New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests

AbstractAssessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10–15 min. Our analysis of NDVI data collected over the Amazon during 2018–19 shows that ABI provides 21–35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

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Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm for Himawari-8 Data

Remote Sensing ◽

10.3390/rs11232771 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2771 ◽

Cited By ~ 2

Author(s):

Lu She ◽

Hankui Zhang ◽

Weile Wang ◽

Yujie Wang ◽

Yun Shi

Keyword(s):

Mean Squared Error ◽

Atmospheric Correction ◽

Remote Sensing Data ◽

Japan Meteorological Agency ◽

Absolute Bias ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Highly Correlated ◽

New Generation

Himawari-8, operated by the Japan Meteorological Agency (JMA), is a new generation geostationary satellite that provides remote sensing data to retrieve atmospheric aerosol optical depth (AOD) at high spatial (1 km) and high temporal (10 min) resolutions. The Geostationary- National Aeronautics and Space Administration (NASA) Earth exchange (GeoNEX) project recently adapted the multiangle implementation of atmospheric correction (MAIAC) algorithm, originally developed for joint retrieval of AOD and surface anisotropic reflectance with the moderate resolution imaging spectroradiometer (MODIS) data, to generate Earth monitoring products from the latest geostationary satellites including Himawari-8. This study evaluated the GeoNEX Himawari-8 ~1 km MAIAC AOD retrieved over all the aerosol robotic network (AERONET) sites between 6°N–30°N and 91°E–127°E. The corresponding JMA Himawari-8 AOD products were also evaluated for comparison. We only used cloud-free and the best quality satellite AOD retrievals and compiled a total of 16,532 MAIAC-AERONET and 21,737 JMA-AERONET contemporaneous pairs of AOD values for 2017. Statistical analyses showed that both MAIAC and JMA data are highly correlated with AERONET AOD, with the correlation coefficient (R) of ~0.77, and the root mean squared error (RMSE) of ~0.16. The absolute bias of MAIAC AOD (0.02 overestimation) appears smaller than that of the JMA AOD (0.05 underestimation). In comparison with the JMA data, the time series of MAIAC AOD were more consistent with AERONET AOD values and better capture the diurnal variations of the latter. The dependence of MAIAC AOD bias on scattering angles is also discussed.

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Estimation of Daily Sunshine Duration from Terra and Aqua MODIS Data

Advances in Meteorology ◽

10.1155/2014/613267 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

H. M. Kandirmaz ◽

K. Kaba

Keyword(s):

Cloud Cover ◽

Sunshine Duration ◽

Quadratic Model ◽

Moderate Resolution ◽

Resolution Imaging ◽

Daily Sunshine ◽

Moderate Resolution Imaging Spectroradiometer ◽

Relative Sunshine ◽

Relative Sunshine Duration ◽

Better Than

Some studies have shown that the estimation of global sunshine duration can be done with the help of geostationary satellites because they can record several images of the same location in a day. In this paper, images obtained from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensors of polar orbiting satellites Aqua and Terra were used to estimate daily global sunshine duration for any region in Turkey. A new quadratic correlation between daily mean cloud cover index and relative sunshine duration was also introduced and compared with the linear correlation. Results have shown that polar orbiting satellites can be used for the estimation of sunshine duration. The quadratic model introduced here works better than the linear model especially for the winter months in which very low sunshine duration values were recorded at the ground stations for many days.

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Derivation of Shortwave Radiometric Adjustments for SNPP and NOAA-20 VIIRS for the NASA MODIS-VIIRS Continuity Cloud Products

Remote Sensing ◽

10.3390/rs12244096 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4096 ◽

Cited By ~ 2

Author(s):

Kerry Meyer ◽

Steven Platnick ◽

Robert Holz ◽

Steve Dutcher ◽

Greg Quinn ◽

...

Keyword(s):

Optical Properties ◽

Infrared Imaging ◽

Trend Detection ◽

Adjustment Factors ◽

Moderate Resolution ◽

Resolution Imaging ◽

Water Cloud ◽

Moderate Resolution Imaging Spectroradiometer ◽

New Generation ◽

Cloud Mask

Climate studies, including trend detection and other time series analyses, necessarily require stable, well-characterized and long-term data records. For satellite-based geophysical retrieval datasets, such data records often involve merging the observational records of multiple similar, though not identical, instruments. The National Aeronautics and Space Administration (NASA) cloud mask (CLDMSK) and cloud-top and optical properties (CLDPROP) products are designed to bridge the observational records of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Aqua satellite and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the joint NASA/National Oceanic and Atmospheric Administration (NOAA) Suomi National Polar-orbiting Partnership (SNPP) satellite and NOAA’s new generation of operational polar-orbiting weather platforms (NOAA-20+). Early implementations of the CLDPROP algorithms on Aqua MODIS and SNPP VIIRS suffered from large intersensor biases in cloud optical properties that were traced back to relative radiometric inconsistency in analogous shortwave channels on both imagers, with VIIRS generally observing brighter top-of-atmosphere spectral reflectance than MODIS (e.g., up to 5% brighter in the 0.67 µm channel). Radiometric adjustment factors for the SNPP and NOAA-20 VIIRS shortwave channels used in the cloud optical property retrievals are derived from an extensive analysis of the overlapping observational records with Aqua MODIS, specifically for homogenous maritime liquid water cloud scenes for which the viewing/solar geometry of MODIS and VIIRS match. Application of these adjustment factors to the VIIRS L1B prior to ingestion into the CLDMSK and CLDPROP algorithms yields improved intersensor agreement, particularly for cloud optical properties.

