Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared channels

2003 ◽  
Vol 108 (D13) ◽  
pp. n/a-n/a ◽  
Author(s):  
Bo-Cai Gao ◽  
Yoram J. Kaufman
Keyword(s):  
Water Vapor ◽  
Near Infrared ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Test of a Modified Infrared-Only ABI Cloud Mask Algorithm for AHI Radiance Observations

2016 ◽  
Vol 55 (11) ◽  
pp. 2529-2546 ◽  
Author(s):  
X. Zhuge ◽  
X. Zou
Keyword(s):  
Near Infrared ◽  
Extended System ◽  
Clear Sky ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask ◽  
Very High ◽  
Lower False Alarm Rate ◽  
Infrared Channel

AbstractAssimilation of infrared channel radiances from geostationary imagers requires an algorithm that can separate cloudy radiances from clear-sky ones. An infrared-only cloud mask (CM) algorithm has been developed using the Advanced Himawari Imager (AHI) radiance observations. It consists of a new CM test for optically thin clouds, two modified Advanced Baseline Imager (ABI) CM tests, and seven other ABI CM tests. These 10 CM tests are used to generate composite CMs for AHI data, which are validated by using the Moderate Resolution Imaging Spectroradiometer (MODIS) CMs. It is shown that the probability of correct typing (PCT) of the new CM algorithm over ocean and over land is 89.73% and 90.30%, respectively and that the corresponding leakage rates (LR) are 6.11% and 4.21%, respectively. The new infrared-only CM algorithm achieves a higher PCT and a lower false-alarm rate (FAR) over ocean than does the Clouds from the Advanced Very High Resolution Radiometer (AVHRR) Extended System (CLAVR-x), which uses not only the infrared channels but also visible and near-infrared channels. A slightly higher FAR of 7.92% and LR of 6.18% occurred over land during daytime. This result requires further investigation.

scholarly journals Identifying drought events in sugarcane using drought indices derived from Modis sensor

2017 ◽  
Vol 52 (11) ◽  
pp. 1063-1071 ◽  
Author(s):  
Michelle Cristina Araujo Picoli ◽  
Daniel Garbellini Duft ◽  
Pedro Gerber Machado
Keyword(s):  
Near Infrared ◽  
Sensor Data ◽  
Drought Indices ◽  
Spectral Indices ◽  
Seasonal Fluctuations ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Aqua Satellite ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Shortwave Infrared

Abstract: The objective of this work was to evaluate the potential of several spectral indices, used on moderate resolution imaging spectroradiometer (Modis) images, in identifying drought events in sugarcane. Images of Terra and Aqua satellites were used to calculate the spectral indices, using visible (red), near infrared, and shortwave infrared bands, and eight indices were selected: NDVI, EVI2, GVMI, NDI6, NDI7, NDWI, SRWI, and MSI. The indices were calculated using images between October and April of the crop years 2007/08, 2008/09, 2009/10, and 2013/14. These indices were then correlated with the standardized precipitation-evapotranspiration index (SPEI), calculated for 1, 3, and 6 months. Four of them had significant correlations with SPEI: GVMI, MSI, NDI7, and NDWI. Spectral indices from Modis sensor on board the Aqua satellite (MYD) were more suited for drought detection, and March provided the most relevant indices for that purpose. Drought indices calculated from Modis sensor data are effective for detecting sugarcane drought events, besides being able to indicate seasonal fluctuations.

scholarly journals A Global MODIS Water Vapor Database for the Operational Atmospheric Correction of Historic and Recent Landsat Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 257 ◽  
Author(s):  
David Frantz ◽  
Marion Stellmes ◽  
Patrick Hostert
Keyword(s):  
Water Vapor ◽  
Landsat Imagery ◽  
Atmospheric Correction ◽  
Atmospheric Conditions ◽  
Wet Season ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Daily Water ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

