Using satellite data to develop environmental indicators

Sherbinin, Alex de; Levy, Marion J.; Zell, E.; Weber, Stephanie; Jaiteh, Malanding S.

doi:10.1088/1748-9326/9/8/084013

Cited by 49 publications

(31 citation statements)

References 36 publications

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“…Satellite‐ and other aerial‐based remote sensing can overcome such gaps by providing wall‐to‐wall coverage of important landscape complexity parameters (e.g., diversity, factuality, and function) over decadal time scales. However, significant barriers to the access and use of satellite data remain (Papadimitriou, 2002; de Sherbinin et al, 2014). In the last two decades, integrations of simulation models and satellite data have frequently been used to estimate regional crop yields and to supply data for large‐scale yield gap analyses (Lobell, 2013), although the approach rejects the variability of management practices among farms.…”

Section: Agri‐environmental Indicatorsmentioning

confidence: 99%

“…Similarly, Yang et al (2000) used SPOT satellite imagery and the Enhanced Stream Water Quality Model QUAL2E to estimate water quality bioindicators of algal growth rate and respiration rate for a large reservoir in Taiwan. Likewise, de Sherbinin et al (2014) combined the GEOS–Chem chemical transport model and satellite data from NASA's Moderate Resolution Imaging Spectroradiometer and Terra satellite's Multi‐angle Imaging Spectroradiometer instruments to develop an annual global air quality indicator (particulate matter with a diameter >2.5 μm; PM 2.5 ).…”

Section: Agri‐environmental Indicatorsmentioning

confidence: 99%

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Environmental Indicator Principium with Case References to Agricultural Soil, Water, and Air Quality and Model‐Derived Indicators

et al. 2018

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Environmental indicators are powerful tools for tracking environmental changes, measuring environmental performance, and informing policymakers. Many diverse environmental indicators, including agricultural environmental indicators, are currently in use or being developed. This special collection of technical papers expands on the peer-reviewed literature on environmental indicators and their application to important current issues in the following areas: (i) model-derived indicators to indicate phosphorus losses from arable land to surface runoff and subsurface drainage, (ii) glutathione-ascorbate cycle-related antioxidants as early-warning bioindicators of polybrominated diphenyl ether toxicity in mangroves, and (iii) assessing the effectiveness of using organic matrix biobeds to limit herbicide dissipation from agricultural fields, thereby controlling on-farm point-source pollution. This introductory review also provides an overview of environmental indicators, mainly for agriculture, with examples related to the quality of the agricultural soil-water-air continuum and the application of model-derived indicators. Current knowledge gaps and future lines of investigation are also discussed. It appears that environmental indicators, particularly those for agriculture, work efficiently at the field, catchment, and local scales and serve as valuable metrics of system functioning and response; however, these indicators need to be refined or further developed to comprehensively meet community expectations in terms of providing a consistent picture of relevant issues and/or allowing comparisons to be made nationally or internationally.

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Section: Agri‐environmental Indicatorsmentioning

confidence: 99%

Section: Agri‐environmental Indicatorsmentioning

confidence: 99%

Environmental Indicator Principium with Case References to Agricultural Soil, Water, and Air Quality and Model‐Derived Indicators

et al. 2018

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“…), CIRs are derived from CDRs to provide authoritative climate reference datasets for use by decision makers to devise strategies to respond and adapt to, as well as mitigate climate change impacts and trends. For example, various climate and environmental indices/indicators (or CIRs) have been developed by combining satellite CDRs with related in-situ and/or model data in the extreme context [26][27][28], which have significant economic and societal implications [29].…”

Section: Value-added Icdr and Cirmentioning

confidence: 99%

Satellite Climate Data Records: Development, Applications, and Societal Benefits

et al. 2016

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This review paper discusses how to develop, produce, sustain, and serve satellite climate data records (CDRs) in the context of transitioning research to operation (R2O). Requirements and critical procedures of producing various CDRs, including Fundamental CDRs (FCDRs), Thematic CDRs (TCDRs), Interim CDRs (ICDRs), and climate information records (CIRs) are discussed in detail, including radiance/reflectance and the essential climate variables (ECVs) of land, ocean, and atmosphere. Major international CDR initiatives, programs, and projects are summarized. Societal benefits of CDRs in various user sectors, including Agriculture, Forestry, Fisheries, Energy, Heath, Water, Transportation, and Tourism are also briefly discussed. The challenges and opportunities for CDR development, production and service are also addressed. It is essential to maintain credible CDR products by allowing free access to products and keeping the production process transparent by making source code and documentation available with the dataset.

