Husi Letu

A Saturated Light Correction Method for DMSP/OLS Nighttime Satellite Imagery

Several studies have clarified that electric power consumption can be estimated from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) stable light imagery. As digital numbers (DNs) of stable light images are often saturated in the center of city areas, we developed a saturated light correction method for the DMSP/OLS stable light image using the nighttime radiance calibration image of the DMSP/OLS. The comparison between the nonsaturated part of the stable light image for 1999 and the radiance calibration image for 1996-1997 in major areas of Japan showed a strong linear correlation (R2 = 92.73) between the DNs of both images. Saturated DNs of the stable light image could therefore be corrected based on the correlation equation between the two images. To evaluate the new saturated light correction method, a regression analysis is performed between statistic data of electric power consumption from lighting and the cumulative DNs of the stable light image before and after correcting for the saturation effects by the new method, in comparison to the conventional method, which is, the cubic regression equation method. The results show a stronger improvement in the determination coefficient with the new saturated light correction method (R2 = 0.91, P = 1.7 ·10-6 <; 0.05) than with the conventional method (R2 = 0.81, P = 2.6 ·10-6 <; 0.05) from the initial correlation with the uncorrected data (R2 = 0.70, P = 4.5 · 10-6 <; 0.05). The new method proves therefore to be very efficient for saturated light correction.

Development of a daytime cloud and haze detection algorithm for Himawari‐8 satellite measurements over central and eastern China

Cloud detection by passive satellite sensors is very challenging in hazy weather over China because the reflective characteristics of haze and clouds are very similar. Consequently, hazy areas tend to be mistaken as cloudy or clear areas by current cloud mask algorithms. The Advanced Himawari Imager (AHI) aboard Himawari-8 is a multispectral earth observation sensor with high temporal and spatial resolutions. A cloud and haze detection algorithm for AHI measurements is urgently needed for monitoring atmospheric pollution and its transport over China. This study presents the new Himawari-8 Cloud and Haze Mask (HCHM) algorithm that classifies image pixels from central and eastern China into one of three categories: clear, cloudy or hazy. Based on the observations that haze occurs near the ground and accumulates in low-elevation plains and basins while clouds form at high altitudes, the proposed HCHM algorithm incorporates altitude information to adjust the thresholds used in the selected threshold tests to separate haze and cloud pixels. We find that combining auxiliary digital elevation model (DEM) data with traditional indicators, such as the R0.86/R0.64, R0.86/R1.6 and BT11-BT3.9, improves the accuracy of cloud and haze discrimination. The HCHM algorithm is applied to Himawari-8 observations from Aug. 2015, Nov. 2015, Jan. 2016 and May 2016 and validated against the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical feature mask (VFM) results. The validation shows that the average leakage rate (LR), false alarm rate (FAR) and haze missing rate (HMR) of the HCHM algorithm are 3.95%, 5.88% and 4.17%, respectively.

Development of an ice crystal scattering database for the global change observation mission/second generation global imager satellite mission: investigating the refractive index grid system and potential retrieval error

Computing time and retrieval error of the effective particle radius are important considerations when developing an ice crystal scattering database to be used in radiative transfer simulation and satellite remote sensing retrieval. Therefore, the light scattering database should be optimized based on the specifications of the satellite sensor. In this study, the grid system of the complex refractive index in the 1.6 μm (SW3) channel of the Global Change Observation Mission/Second Generation Global Imager satellite sensor is investigated for optimizing the ice crystal scattering database. This grid system is separated into twelve patterns according to the step size of the real and imaginary parts of the refractive index. Specifically, the LIght Scattering solver Applicable to particles of arbitrary Shape/Geometrical-Optics Approximation technique is used to simulate the scattering of light by randomly oriented large hexagonal ice crystals. The difference of radiance with different step size of the refractive index is calculated from the developed light scattering database using the radiative transfer (R-STAR) solver. The results indicated that the step size of the real part is a significant factor in difference of radiance.

Wetlands Mapping in North America by Decision Rule Classification Using MODIS and Ancillary Data

An up-to-date wetlands map based on remote sensing data at a continental scale is urgently needed for estimating global environmental change. In this study, a wetlands map of North America was developed using Moderate Resolution Imaging Spectroradiometer (MODIS) data obtained in 2008 and ancillary data. For this purpose, a decision rule classification method was developed relied upon the hierarchical characteristics of land types and prior knowledge about the geographical location of wetlands. Two hierarchical levels of land types were used to extract wetlands. At the first level, non-vegetation land types including water, snow, urban, and bare areas were separately extracted from vegetation land types using threshold methods. At the second level, wetlands were discriminated from non-wetland vegetation land types with the MODIS tasseled cap (brightness, greenness, and wetness) indices using the decision tree method. In addition, elevation data were used to build the elevation mask and a climate map was used to subdivide the study area into five sub-regions. In the quantitative accuracy assessment, users and producers accuracies of wetlands for the whole study area were calculated as 80.3% and 83.7%, respectively. In a comparison with two existing global land cover datasets, GLC2000 and IGBP DISCover, our results show significant improvement in extracting coastal and narrow types of wetlands. This study indicates that decision rule classification, integrated with multi-temporal MODIS data and ancillary data, is useful to develop an improved wetlands map at a continental scale.