Analysis Ready Data (ARD) have undergone the most relevant pre-processing steps to satisfy most user demands. The freely available software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring) is capable of generating Landsat ARD. An essential step of generating ARD is atmospheric correction, which requires water vapor data. FORCE relies on a water vapor database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). However, two major drawbacks arise from this strategy: (1) The database has to be compiled for each study area prior to generating ARD; and (2) MODIS and Landsat commissioning dates are not well aligned. We have therefore compiled an application-ready global water vapor database to significantly increase the operational readiness of ARD production. The free dataset comprises daily water vapor data for February 2000 to July 2018 as well as a monthly climatology that is used if no daily value is available. We systematically assessed the impact of using this climatology on surface reflectance outputs. A global random sample of Landsat 5/7/8 imagery was processed twice (i) using daily water vapor (reference) and (ii) using the climatology (estimate), followed by computing accuracy, precision, and uncertainty (APU) metrics. All APU measures were well below specification, thus the fallback usage of the climatology is generally a sound strategy. Still, the tests revealed that some considerations need to be taken into account to help quantify which sensor, band, climate, and season are most or least affected by using a fallback climatology. The highest uncertainty and bias is found for Landsat 5, with progressive improvements towards newer sensors. The bias increases from dry to humid climates, whereas uncertainty increases from dry and tropic to temperate climates. Uncertainty is smallest during seasons with low variability, and is highest when atmospheric conditions progress from a dry to a wet season (and vice versa).

scholarly journals Estimation of spatio-temporal distribution of precipitable water using MODIS and AVHRR data: a case study for Cyprus

2011 ◽  
Vol 30 ◽  
pp. 23-29 ◽  
Author(s):  
D. Hadjimitsis ◽  
Z. Mitraka ◽  
I. Gazani ◽  
A. Retalis ◽  
N. Chrysoulakis ◽  
...  
Keyword(s):  
Near Infrared ◽  
Temporal Distribution ◽  
Grid Cell ◽  
Precipitable Water ◽  
Avhrr Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Spatio Temporal ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. In this paper, the atmospheric precipitable water (PW) over the area of Cyprus was estimated by means of Advanced Very High Resolution Radiometer (AVHRR) thermal channels brightness temperature difference (ΔT). The AVHRR derived ΔT was calculated in a grid of 5 × 5 km cells; the corresponding PW value in each grid cell was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 product (near-infrared algorithm). Once the PW – ΔT relationship coefficients corresponding to the area of Cyprus were calculated, the relationship was applied to AVHRR data for one month period. Radiosonde derived PW values, as well as MODIS independent PW values were used to validate the estimations and a good agreement was noted.

scholarly journals Critical evaluation of cloud contamination in the MISR aerosol products using MODIS cloud masking products

2013 ◽  
Vol 6 (6) ◽  
pp. 10057-10079 ◽  
Author(s):  
Y. Shi ◽  
J. Zhang ◽  
J. S. Reid ◽  
B. Liu ◽  
E. J. Hyer
Keyword(s):  
High Latitude ◽  
Near Infrared ◽  
Critical Evaluation ◽  
Mean Value ◽  
Screening Methods ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
One Year ◽  
Cloud Screening

Abstract. For the first time, using the Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-based cloud screening methods, we have evaluated the impacts of cloud contamination on the Terra Multi-angle Imaging Spectroradiometer (MISR) aerosol optical depth (AOD) product. Our study, based on one year of collocated MISR and MODIS data, suggests that cloud contamination exists in both over-water and over-land MISR AOD data with heavier cloud contamination occurring over the high latitude Southern hemispheric oceans. On average globally, our study shows that thin cirrus cloud contamination introduces a possible ~0.01 high bias for the over-water MISR AOD retrievals. Over the mid to high latitude oceans and Southeast Asia, this number increases to 0.015–0.02. However, biases much larger than this mean value are found in individual retrievals. This study suggests that cloud-clearing methods using observations from MISR alone, which has only visible and near infrared channels, may not be sufficient. Measurements from MODIS can be applied to assist cloud-clearing of the MISR aerosol retrievals. Cloud screening algorithms based on multi-sensor approaches are feasible and should be considered for current and future satellite aerosol studies.