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Assessing the RST_VOLC algorithm implementation on infrared Sentinel 3 SLSTR data

10.5194/egusphere-egu2020-2701 ◽

2020 ◽

Author(s):

Alfredo Falconieri ◽

Francesco Marchese ◽

Giuseppe Mazzeo ◽

Nicola Pergola ◽

Valerio Tramutoli

Keyword(s):

Land Surface ◽

European Space Agency ◽

Space Agency ◽

Mt Etna ◽

Multi Temporal ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

New Generation ◽

Algorithm Implementation

<p>RSTVOLC is a multi-temporal algorithm developed for detecting volcanic hotspots that was successfully used to monitor active volcanoes located in different geographic areas exploiting both polar and geostationary satellite data. The algorithm runs operationally at the Institute of Methodologies for Environmental Analysis (IMAA) to monitor Italian volcanoes in near-real time by means of Advanced Very-High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data. In this study, we assess the possible RSTVOLC implementation on data from the Sea and Land Surface Temperature Radiometer (SLSTR). The latter is a new generation sensor flying onboard the ESA (European Space Agency) Sentinel-3 mission, offering some spectral channels in the infrared bands particularly suited to identify high temperature surfaces such as lava flows. Here, we verify the RSTVOLC implementation on SLSTR data despite the absence of a multiannual time series of satellite records, by using synthetic spectral reference fields. Results achieved by investigating recent eruptions of Mt. Etna and Stromboli (Italy) volcanoes are presented and discussed.</p>

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Limitations and Challenges of MODIS-Derived Phenological Metrics Across Different Landscapes in Pan-Arctic Regions

Remote Sensing ◽

10.3390/rs10111784 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1784 ◽

Cited By ~ 3

Author(s):

Siyu Wang ◽

Xinchen Lu ◽

Xiao Cheng ◽

Xianglan Li ◽

Matthias Peichl ◽

...

Keyword(s):

Vegetation Index ◽

Vegetation Indices ◽

Optical Remote Sensing ◽

Arctic Ecosystems ◽

Arctic Regions ◽

Land Cover Dynamics ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Phenological Metrics

Recent efforts have been made to monitor the seasonal metrics of plant canopy variations globally from space, using optical remote sensing. However, phenological estimations based on vegetation indices (VIs) in high-latitude regions such as the pan-Arctic remain challenging and are rarely validated. Nevertheless, pan-Arctic ecosystems are vulnerable and also crucial in the context of climate change. We reported the limitations and challenges of using MODerate-resolution Imaging Spectroradiometer (MODIS) measurements, a widely exploited set of satellite measurements, to estimate phenological transition dates in pan-Arctic regions. Four indices including normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), phenology index (PI), plant phenological index (PPI) and a MODIS Land Cover Dynamics Product MCD12Q2, were evaluated and compared against eddy covariance (EC) estimates at 11 flux sites of 102 site-years during the period from 2000 to 2014. All the indices were influenced by snow cover and soil moisture during the transition dates. While relationships existed between VI-based and EC-estimated phenological transition dates, the R2 values were generally low (0.01–0.68). Among the VIs, PPI-estimated metrics showed an inter-annual pattern that was mostly closely related to the EC-based estimations. Thus, further studies are needed to develop region-specific indices to provide more reliable estimates of phenological transition dates.

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Estimation of fractional cloud cover for Moderate Resolution Imaging Spectroradiometer/Terra cloud mask classes with high-resolution over ocean ASTER observations

Journal of Geophysical Research Atmospheres ◽

10.1029/2009jd013520 ◽

2010 ◽

Vol 115 (D22) ◽

Cited By ~ 14

Author(s):

Andrzej Z. Kotarba

Keyword(s):

High Resolution ◽

Cloud Cover ◽

Fractional Cloud ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cloud Mask

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Examination of POLDER/PARASOL and MODIS/Aqua Cloud Fractions and Properties Representativeness

Journal of Climate ◽

10.1175/2011jcli3857.1 ◽

2011 ◽

Vol 24 (16) ◽

pp. 4435-4450 ◽

Cited By ~ 18

Author(s):

Shan Zeng ◽

Frédéric Parol ◽

Jérôme Riedi ◽

Céline Cornet ◽

François Thieuleux

Keyword(s):

