The Earth Observing System: A Space-based Program for Assessing Mankind's Impact on the Global Environment

King, Michael D.; Herring, David; Diner, David J.

doi:10.1364/opn.6.1.000034

Cited by 42 publications

(10 citation statements)

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“…The Moderate Resolution Imaging Spectroradiometer (MODIS) was developed as the keystone instrument on the Earth Observing System (EOS) AM‐1 (Terra, EQX ∼1030) and EOS PM‐1 (Aqua, EQX ∼1330) platforms for global studies including ocean processes [ Salomonson et al , 1989; King et al , 1995; Esaias et al , 1998]. Two bands are imaged at a nominal resolution of 250 m at nadir, with five bands at 500 m and at the remaining 29 bands at 1000 m. Three bands of 1000 m resolution at thermal infrared wavelengths (channel 20, 3.6–3.8 μm; channel 31, 10.8–11.3 μm; and channel 32, 11.8–12.3 μm) are used for SST observations [ Esaias et al , 1998; Brown and Minnett , 1999].…”

Section: In Situ Observations and Data Processingmentioning

confidence: 99%

Lake surface water temperature retrieval using advanced very high resolution radiometer and Moderate Resolution Imaging Spectroradiometer data: Validation and feasibility study

et al. 2005

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[1] In this study we prove the feasibility of the advanced very high resolution radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer sea surface temperature algorithms to derive operational lake surface water temperature (LSWT). A validation study covering 2 years was done using data from the AVHRR on NOAA 12, 15, 16, and 17, the MODIS on TERRA, and AQUA and with different method-ingested in situ data from different sized lakes. Best results were found for NOAA 16 nighttime data at Lake Geneva (bias of 0.18 K and standard deviation of 0.73 K) and TERRA nighttime data at Lake Constance (satellite-buoy bias of À0.08 K and standard deviation of 0.92 K). For all sensor families an overall scatter ranging from 0.9 to 1.6 K was found. Bias of MODIS is larger, À1.73 to 1.9 K, than the one of the AVHRR (À0.28 to 1.5 K). The current orbital configuration of the platforms used revealed the diurnal evolution of the lake surface temperature amplitude from space. The damped mixing found for a typical calm and clear-sky regime is different from open ocean conditions. As the main error source, we found undetected cloudy pixel. Furthermore, the physical difference between skin and bulk temperature, especially its relation to the diurnal thermocline, solar insolation, and wind stress contributes to the bias and scatter within the match-up data set. The data sets have been validated to allow further application for LSWT climatology and assimilation into numerical weather prediction models.Citation: Oesch, D. C., J.-M. Jaquet, A. Hauser, and S. Wunderle (2005), Lake surface water temperature retrieval using advanced very high resolution radiometer and Moderate Resolution Imaging Spectroradiometer data: Validation and feasibility study,

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Section: In Situ Observations and Data Processingmentioning

confidence: 99%

Lake surface water temperature retrieval using advanced very high resolution radiometer and Moderate Resolution Imaging Spectroradiometer data: Validation and feasibility study

et al. 2005

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“…As presented in section 7, Rayleigh scattering effects can be observed with satellite and/or airborne multichannel measurements. This can also be observed and validated with satellite multiangle measurements, e.g., the Multiangle Imaging Spectroradiometer (MISR) [Diner et al, 1989] scheduled to be flown on the same platform as MODIS in 1998 [King et al, 1995]. MISR consists of a set of nine cameras that view the Earth at nine angles (0, +_26.1 ø, _45.6 ø, _60.0 ø, and _+70.5 ø to the Earth's surface normal, where positive and negative angles refer to directions ahead of and behind the spacecraft, respectively) and in four spectral bands (0.443, 0.555, 0.670, and 0.865 p•m).…”

Section: Discussionmentioning

confidence: 99%

Correction of Rayleigh scattering effects in cloud optical thickness retrievals

Wang¹,

King²

1997

J. Geophys. Res.

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“…The spatial pattern of PWV was analysed through the semivariogram, which is a geostatistical tool that allows the characterization of the variability between two spatial points of a given variable, depending on the distance between these two points. Hence, if the variability increases, then a decline of the influence of the PWV at increasingly remote regions is observed [22,23]. Also, it is not necessary to know the mean of the variable for its calculation.…”

