 20% coefficient of variation between VV10m and VH10m with significant spatial variation in rice area. Landscape metrics show rice fields are typically planted in groups of 3-4 fields with over 796 000 collectives and 2.69 million fields estimated in the study area.