The earth surface is monitored periodically by numerous satellite sensors which have different spectral response functions, image acquisition heights, atmosphere correction schemes, overpass times, and sun/view angle geometries. Temporal and spatial variations of land surface properties, such as vegetation index, Leaf Area Index (LAI), land surface temperature, and soil moisture, have been provided by long-term time series of various remote sensing datasets. Inter-sensor translation equations are required to build long-term time series by the combination of multiple sensors from historical to advanced and new satellite datasets. In the first chapter, inter-sensor translation equations of band reflectances and two vegetation indices (e.g. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) were derived using linear regression equations relative to Moderate Resolution Imaging Spectroradiometer (MODIS) values. The consistency and validation of inter-sensor transforms were investigated through statistical student's t-test and the root mean square error (RMSE).In the second chapter, cross-sensor extension of EVI and a 2-band EVI (without the blue band; EVI2) were investigated based on the continuity of both EVI's. Sensor specific red-blue coherencies were examined for the possibility of the EVI and EVI2 extension from MODIS sensor. The EVI continuity to MODIS was particularly problematic for the Visible Infrared Imager / Radiometer Suite (VIIRS) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) that have dissimilar blue bands from that of MODIS. The cross-sensor extension and compatibility of EVI2 were improved and provided the possibility to be lengthened to the Advanced Very High Resolution Radiometer (AVHRR) using its translation equation.Finally, we evaluated the use of sensor-specific EVI and NDVI data sets, using a time sequence of Hyperion images over Amazon rainforest in Tapajos National Forest, Brazil for the 2001 and 2002 dry seasons. We computed NDVI, EVI, and EVI2 with the convolution data of different global monitoring and high temporal resolution sensor systems (AVHRR, MODIS, VIIRS, SPOT-VGT, and SeaWiFS) from Hyperion, and evaluated their spectral deviations and continuity in the characterization of tropical forest phenology. Our analyses show that EVI2 maintains the desirable properties of increased sensitivity in high biomass forests across all sensor systems evaluated.

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A methodology is presented to evaluate the accuracy of cloud cover fraction (CCf) forecasts generated by numerical weather prediction (NWP) and climate models. It is demonstrated with a case study consisting of simulations from theWeather Research and Forecasting (WRF) model. In this study, since the WRF CCf forecasts were initialized with reanalysis fields from the North American Mesoscale (NAM) Forecast System, the characteristics of the NAM CCf products were also evaluated. The procedures relied extensively upon manually-generated, binary cloud masks created from VIIRS (Visible Infrared Imager Radiometry Suite) imagery, which were subsequently converted into CCf truth at the resolution of the NAM and WRF gridded data. The initial results from the case study revealed biases toward under-clouding in the NAM CCf analyses and biases toward over-clouding in the WRF CCf products. These biases were evident in images created from the gridded NWP products when compared to VIIRS imagery and CCf truth data. Thus, additional simulations were completed to help assess the internal procedures used in the WRF model to translate moisture forecast fields into layered CCf products. Two additional sets of WRF CCf 24 h forecasts were generated for the region of interest using WRF restart files. One restart file was updated with CCf truth data and another was not changed. Over-clouded areas in the updated WRF restart file that were reduced with an update of the CCf truth data became over-clouded again in the WRF 24 h forecast, and were nearly identical to those from the unchanged restart file. It was concluded that the conversion of WRF forecast fields into layers of CCf products deserves closer examination in a future study.