Section: Spatial Analysismentioning

confidence: 99%

“…Data pairs located at short distances show low values of semivariance; when the distance increases, the semivariance also increases, until it reaches a maximum value named sill. Range is defined as the distance for the sill to be reached, that is, when the semivariogram reaches its limit value, and it represents the distance from which the correlation is negligible [22,24]. Furthermore, there are two types of semivariogram, the first one considers that there is a spatial variation independent of the direction (isotropic behaviour), known as omnidirectional semivariogram; the second type considers the existence of anisotropy, that is, a direction-dependent variability, known as directional variograms [22,25,26].…”

Section: Spatial Analysismentioning

confidence: 99%

Spatio-Temporal Variability of the Precipitable Water Vapor over Peru through MODIS and ERA-Interim Time Series

2019

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Precipitable water vapor (PWV) is a meteorological variable that influences the main processes that occur in the atmosphere. It is not a homogeneous variable, but varies both temporally and spatially according to local conditions. This study analyzes the spatial and temporal variability of the PWV in Peru using MODIS satellite data (MOD05/MYD05 products) during the period 2000 to 2017. MODIS-derived PWV values were complemented with ERA-Interim reanalysis data to take the study period back to 1979. PWV values extracted from MODIS and ERA-Interim were compared against in situ values obtained from five radiosonde stations between the years of 2003 and 2016 (non-continuous data). The study was performed over nine sub-regions of the Peruvian territory: coastal, highland, and jungle sub-regions, which in turn were classified into northern, central and southern regions. The analysis of spatial variability was performed using monthly semivariograms and influencing parameters such as sill and range, whereas the temporal variation was examined by time series of monthly, seasonal, and multi-annual means. The Mann-Kendall test was also applied to determine the presence of trends. The spatial analysis evidenced the heterogeneity of the PWV over the study region, and in most of the sub-regions there was directional variability during the austral summer and austral winter, with the Northeast (NE) and East (E) directions having the greatest spatial variability. The omnidirectional analysis of the sill and range showed that there was a high spatial variability of PWV mainly over the northern and southern jungle, even exceeding the limit area of these sub-regions. The temporal analysis shows that this variability occurs more in the north and center of the jungle and in the north coast, where the content of PWV is higher in relation to other regions, while the central and southern highlands have the lowest values. In addition, the trend test determines that there is a slight increase in PWV for the coast and jungle regions of Peru. Validation analysis using the radiosonde data showed a similar performance of both datasets (MODIS and ERA), with better results for the case of the MODIS product (RMSE < 0.6 cm and R 2 = 0.71). pattern, especially over oceans, where the PWV amount is higher over the Equator and lower over the Poles. However, over continental lands there is a significant influence of the orography, with the lowest values over high altitude sites and locations far away from the sea, and the highest values over low elevation sites and sites near the coast. The PWV also follows a seasonal cycle, with higher values in summer and lower values in winter.The knowledge of the spatial and temporal variations of PWV are extremely important to produce realistic outputs from climate models [1]. This is especially important for Peru because of the number of microclimates that characterize this country. However, as far as we know, these parameters have been poorly characterized for Peru.The main objective of this work is to provide a...

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The Earth Observing System: A Space-based Program for Assessing Mankind's Impact on the Global Environment

Cited by 42 publications

References 0 publications

Lake surface water temperature retrieval using advanced very high resolution radiometer and Moderate Resolution Imaging Spectroradiometer data: Validation and feasibility study

Lake surface water temperature retrieval using advanced very high resolution radiometer and Moderate Resolution Imaging Spectroradiometer data: Validation and feasibility study

Correction of Rayleigh scattering effects in cloud optical thickness retrievals

Spatio-Temporal Variability of the Precipitable Water Vapor over Peru through MODIS and ERA-Interim Time Series

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