Prediction of Satellite-Based Column CO<sub>2</sub> Concentration by Combining Emission Inventory and LULC Information

Bhattacharjee, Shrutilipi; Chen, Jia

doi:10.1109/tgrs.2020.2985047

Cited by 32 publications

(7 citation statements)

References 70 publications

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“…In recent years, machine-learning-based approaches have become more common to fill gaps in univariate, gappy satellite data or to upscale sparse station networks (Kadow et al, 2020;Gerber et al, 2018;Zeng et al, 2015;Shen and Zhang, 2009). These methods are by default univariate but can be extended into multivariate settings (Bhattacharjee and Chen, 2020;von Buttlar et al, 2014).…”

Section: Missing Observations In Earth System Sciencementioning

confidence: 99%

CLIMFILL v0.9: a framework for intelligently gap filling Earth observations

2022

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Abstract. Remotely sensed Earth observations have many missing values. The abundance and often complex patterns of these missing values can be a barrier for combining different observational datasets and may cause biased estimates of derived statistics. To overcome this, missing values in geoscientific data are regularly infilled with estimates through univariate gap-filling techniques such as spatial or temporal interpolation or by upscaling approaches in which complete donor variables are used to infer missing values. However, these approaches typically do not account for information that may be present in other observed variables that also have missing values. Here we propose CLIMFILL (CLIMate data gap-FILL), a multivariate gap-filling procedure that combines kriging interpolation with a statistical gap-filling method designed to account for the dependence across multiple gappy variables. In a first stage, an initial gap fill is constructed for each variable separately using state-of-the-art spatial interpolation. Subsequently, the initial gap fill for each variable is updated to recover the dependence across variables using an iterative procedure. Estimates for missing values are thus informed by knowledge of neighbouring observations, temporal processes, and dependent observations of other relevant variables. CLIMFILL is tested using gap-free ERA-5 reanalysis data of ground temperature, surface-layer soil moisture, precipitation, and terrestrial water storage to represent central interactions between soil moisture and climate. These variables were matched with corresponding remote sensing observations and masked where the observations have missing values. In this “perfect dataset approach” CLIMFILL can be evaluated against the original, usually not observed part of the data. We show that CLIMFILL successfully recovers the dependence structure among the variables across all land cover types and altitudes, thereby enabling subsequent mechanistic interpretations in the gap-filled dataset. Correlation between original ERA-5 data and gap-filled ERA-5 data is high in many regions, although it shows artefacts of the interpolation procedure in large gaps in high-latitude regions during winter. Bias and noise in gappy satellite-observable data is reduced in most regions. A case study of the European 2003 heatwave shows how CLIMFILL reduces biases in ground temperature and surface-layer soil moisture induced by the missing values. Furthermore, in idealized experiments we see the impact of fraction of missing values and the complexity of missing value patterns to the performance of CLIMFILL, showing that CLIMFILL for most variables operates at the upper limit of what is possible given the high fraction of missing values and the complexity of missingness patterns. Thus, the framework can be a tool for gap filling a large range of remote sensing observations commonly used in climate and environmental research.

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Section: Missing Observations In Earth System Sciencementioning

confidence: 99%

CLIMFILL v0.9: a framework for intelligently gap filling Earth observations

2022

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“…OCO-2´s nominal footprint is 1.3 × 2.25 km [38]. OCO-2 provides global coverage with a 16-day temporal resolution and gives information on the physiological state of the canopy at~1:30 p.m. local time [37,50]. Thus, it captures the sensitivity of the fluorescence yield (PAR•fPAR•Φ F ; Equation (1)) to water stress, which generally peaks in the afternoon [51].…”

Section: Solar-induced Fluorescence From Oco-2mentioning

confidence: 99%

“…The recent NASA Orbiting Carbon Observatory-2 (OCO-2) mission (launched in September, 2014) [31][32][33][34][35][36][37] offers a higher-resolution alternative (footprint of 2.25 × 1.3 km) than the previous coarser SIF measurements [38] by GOSAT (footprint of 10 km diameter) [28,39] and GOME-2 (40 × 40 km) [40]. Along with a high spatial resolution, OCO-2's SIF provides the opportunity to study the vegetation responses of specific vegetation types to different climate conditions, thus providing completely new perspectives for SIF analysis [20,41].…”

