Impact of the screen configurations on the estimated Terra MODIS SD degradation

2021 ◽  
Author(s):  
Sarah Henderson ◽  
Kevin Twedt ◽  
Xiaoxiong Xiong
Keyword(s):  
Terra Modis

Spring leads in the Beaufort Sea and its interannual trend using Terra/MODIS thermal imagery

2021 ◽  
Vol 256 ◽  
pp. 112342
Author(s):  
Meng Qu ◽  
Xiaoping Pang ◽  
Xi Zhao ◽  
Ruibo Lei ◽  
Qing Ji ◽  
...  
Keyword(s):  
Beaufort Sea ◽  
Thermal Imagery ◽  
Terra Modis

scholarly journals Impact of Monsoon-Transported Anthropogenic Aerosols and Sun-Glint on the Satellite-Derived Spectral Remote Sensing Reflectance in the Indian Ocean

2021 ◽  
Vol 13 (2) ◽  
pp. 184
Author(s):  
Rongjie Liu ◽  
Jie Zhang ◽  
Tingwei Cui ◽  
Haocheng Yu
Keyword(s):  
Remote Sensing ◽  
Indian Ocean ◽  
Infrared Imaging ◽  
Atmospheric Correction ◽  
Spatiotemporal Pattern ◽  
Anthropogenic Aerosols ◽  
Remote Sensing Reflectance ◽  
The Indian Ocean ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Modis

Spectral remote sensing reflectance (Rrs(λ), sr−1) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where Rrs(λ) accuracy has not been well documented, the quality of Rrs(λ) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual Rrs(λ) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of Rrs(λ) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly Rrs(λ) over the IO indicates good quality with the average scores of 0.93 ± 0.02, 0.92 ± 0.02 and 0.92 ± 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (~0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (~0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the Rrs(λ) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean–atmosphere environment.

Inter-comparison of Aqua and Terra MODIS using Adeos-II GLI orbital intersections

2005 ◽  
Author(s):  
A. Wu ◽  
X. Xiong ◽  
H. Murakami ◽  
W. Barnes
Keyword(s):  
Terra Modis

Monitoring on-orbit calibration stability of the Terra MODIS and Landsat 7 ETM+ sensors using pseudo-invariant test sites

2010 ◽  
Vol 114 (4) ◽  
pp. 925-939 ◽  
Author(s):  
Gyanesh Chander ◽  
Xiaoxiong (Jack) Xiong ◽  
Taeyoung (Jason) Choi ◽  
Amit Angal
Keyword(s):  
Landsat 7 ◽  
Invariant Test ◽  
Terra Modis

An Overview of Terra MODIS Reflective Solar Bands On-orbit Calibration

2006 ◽  
Author(s):  
X. Xiong ◽  
V. Salomonson ◽  
W. Barnes ◽  
B. Guenther ◽  
X. Xie ◽  
...  
Keyword(s):  
Terra Modis

scholarly journals Global estimation of burned area using MODIS active fire observations

2006 ◽  
Vol 6 (4) ◽  
pp. 957-974 ◽  
Author(s):  
L. Giglio ◽  
G. R. van der Werf ◽  
J. T. Randerson ◽  
G. J. Collatz ◽  
P. Kasibhatla
Keyword(s):  
Vegetation Cover ◽  
Regression Trees ◽  
The United States ◽  
Tree Cover ◽  
Burned Area ◽  
Data Set ◽  
Active Fire ◽  
Terra Modis ◽  
Good Agreement ◽  
Area Data

Abstract. We present a method for estimating monthly burned area globally at 1° spatial resolution using Terra MODIS data and ancillary vegetation cover information. Using regression trees constructed for 14 different global regions, MODIS active fire observations were calibrated to burned area estimates derived from 500-m MODIS imagery based on the assumption that burned area is proportional to counts of fire pixels. Unlike earlier methods, we allow the constant of proportionality to vary as a function of tree and herbaceous vegetation cover, and the mean size of monthly cumulative fire-pixel clusters. In areas undergoing active deforestation, we implemented a subsequent correction based on tree cover information and a simple measure of fire persistence. Regions showing good agreement between predicted and observed burned area included Boreal Asia, Central Asia, Europe, and Temperate North America, where the estimates produced by the regression trees were relatively accurate and precise. Poorest agreement was found for southern-hemisphere South America, where predicted values of burned area are both inaccurate and imprecise; this is most likely a consequence of multiple factors that include extremely persistent cloud cover, and lower quality of the 500-m burned area maps used for calibration. Application of our approach to the nine remaining regions yielded comparatively accurate, but less precise, estimates of monthly burned area. We applied the regional regression trees to the entire archive of Terra MODIS fire data to produce a monthly global burned area data set spanning late 2000 through mid-2005. Annual totals derived from this approach showed good agreement with independent annual estimates available for nine Canadian provinces, the United States, and Russia. With our data set we estimate the global annual burned area for the years 2001-2004 to vary between 2.97 million and 3.74 million km2, with the maximum occurring in 2001. These coarse-resolution burned area estimates may serve as a useful interim product until long-term burned area data sets from multiple sensors and retrieval approaches become available.

scholarly journals Remote Sensing Data for Calibrated Assessment of Wildfire Emissions in Siberian Forests

Proceedings ◽  
2018 ◽  
Vol 2 (7) ◽  
pp. 348
Author(s):  
Evgenii Ponomarev ◽  
Eugene Shvetsov ◽  
Kirill Litvintsev ◽  
Irina Bezkorovaynaya ◽  
Tatiana Ponomareva ◽  
...  
Keyword(s):  
Standard Procedure ◽  
Remote Sensing Data ◽  
Fire Danger ◽  
Variable Intensity ◽  
Burned Areas ◽  
Radiative Power ◽  
Terra Modis ◽  
Siberian Forests

This study was carried out for Siberia using Terra/Modis satellite data (2002–2016), data of ground surveys on burned areas of different ages, long-term meteorological information, and numerical simulation results. On the basis of meteorological and wildfire databases, we evaluated the probability (~18%) of an extreme fire danger scenario that was found to occur every 8 ± 3 years in different parts of the region. Next, we used Fire Radiative Power (FRP) measurements to classify the varieties of burning conditions for each wildfire in the database. The classification of the annually burned forest area was obtained in accordance with the assessments of burning intensity ranges categorized by FRP. Depending on the fire danger scenario in Siberia, 47.04 ± 13.6% of the total wildfire areas were classified as low-intensity burning, 42.46 ± 10.50% as medium-intensity fire areas, and 10.50 ± 6.90% as high-intensity. Next, we calculated the amount of combusted biomass and the direct emissions for each wildfire, taking into account the variable intensity of burning within the fire polygons. The total annual emissions were also calculated for Siberia for the last 15 years, from 2002 to 2016. The average estimate of direct carbon emission was 83 ± 21 Tg/year, which is lower than the result (112 ± 25 Tg/year) we obtained using the standard procedure.

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