As the 10 year Moderate Resolution Imaging Spectroradiometer Land Surface Temperature MODIS LST becomes available, it is significant to perform a comprehensive evaluation on the long-term product before downstream users use it for climate studies and atmospheric models. In this study, a validation is carried out using observations from the US Surface Radiation budget (SURFRAD) network. Strict quality control removes cloud-contaminated samples from MODIS LST collection and decreases noise information from SURFRAD measurements, thereby making the validation more persuasive. With analysis on 19,735 valid samples, Aqua/MODIS LST from a split-window algorithm shows retrieval errors from –14 K to 17 K with a bias of –0.93 K, an RMSE of 2.65 K, and a standard deviation of 2.48 K. The errors also show strong seasonal signals. With correlation tests between LST errors and several other factors, it is disclosed that LST retrieval errors mainly come from atmospheric effects and surface emissivity uncertainties, which are closely related to relative air humidity, absolute air humidity, sensor zenith angle, wind speed, normalized difference vegetation index (NDVI), and soil moisture. In addition, the impacts from these factors may not be independent. These impact factors suggest a deficiency of the split-window algorithm in dealing with atmospheric and surface complexity and variety.

Sea fog is a problematic weather phenomenon for marine transportation and navigation. Lacking ground observations, sea fog monitoring mainly depends on meteorological and environmental satellites because they provide large swaths of data with good spatial and temporal resolution. The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites provides several new features for sea fog detection because of the more available channels. In addition to the traditional variable-dual channel difference (DCD_IR), which is widely used to detect sea fog and stratus clouds at night, this study uses and analyses several other variables including NDSI (normalized difference snow index), BTD_back (brightness temperature difference in the thermal infrared channel between a sea fog/stratus cloud pixel and nearby clear-sky ocean surface), NWVI (normalized difference near-infrared water vapour index) and D_NWVI (NWVI difference between a possible sea fog/stratus cloud pixel and nearby clear-sky ocean surface), for all seasons. BTD_back, NWVI, and D_NWVI show outstanding ability to discriminate between sea fog and stratus clouds. Automatic sea fog detection algorithms are developed using these variables for both daytime and night time with Terra/MODIS data based on a threshold scheme. During development of the algorithms, a series of data processes are also considered to maintain stable performance of the algorithms over wide areas and in all seasons. The algorithms are applied to Terra/MODIS data at a semi-operational mode from 2007 to 2013 and show promising results. Validation with data from field campaigns, one buoy station with good maintenance, 18 weather stations, and CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization)/CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) demonstrates the accuracy of the algorithms. The sea fog detection results are highly consistent with fog observations from the field campaign data. The validation with the buoy station data shows an overall accuracy of 90% under all weather conditions, an accuracy of 86% during foggy weather condition, and a KSS (Hanssen–Kuiper Skill Score) of 0.81. The validation with two-year data from 18 weather stations and CALIOP/CALIPSO over the Bohai Sea and Yellow Sea shows accuracy of 76.3% and 77.9%, respectively. The promising results indicate high probability of applying the algorithms in operational systems over the oceans adjacent to China or even wider oceans using Terra/MODIS data.

Cloud shadows are a major problem in the detection of flood/standing water using satellite data. Because cloud shadows and flood/standing water have similar spectral characteristics, the traditional means of detection based on spectral properties may fail to distinguish them from each other accurately. Because clouds cast shadows over land, this phenomenon can be analysed using the geometric correlations between clouds and cloud shadows; thus, this method might detect cloud shadows. Based on this concept, geometric relationships were established between clouds and their shadows using satellite data and satellite-solar geometries. Furthermore, an iterative method combining geometric and spectral properties was developed to automatically remove cloud shadows from flood/standing water in satellite maps. This method was applied and tested using MSG/SEVIRI (Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager) data and continues to show promising and consistent results.