Results are presented from the 2021 test run of active fire detection using the Visual Infrared Imaging Radiometer Suite (VIIRS) instrument, that is currently onboard the polar satellites Suomi-NPP and NOAA-20. The test is performed by the Swedish Civil Contingencies Agency and the Swedish Meteorological and Hydrological Institute, in cooperation with local fire departments in Sweden. The aim of this report was to study the impact of clouds on the ability of active fire detection, as well as to identify objects that potentially can cause commission errors in the VIIRS 375 m active fire algorithm (false positive notifications). Also, the study aimed to investigate what may cause omission errors in the algorithm, and to show to what extent the detections can be used to represent the true time development of the wildfire front and burned area. Using a cloudmask and a cloudtype classification product from Nowcasting Satellite Application Facilities (NWC SAF), the impact of clouds was anlalyzed by comparing the cloud data with the obtained fire notifications from the satellites. Active fires and newly burned areas were also studied using Sentinel-2 imagery, specifically the False Color Urban and the Short Wave Infrared (SWIR) RGB composites, as well as images from the 842 nm band, making use of the relatively high spatial resolution as well as the spectral signatures of fire and newly burned vegetation. Detection of active fires occurred in both cloud free and completely cloud covered conditions. How-ever, roughly 70% of the detected vegetation fire pixels were obtained in conditions with 20% clouds or less. / I rapporten presenteras resultat från 2021 års test av satellitdetektering av skogs- och vegetationsbränder, ett test som genomförs av MSB och SMHI i samverkan med kommunala räddningstjänster. De två satelliter som ingår i testet (Suomi-NPP och NOAA-20) går i polära omloppsbanor och är utrustade med instrumentet Visible Infrared Imaging Radiometer Suite (VIIRS). Detta projekt syftade till att undersöka hur molnighet påverkar möjligheten till detektion med satellit, vilka objekt som potentiellt kan ge upphov till falska detektioner samt vad som kan orsaka uteblivna satellitdetektioner. Ytterligare ett mål med rapporten var att genom fallstudier av större bränder undersöka i vilken utsträckning satellitdetektionerna kan användas för att representera brandfrontens utveckling med tiden och brandens faktiska utbredning. Vid studierna av molnighet analyserades en molnmask och en molnklassificeringsprodukt från Nowcasting Satellite Application Facilities (NWC SAF). I utvärderingen användes även data från Sentinel-2 för att studera pågående bränder och avbränd yta, som syns tydligt i RGB-kompositerna False Color Urban och Short Wave Infrared (SWIR) och i 842 nm-bandet, tack vare den relativt höga bildupplösningen och den nyligen avbrända ytans spektralsignatur. Brand detekterades i både molnfria och helt molntäckta förhållanden. Drygt 70 % av detektionerna vid vegetationsbrand kom emellertid i förhållanden med 20 % moln eller mindre.

An accurate estimation of biomass burning emissions is in part limited by the lack of knowledge of fire burning phase (smoldering/flaming). In recent years, several fire detection products have been developed to provide information of fire radiative power (FRP), location, size, and temperature of fire pixels, but no information regarding fire burning phase is retrieved. The Day-Night band (DNB) aboard Visible Infrared Imaging Radiometer Suite (VIIRS) is sensitive to visible light from flaming fires in the night. In contrast, VIIRS 4 µm moderate resolution band #13 (M13), though capable to detect fires at all phases, has no direct sensitivity for discerning fire phase. However, the hybrid usage of VIIRS DNB and M-bands data is hampered due to their different scanning technology and spatial resolution. In this study, we present a novel method to rapidly and accurately resample DNB pixel radiances to M-band pixels’ footprint that is based on DNB and M-band’s respective characteristics in their onboard schemes for detector aggregation and bow-tie effect removals. Subsequently, the visible energy fraction (VEF) as an indicator of fire burning phase is introduced and is calculated as the ratio of visible light power (VLP) and FRP for each fire pixel retrieved from VIIRS 750 m active fire product. A global distribution of VEF values, and thereby the fire phase, is quantitatively obtained, showing mostly smoldering wildfires such as peatland fires (with smaller VEF values) in Indonesia, flaming wildfires (with larger VEF values) over grasslands and savannahs in sub-Sahel region, and gas fares with largest VEF values in the Middle East. VEF is highly correlated with modified combustion efficiency (MCE) for different land cover types or regions. These results together with a case study of the 2018 California Campfire show that the VEF has the potential to be an indicator of fire combustion phase for each fire pixel, appropriate for estimating emission factors at the satellite pixel level.