Section: Introductionmentioning

confidence: 99%

OCO-2 Solar-Induced Chlorophyll Fluorescence Variability across Ecoregions of the Amazon Basin and the Extreme Drought Effects of El Niño (2015–2016)

et al. 2020

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Amazonian ecosystems are major biodiversity hotspots and carbon sinks that may lose species to extinction and become carbon sources due to extreme dry or warm conditions. We investigated the seasonal patterns of high-resolution solar-induced chlorophyll fluorescence (SIF) measured by the satellite Orbiting Carbon Observatory-2 (OCO-2) across the Amazonian ecoregions to assess the area´s phenology and extreme drought vulnerability. SIF is an indicator of the photosynthetic activity of chlorophyll molecules and is assumed to be directly related to gross primary production (GPP). We analyzed SIF variability in the Amazon basin during the period between September 2014 and December 2018. In particular, we focused on the SIF drought response under the extreme drought period during the strong El Niño in 2015–2016, as well as the 6-month drought peak period. During the drought´s peak months, the SIF decreased and increased with different intensities across the ecoregions of the Amazonian moist broadleaf forest (MBF) biome. Under a high temperature, a high vapor pressure deficit, and extreme drought conditions, the SIF presented differences from −31.1% to +17.6%. Such chlorophyll activity variations have been observed in plant-level measurements of active fluorescence in plants undergoing physiological responses to water or heat stress. Thus, it is plausible that the SIF variations in the ecoregions’ ecosystems occurred as a result of water and heat stress, and arguably because of drought-driven vegetation mortality and collateral effects in their species composition and community structures. The SIF responses to drought at the ecoregional scale indicate that there are different levels of resilience to drought across MBF ecosystems that the currently used climate- and biome-region scales do not capture. Finally, we identified monthly SIF values of 32 ecoregions, including non-MBF biomes, which may give the first insights into the photosynthetic activity dynamics of Amazonian ecoregions.

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“…A more in-depth site-based study is also recommended to get more insight into ecophysiological stress mechanism in forests during drought. Furthermore, an interpolation of OCO's dataset through machine learning and statistical tools [34,95] to cover the vacant areas may provide solutions for more rigorous analyses.…”

Section: Oco-2 Sif For Studying Drought Impactmentioning

confidence: 99%

Capturing the Impact of the 2018 European Drought and Heat across Different Vegetation Types Using OCO-2 Solar-Induced Fluorescence

et al. 2020

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The European heatwave of 2018 led to record-breaking temperatures and extremely dry conditions in many parts of the continent, resulting in widespread decrease in agricultural yield, early tree-leaf senescence, and increase in forest fires in Northern Europe. Our study aims to capture the impact of the 2018 European heatwave on the terrestrial ecosystem through the lens of a high-resolution solar-induced fluorescence (SIF) data acquired from the Orbiting Carbon Observatory-2 (OCO-2) satellite. SIF is proposed to be a direct proxy for gross primary productivity (GPP) and thus can be used to draw inferences about changes in photosynthetic activity in vegetation due to extreme events. We explore spatial and temporal SIF variation and anomaly in the spring and summer months across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest) during the European heatwave of 2018 and compare it to non-drought conditions (most of Southern Europe). About one-third of Europe’s land area experienced a consecutive spring and summer drought in 2018. Comparing 2018 to mean conditions (i.e., those in 2015–2017), we found a change in the intra-spring season SIF dynamics for all vegetation types, with lower SIF during the start of spring, followed by an increase in fluorescence from mid-April. Summer, however, showed a significant decrease in SIF. Our results show that particularly agricultural areas were severely affected by the hotter drought of 2018. Furthermore, the intense heat wave in Central Europe showed about a 31% decrease in SIF values during July and August as compared to the mean over the previous three years. Furthermore, our MODIS (Moderate Resolution Imaging Spectroradiometer) and OCO-2 comparative results indicate that especially for coniferous and mixed forests, OCO-2 SIF has a quicker response and a possible higher sensitivity to drought in comparison to MODIS’s fPAR (fraction of absorbed photosynthetically active radiation) and the Normalized Difference Vegetation Index (NDVI) when considering shorter reference periods, which highlights the added value of remotely sensed solar-induced fluorescence for studying the impact of drought on vegetation.

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Prediction of Satellite-Based Column CO₂ Concentration by Combining Emission Inventory and LULC Information

Cited by 32 publications

References 70 publications

CLIMFILL v0.9: a framework for intelligently gap filling Earth observations

CLIMFILL v0.9: a framework for intelligently gap filling Earth observations

OCO-2 Solar-Induced Chlorophyll Fluorescence Variability across Ecoregions of the Amazon Basin and the Extreme Drought Effects of El Niño (2015–2016)

Capturing the Impact of the 2018 European Drought and Heat across Different Vegetation Types Using OCO-2 Solar-Induced Fluorescence

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Prediction of Satellite-Based Column CO2 Concentration by Combining Emission Inventory and LULC Information

Cited by 32 publications

References 70 publications

CLIMFILL v0.9: a framework for intelligently gap filling Earth observations

CLIMFILL v0.9: a framework for intelligently gap filling Earth observations

OCO-2 Solar-Induced Chlorophyll Fluorescence Variability across Ecoregions of the Amazon Basin and the Extreme Drought Effects of El Niño (2015–2016)

Capturing the Impact of the 2018 European Drought and Heat across Different Vegetation Types Using OCO-2 Solar-Induced Fluorescence

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Prediction of Satellite-Based Column CO₂ Concentration by Combining Emission Inventory and LULC Information