Since 2 June 2020, unusual heavy and continuous rainfall from the Asian summer monsoon rainy season caused widespread catastrophic floods in many Asian countries, including primarily the two most populated countries, China and India. To detect and monitor the floods and estimate the potentially affected population, data from sensors aboard the operational polar-orbiting satellites Suomi National Polar-Orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration (NOAA)-20 were used. The Visible Infrared Imaging Radiometer Suite (VIIRS) with a spatial resolution of 375 m available twice per day aboard these two satellites can observe floodwaters over large spatial regions. The flood maps derived from the VIIRS imagery provide a big picture over the entire flooding regions, and demonstrate that, in July, in China, floods mainly occurred across the Yangtze River, Hui River and their tributaries. The VIIRS 5-day composite flood maps, along with a population density dataset, were combined to estimate the population potentially exposed (PPE) to flooding. We report here on the procedure to combine such data using the Zonal Statistic Function from the ArcGIS Spatial Analyst toolbox. Based on the flood extend for July 2020 along with the population density dataset, the Jiangxi and Anhui provinces were the most affected regions with more than 10 million people in Jingdezhen and Shangrao in Jiangxi province, and Fuyang and Luan in Anhui province, and it is estimated that about 55 million people in China might have been affected by the floodwaters. In addition to China, several other countries, including India, Bangladesh, and Myanmar, were also severely impacted. In India, the worst inundated states include Utter Pradesh, Bihar, Assam, and West Bengal, and it is estimated that about 40 million people might have been affected by severe floods, mainly in the northern states of Bihar, Assam, and West Bengal. The most affected country was Bangladesh, where one third of the country was underwater, and the estimated population potentially exposed to floods is about 30 million in Bangladesh.

Terrain shadow is a big challenge to land products such as flood extent and snow cover from moderate-resolution optical satellite data. Because terrain shadows share similar spectral features with floodwaters, they can be easily detected as floodwaters by flood detection algorithms based on spectral features in visible, near-infrared, and short-wave infrared channels, which decreases the accuracy of flood detection substantially. However, because terrain shadows appear in mountainous areas with large surface roughness while floodwaters accumulate in low-lying areas with small surface roughness, analysis on surface roughness between terrain shadows and floodwaters can be very effective to distinguish one from the other. Root-mean-square height, internal height difference, and external height difference are used as principal quantitative surface roughness parameters in this study and calculated upon water objects that are clustered from a group of adjacent water pixels. This object-based method is applied in terrain shadow removal from SNPP/VIIRS (Suomi National Polar Orbit Partnership/Visible Infrared Imager Radiometer Suite) near-real-time flood maps and shows promising results according to the tests with 10,000+ VIIRS granules across global areas. Quantitative evaluation in the northwest of the USA also indicates that more than 99% terrain shadow pixels could be removed from VIIRS flood maps by this method, which significantly improves the accuracy of near-real-time flood detection from SNPP/VIIRS imagery.

Hurricane Harvey made landfall as a Category-4 storm in the United States on 25 August 2017 in Texas, causing catastrophic flooding in the Houston metropolitan area and resulting in a total economic loss estimated to be about $125 billion. To monitor flooding in the areas affected by Harvey, we used data from sensors aboard the Suomi National Polar-Orbiting Partnership Satellite (SNPP) and the new Geostationary Operational Environmental Satellite (GOES)-16. The GOES-16 Advanced Baseline Imager (ABI) observations are available every 5 min at 1-km spatial resolution across the entire United States, allowing for the possibility of frequent cloud free views of the flooded areas; while the higher resolution 375-m imagery available twice per day from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the SNPP satellite can observe more details of the flooded regions. Combining the high spatial resolution from VIIRS with the frequent observations from ABI offers an improved capability for flood monitoring. The flood maps derived from the SNPP VIIRS and GOES-16 ABI observations were provided to the Federal Emergency Management Agency (FEMA) continuously during Hurricane Harvey. According to FEMA’s estimate on 3 September 2017, approximately 155,000 properties might have been affected by the floodwaters of Hurricane Harvey.