Cloud Cover ◽

Temporal Variations ◽

Cloud Fraction ◽

Atmospheric Sciences ◽

Cloud Parameters ◽

Cloud Properties ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Level 2

Abstract The Polarization and Anisotropy of Reflectances for Atmospheric Sciences Coupled with Observations from a Lidar (PARASOL) and Aqua are two satellites on sun-synchronous orbits in the A-Train constellation. Aboard these two platforms, the Polarization and Directionality of Earth Reflectances (POLDER) and Moderate Resolution Imaging Spectroradiometer (MODIS) provide quasi simultaneous and coincident observations of cloud properties. The similar orbits but different detecting characteristics of these two sensors call for a comparison between the derived datasets to identify and quantify potential uncertainties in retrieved cloud properties. To focus on the differences due to different sensor spatial resolution and coverage, while minimizing sampling and weighting issues, the authors have recomputed monthly statistics directly from the respective official level-2 products. The authors have developed a joint dataset that contains both POLDER and MODIS level-2 cloud products collocated on a common sinusoidal grid. The authors have then computed and analyzed monthly statistics of cloud fractions corresponding either to the total cloud cover or to the “retrieved” cloud fraction for which cloud optical properties are derived. These simple yet crucial cloud statistics need to be clearly understood to allow further comparison work of the other cloud parameters. From this study, it is demonstrated that on average POLDER and MODIS datasets capture correctly the main characteristics of global cloud cover and provide similar spatial distributions and temporal variations. However, each sensor has its own advantages and weaknesses in discriminating between clear and cloudy skies in particular situations. Also it is shown that significant differences exist between the MODIS total cloud fraction (day mean) and the “retrieved” cloud fraction (combined mean). This study found a global negative difference of about 10% between POLDER and MODIS day-mean cloud fraction. On the contrary, a global positive difference of about 10% exists between POLDER and MODIS combined-mean cloud fraction. These statistical biases show both global and regional distributions that can be driven by sensors characteristics, environmental factors, and also carry potential information on cloud cover structure. These results provide information on the quality of cloud cover derived from POLDER and MODIS and should be taken into account for the use of other cloud products.

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A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1606162113 ◽

2016 ◽

Vol 113 (46) ◽

pp. 13087-13092 ◽

Cited By ~ 96

Author(s):

John A. Gamon ◽

K. Fred Huemmrich ◽

Christopher Y. S. Wong ◽

Ingo Ensminger ◽

Steven Garrity ◽

...

Keyword(s):

Remote Sensing ◽

Photosynthetic Activity ◽

Spatial Scales ◽

Optical Remote Sensing ◽

Ecosystem Carbon ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Warming World ◽

Evergreen Conifers

In evergreen conifers, where the foliage amount changes little with season, accurate detection of the underlying “photosynthetic phenology” from satellite remote sensing has been difficult, presenting challenges for global models of ecosystem carbon uptake. Here, we report a close correspondence between seasonally changing foliar pigment levels, expressed as chlorophyll/carotenoid ratios, and evergreen photosynthetic activity, leading to a “chlorophyll/carotenoid index” (CCI) that tracks evergreen photosynthesis at multiple spatial scales. When calculated from NASA’s Moderate Resolution Imaging Spectroradiometer satellite sensor, the CCI closely follows the seasonal patterns of daily gross primary productivity of evergreen conifer stands measured by eddy covariance. This discovery provides a way of monitoring evergreen photosynthetic activity from optical remote sensing, and indicates an important regulatory role for carotenoid pigments in evergreen photosynthesis. Improved methods of monitoring photosynthesis from space can improve our understanding of the global carbon budget in a warming world of changing vegetation phenology.

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Evaluation of Cloud Mask and Cloud Top Height from Fengyun-4A with MODIS Cloud Retrievals over the Tibetan Plateau

Remote Sensing ◽

10.3390/rs13081418 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1418

Author(s):

Wenjing Xu ◽

Daren Lyu

Keyword(s):

Tibetan Plateau ◽

Climate Science ◽

The Tibetan Plateau ◽

Cloud Detection ◽

Cloud Properties ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

New Generation ◽

Cloud Mask

The Tibetan Plateau (TP) has profound thermal and dynamic influences on the atmospheric circulation, energy, and water cycles of the climate system, which make the clouds over the TP the forefront of atmospheric and climate science. However, the highest altitude and most complex terrain of the TP make the retrieval of cloud properties challenging. In order to understand the performance and limitations of cloud retrievals over the TP derived from the state-of-the-art Advanced Geosynchronous Radiation Imager (AGRI) onboard the new generation of Chinese Geostationary (GEO) meteorological satellites Fengyun-4 (FY-4), a three-month comparison was conducted between FY-4A/AGRI and the Moderate Resolution Imaging Spectroradiometer (MODIS) for both cloud detection and cloud top height (CTH) pixel-level retrievals. For cloud detection, the AGRI and MODIS cloud mask retrievals showed a fractional agreement of 0.93 for cloudy conditions and 0.73 for clear scenes. AGRI tended to miss lower CTH clouds due to the lack of thermal contrast between the clouds and the surface of the TP. For cloud top height retrievals, the comparison showed that on average, AGRI underestimated the CTH relative to MODIS by 1.366 ± 2.235 km, and their differences presented a trend of increasing with height.

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