Diurnal cycle and seasonal variation of cloud cover over the Tibetan Plateau as determined from Himawari-8 new-generation geostationary satellite data

Analysis of cloud cover and its diurnal variation over the Tibetan Plateau (TP) is highly reliant on satellite data; however, the accuracy of cloud detection from both polar-orbiting and geostationary satellites over this area remains unclear. The new-generation geostationary Himawari-8 satellites provide high-resolution spatial and temporal information about clouds over the Tibetan Plateau. In this study, the cloud detection of MODIS and AHI is investigated and validated against CALIPSO measurements. For AHI and MODIS, the false alarm rate of AHI and MODIS in cloud identification over the TP was 7.51% and 1.94%, respectively, and the cloud hit rate was 73.55% and 80.15%, respectively. Using hourly cloud-cover data from the Himawari-8 satellites, we found that at the monthly scale, the diurnal cycle in cloud cover over the TP tends to increase throughout the day, with the minimum and maximum cloud fractions occurring at 10:00 a.m. and 18:00 p.m. local time. Due to the limited time resolution of polar-orbiting satellites, the underestimation of MODIS daytime average cloud cover is approximately 4.00% at the annual scale, with larger biases during the spring (5.40%) and winter (5.90%).

Relationship between DMSP/OLS nighttime light and CO2 emission from electric power plant

In this study, we estimated the CO2 emission by fossil fuel consumption from electric power plant using DMSP stable light image for 1999 after correction for saturation effect. Digital number (DNs) of the stable light image in center of city areas are saturated for the strong nighttime intensity and characteristic of the OLS satellite sensor. To estimate the CO2 emission using stable light image, saturation light correction method was developed by using DMSP radiance calibration image, which has not included saturation pixel in city areas. Then, regression analysis was performed with cumulative DNs of the corrected stable light image, electric power consumption, electric power generation and CO2 emission by fossil fuel consumption from electric power plant each other. Results indicated that there are good relationship (R2<90%) between DNs of the corrected stable light image and other parameters. Finally, we estimated the CO2 emission from electric power plant using corrected stable light image.

Validation of the wetlands map derived from MODIS imagery in North America

As wetlands are among the most important ecosystems in the world, it is becoming increasingly important to develop a wetlands map at continental or global scale. A wetlands map in North America was produced using 500 m MODIS data obtained in 2008. To assess the accuracy of the map, the quantitative accuracy assessment was performed. A stratified random sampling method was applied to collect the validation point. A total of 2400 sampling pixels were used for the accuracy assessment. The overall accuracy of the map was assessed at 80.3%. Furthermore, the wetlands map was also compared with the existing global land cover products GLC2000 and IGBP DISCover. Three wetland sites designated in the Ramsar Convention were used to compare with Landsat images. As a result, the spatial distributions of wetlands in the new map were closest to those were in Landsat images. The new map also gave more detailed spatial information on wetlands especially in the transition zone between aquatic and terrestrial area. This study indicates that MODIS data are capable for developing an improved wetlands map at a global scale.

On the cloud observations in JAXA's next coming satellite missions

The use of JAXA’s next generation satellites, the EarthCARE and the GCOM-C, for observing overall cloud systems on the Earth is discussed. The satellites will be launched in the middle of 2010-era and contribute for observing aerosols and clouds in terms of climate change, environment, weather forecasting, and cloud revolution process study. This paper describes the role of such satellites and how to use the observing data showing concepts and some sample viewgraphs. Synergistic use of sensors is a key of the study. Visible to infrared bands are used for cloudy and clear discriminating from passively obtained satellite images. Cloud properties such as the cloud optical thickness, the effective particle radii, and the cloud top temperature will be retrieved from visible to infrared wavelengths of imagers. Additionally, we are going to combine cloud properties obtained from passive imagers and radar reflectivities obtained from an active radar in order to improve our understanding of cloud evolution process. This is one of the new techniques of satellite data analysis in terms of cloud sciences in the next decade. Since the climate change and cloud process study have mutual beneficial relationship, a multispectral wide-swath imagers like the GCOM-C SGLI and a comprehensive observation package of cloud and aerosol like the EarthCARE are both necessary.

Cloud sciences using satellite remote sensing, cloud growth model, and radiative transfer.

In recent years, it has been revealed that the cloud microphysical properties such as cloud particle radii obtained from satellite remote sensing were of apparent values. A combined use of passive and active sensor has gradually revealed about what we observed using passive imager thorough the vertical information of clouds obtained from active sensors. For understanding the process of cloud growth in nature, model that simulates cloud droplet growth is also needed. Observation results obtained from the satellite remote sensing are used for validating model such as cloud resolving model and spectral-bin microphysics cloud model. Vice-versa, models are used for understanding the process that are hidden in satellite-remote sensing results. We are aiming consistent understanding of clouds with observation and modeling. In this paper, we will introduce a preliminary result of multi-sensor view of warm water clouds and we will review our research strategy of cloud sciences, using satellite remote sensing, the cloud growth model, and the radiative transfer.

Assessment of stable light derived from DMSP/OLS night‐time imagery

A stable light image for south‐eastern Asia was extracted from the Defense Meteorological Satellite Programs Operational Linescan System (DMSP/OLS) night‐time imagery for 1999. The accuracy assessment of the stable light image has been completed using two methods: a reference data‐based comparison and a stratified random sampling method. The stable light image was compared with the conventional stable light image for 1999 and the Landsat ETM+ 2000 image for the Kanto region of Japan. The results show that the digital numbers of the new stable light image (almost <20) in the non‐urban area are much lower than those of the conventional stable light image (almost >60) because the new stable light image includes little noise. The stratified random sampling method could assess the accuracy for both the new stable light image and the conventional stable light image in Asia by classifying the images into stable and non‐stable light areas.