Dataset Information

Direct retrieval of isoprene from satellite-based infrared measurements.

ABSTRACT: Isoprene is the atmosphere's most important non-methane organic compound, with key impacts on atmospheric oxidation, ozone, and organic aerosols. In-situ isoprene measurements are sparse, and satellite-based constraints have employed an indirect approach using its oxidation product formaldehyde, which is affected by non-isoprene sources plus uncertainty and spatial smearing in the isoprene-formaldehyde relationship. Direct global isoprene measurements are therefore needed to better understand its sources, sinks, and atmospheric impacts. Here we show that the isoprene spectral signatures are detectable from space using the satellite-borne Cross-track Infrared Sounder (CrIS), develop a full-physics retrieval methodology for quantifying isoprene abundances from these spectral features, and apply the algorithm to CrIS measurements over Amazonia. The results are consistent with model output and in-situ data, and establish the feasibility of direct global space-based isoprene measurements. Finally, we demonstrate the potential for combining space-based measurements of isoprene and formaldehyde to constrain atmospheric oxidation over isoprene source regions.

SUBMITTER: Fu D

PROVIDER: S-EPMC6707292 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Direct retrieval of isoprene from satellite-based infrared measurements.

Fu Dejian D Millet Dylan B DB Wells Kelley C KC Payne Vivienne H VH Yu Shanshan S Guenther Alex A Eldering Annmarie A

Nature communications 20190823 1

Isoprene is the atmosphere's most important non-methane organic compound, with key impacts on atmospheric oxidation, ozone, and organic aerosols. In-situ isoprene measurements are sparse, and satellite-based constraints have employed an indirect approach using its oxidation product formaldehyde, which is affected by non-isoprene sources plus uncertainty and spatial smearing in the isoprene-formaldehyde relationship. Direct global isoprene measurements are therefore needed to better understand it ...[more]

PMID: 31444348

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Project description:Due to its worldwide coverage and high revisit time, satellite-based remote sensing provides the ability to monitor in-season crop state variables and yields globally. In this study, we presented a novel approach to training agronomic satellite retrieval algorithms by utilizing collocated crop growth model simulations and solar-reflective satellite measurements. Specifically, we showed that bidirectional long short-term memory networks (BLSTMs) can be trained to predict the in-season state variables and yields of Agricultural Production Systems sIMulator (APSIM) maize crop growth model simulations from collocated Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m satellite measurements over the United States Corn Belt at a regional scale. We evaluated the performance of the BLSTMs through both k-fold cross validation and comparison to regional scale ground-truth yields and phenology. Using k-fold cross validation, we showed that three distinct in-season maize state variables (leaf area index, aboveground biomass, and specific leaf area) can be retrieved with cross-validated R2 values ranging from 0.4 to 0.8 for significant portions of the season. Several other plant, soil, and phenological in-season state variables were also evaluated in the study for their retrievability via k-fold cross validation. In addition, by comparing to survey-based United State Department of Agriculture (USDA) ground truth data, we showed that the BLSTMs are able to predict actual county-level yields with R2 values between 0.45 and 0.6 and actual state-level phenological dates (emergence, silking, and maturity) with R2 values between 0.75 and 0.85. We believe that a potential application of this methodology is to develop satellite products to monitor in-season field-scale crop growth on a global scale by reproducing the methodology with field-scale crop growth model simulations (utilizing farmer-recorded field-scale agromanagement data) and collocated high-resolution satellite data (fused with moderate-resolution satellite data).

Dataset Information

Direct retrieval of isoprene from satellite-based infrared measurements.

Publications

Direct retrieval of isoprene from satellite-based infrared measurements.

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