scholarly journals MISR calibration issues in high-contrast scenes, and empirical corrections

2015 ◽  
Vol 8 (3) ◽  
pp. 2521-2554 ◽  
Author(s):  
J. A. Limbacher ◽  
R. A. Kahn
Keyword(s):  
Near Infrared ◽  
High Contrast ◽  
Green Band ◽  
Validation Data ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Satellite ◽  
Cloud Screening

Abstract. We diagnose the potential causes for the Multi-angle Imaging SpectroRadiometer's (MISR) persistent high aerosol optical depth (AOD) bias at low AOD with the aid of coincident MODerate-resolution Imaging Spectroradiometer (MODIS) imagery from NASA's Terra satellite. Internal reflections within the MISR instrument are responsible for a large portion of the high AOD bias in high-contrast scenes, which are especially common as broken-cloud situations over ocean. Discrepancies between MODIS and MISR nadir-viewing near-infrared (NIR) images are used to optimize nine parameters, along with a background reflectance modulation term (that was modeled separately), to represent the observed features. Independent, surface-based AOD measurements from the AErosol RObotic NETwork (AERONET) and the Marine Aerosol Network (MAN) are compared with MISR Research Algorithm (RA) AOD retrievals for 1118 coincidences to validate the corrections when applied to the nadir and off-nadir cameras. Additionally, the calibration coefficients for the red and NIR channels used for MISR over-water aerosol retrievals were reassessed with the RA to be consistent on a camera-by-camera basis. With these corrections, plus the baseline RA corrections applied (except enhanced cloud screening), the median AOD bias in the mid-visible (green) band decreases from 0.010 to 0.002, the RMSE decreases by ~ 10%, and the slope and correlation of the MISR vs. sun photometer Ångström Exponent improves. For AOD558 nm < 0.10 and with additional cloud screening, the median bias for the RA-retrieved AOD in the green band decreases from 0.011 to 0.003, compared to ~ 0.023 for the Standard Algorithm (SA). RMSE decreases by ~ 20% compared to the baseline (uncorrected) RA and by 17–53% compared to the SA. After all corrections and cloud screening are implemented, for AOD558 nm < 0.10, which includes about half the validation data, 68% absolute AOD errors for the RA have dropped to < 0.02 (~ 0.018).

scholarly journals Assessment of the relative accuracy of hemispheric-scale snow-cover maps

2002 ◽  
Vol 34 ◽  
pp. 24-30 ◽  
Author(s):  
Dorothy K. Hall ◽  
Richard E. J. Kelly ◽  
George A. Riggs ◽  
Alfred T. C. Chang ◽  
James L. Foster
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Passive Microwave ◽  
Defense Meteorological Satellite Program ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Microwave Imager ◽  
Meteorological Satellite ◽  
Microwave Data

AbstractThere are several hemispheric-scale satellite-derived snow-cover maps available, but none has been fully validated. For the period 23 October–25 December 2000, we compare snow maps of North America derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and operational snow maps from the U.S. National Oceanic and Atmospheric Administration (NOAA) National Operational Hydrologic Remote Sensing Center (NOHRSC), both of which rely on satellite data from the visible and near-infrared parts of the spectrum; we also compare MODIS maps with Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) passive-microwave snow maps for the same period. The maps derived from visible and near-infrared data are more accurate for mapping snow cover than are the passive-microwave-derived maps, but discrepancies exist as to the location and extent of the snow cover even between operational snow maps. The MODIS snow-cover maps show more snow in each of the 8 day periods than do the NOHRSC maps, in part because MODIS maps the effects of fleeting snowstorms due to its frequent coverage. The large (~30 km) footprint of the SSM/I pixel, and the difficulty in distinguishing wet and shallow snow from wet or snow-free ground, reveal differences up to 5.33 x 106 km2 in the amount of snow mapped using MODIS vs SSM/I data. Algorithms that utilize both visible and passive-microwave data, which would take advantage of the all-weather mapping capability of the passive-microwave data, will be refined following the launch of the Advanced Microwave Scanning Radiometer (AMSR) in the fall of 2001.

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