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“…Satellite observations of AOD to estimate PM exposures provide more complete spatial and temporal coverage than fixed monitoring sites [20][21][22][23][24][25], but the existing methods using AOD are still limited to the resolution of the instrument's aerosol product retrieval algorithm. Fortunately, the spatial resolutions of satellite aerosol products have improved dramatically since their inception.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Subgrid-Scale Variability in Particulate Matter with Respect to Satellite Aerosol Observations

et al. 2018

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Recent use of satellite observations of aerosol optical depth (AOD) to characterize surface concentrations of particulate matter (PM) air pollution has proven extremely valuable in estimating exposures for health effects studies. While the spatial resolutions of satellite data provide far better coverage than existing fixed site surface monitoring stations, they are not able to capture atmospheric processes such as dilution of primary pollutants that vary at small spatial scales. As a result, small-scale variability due to highly localized sources such as traffic may be poorly represented, which in turn may lead to exposure measurement error in epidemiological analyses. Using a fixed spatial grid representing 4.4 km Multiangle Imaging SpectroRadiometer (MISR) aerosol observations, we examined the spatial variability in fine and coarse mode PM (PM 2.5 and PM 2.5-10 respectively) measured at ground monitors from a unique spatially-dense sampling campaign in Southern California. We found that while the variance in measured PM 2.5 differed seasonally (warm 6.82 µg 2 /m 6 and cool 24.5 µg 2 /m 6 ) across the study region, the average subgrid (<4.4 km) variance did not (warm 2.03 µg 2 /m 6 and cool 2.43 µg 2 /m 6 ) and was significantly smaller. On the other hand, ground monitor PM 2.5-10 concentrations showed large variance in warm (18.6 µg 2 /m 6 ) and cool (20.6 µg 2 /m 6 ) seasons, as well as seasonal differences in subgrid variance (warm 8.90 µg 2 /m 6 and cool 3.28 µg 2 /m 6 ). Geostatistical analysis of the semivariance as a function of distance indicated that variability in measured PM 2.5 and PM 2.5-10 concentrations was relatively constant for spatial scales of one to five kilometers, but there was evidence of small-scale (~500 m) variability in PM 2.5-10 concentrations in the cool season. The lack of small-scale spatial variability in the warm season was likely due to large photochemical contributions to regional PM 2.5 , and greater regional contributions to PM 2.5-10 from windblown dust. In contrast, in the cool season there tends to be greater localized concentrations from primary traffic sources due to stronger nocturnal inversions and delayed morning winds reducing dilution that contribute to greater spatial heterogeneity. Overall, these results suggest that regional contributions tend to dominate PM 2.5 , and spatial resolutions of satellite observations including the 4.4 km MISR and 3 km MODIS aerosol products aptly capture relevant spatial variability. Coarse PM 2.5-10 can have seasonally dependent localized contributions, leading to small-scale variability below current satellite aerosol product resolutions.

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Using satellite data to develop environmental indicators

Cited by 49 publications

References 36 publications

Environmental Indicator Principium with Case References to Agricultural Soil, Water, and Air Quality and Model‐Derived Indicators

Environmental Indicator Principium with Case References to Agricultural Soil, Water, and Air Quality and Model‐Derived Indicators

Satellite Climate Data Records: Development, Applications, and Societal Benefits

Characterization of Subgrid-Scale Variability in Particulate Matter with Respect to Satellite Aerosol Observations

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