Towards agricultural soil carbon monitoring, reporting, and verification through the Field Observatory Network (FiON)

Nevalainen, Olli; Niemitalo, Olli; Fer, Istem; Juntunen, Antti; Mattila, Tiina; Koskela, Olli; Kukkamäki, Joni; Höckerstedt, Layla; Mäkelä, Laura; Jarva, Pieta; Heimsch, Laura; Vekuri, Henriikka; Kulmala, Liisa; Stam, Åsa; Kuusela, Otto; Gerin, Stephanie; Viskari, Toni; Vira, Julius; Hyväluoma, Jari; Tuovinen, Juha‐Pekka; Lohila, Annalea; Laurila, Tuomas; Heinonsalo, Jussi; Aalto, Tuula; Kunttu, Iivari; Liski, Jari

doi:10.5194/gi-11-93-2022

Cited by 17 publications

(9 citation statements)

References 45 publications

(51 reference statements)

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“…The growing season receives about 350 mm of precipitation, but the majority of it arrives late in the season, making the main production extremely dependent on soil water storage. See Nevalainen et al 22 and Heimsch et al 15 for more details on soil carbon sequestration and effects of carbon farming practices in Qvidja.…”

Section: Methodsmentioning

confidence: 99%

The synergistic role of sulfuric acid, ammonia and organics in particle formation over an agricultural land

Dada,

Okuljar,

Shen

et al. 2023

Environ. Sci.: Atmos.

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Section: Methodsmentioning

confidence: 99%

The synergistic role of sulfuric acid, ammonia and organics in particle formation over an agricultural land

Dada,

Okuljar,

Shen

et al. 2023

Environ. Sci.: Atmos.

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“…In the future, if the confidence in this approach increases, surrogate ML models could be a good option to consider in order to reduce computational needs for large scale carbon budget components monitoring (Wolanin et al, 2019). In the current state, it is reasonable to consider that an MRV platform for SOC carbon stock changes shall include ensemble approaches (direct measurements, ML, statistical, physical models) with varying levels of complexity and involving a diverse array of stakeholders (Nevalainen et al, 2022) (e.g. Tier 1,2 and 3), similar to what has been implemented in the IPCC approaches (Parker, 2013).…”

Section: Limitations Of the Bayesian And Physically Based Approachmentioning

confidence: 99%

AgriCarbon-EO: v1.0.1: Large Scale and High Resolution Simulation of Carbon Fluxes by Assimilation of Sentinel-2 and Landsat-8 Reflectances using a Bayesian approach

Wijmer¹,

Bitar²,

Arnaud³

et al. 2023

Preprint

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Abstract. Soil carbon storage is a well identified climate change mitigation solution. The extensive in-situ monitoring of the soil carbon storage in cropland for agricultural policy and offset carbon markets is prohibitive, especially at intra-field scale. For this reason, comprehensive Monitoring, Reporting and Verification (MRV) of soil carbon and its explanatory variables at large scale needs to rely on remote sensing and modelling tools that provide the spatio-temporal dynamics of the carbon budget and it’s components at high resolution with associated uncertainties. In this paper, we present AgriCarbon-EO v1.0: an end-to-end processing chain that enables the estimation of carbon budget components of major crops and cover crops at intra-field resolution (10 m) and large scale (over 110×110 km) by assimilating remote sensing data in physically-based radiative transfert and agronomic models. The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines Normalised Importance Sampling (NIS) and Look-Up Table (LUT) generation. This approach propagates the 10 m uncertainties across the processing chain from the reflectances to the output variables. The chain considers as input a land cover map, multi-spectral reflectance maps from the Sentinel-2 and Landsat-8 satellites, and daily weather forcing. The PROSAIL radiative transfer model is inversed in a first step to obtain Green Leaf Area Index (GLAI). The GLAI time series are then assimilated into the SAFYE-CO2 crop model taking into consideration their uncertainty. The chain is applied over winter wheat in the south-west of France during the cropping seasons 2017 and 2019. We compare the results against the net ecosystem exchange measured at the FR-AUR ICOS site (RMSE = 1.69 - 2.4 gC m−2 , R2 = 0.88 - 0.88), biomass (RMSE = 250 g m−2 , R2 = 0.9), and combine harvester yield maps. We quantify the difference between pixel and field and pixel scale simulations of biomass (bias = -47 g m−2 , -39 % variability), and the impact of the number of remote sensing acquisitions on the outputs (-66 % of mean uncertainty of biomass). Finally, we conduct a coherency analysis at regional scale to test the consistency of the observed patterns with soil texture, altitude and exposition variability. Results show higher biomass for higher clay soils and earlier emergence and senescence for south western exposition.

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“…HAMK Smart research unit brings cutting-edge digital applications in the carbon extract studies (e.g. sensors and data processing) [11].…”

Section: Environmental Issuesmentioning

confidence: 99%

Climate actions at Häme University of Applied Science (HAMK), Finland

Friman

Salminen

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Sustainable development (SD) strategy of HAMK has directed e.g. developing energy efficient solutions among other sustainability efforts in the university. Participating in GreenMetric rankings gives us useful data to improve comprehensively SD operations of the university. HAMK has long tradition to reduce its ecological footprint: for example, today half of the energy demand is fulfilled by green energy sources bought from the markets. During recent years, less emitting fuel sources and smart energy technologies had implemented on campus. In Finland, national Covid-pandemic actions were closed entire campuses for months, and hence energy consumption (heating and electricity) reduced even during cold winter-spring season. Evidently, above responsible actions have positive impact to the energy budget of the university. However, the university continuously developing pandemic-independent smart energy solutions for possible forthcoming energy crises as well as demands for the carbon neutrality (e.g. EU’s Green Deal). Multidiciplinary research (an ecological handprint) for the energy industry sector for climate-wise solutions are in the focus of many RDI-projects and are included in the HAMK’s strategic research agenda. Multiple energy sources (fuels) and balance in energy costs increase university’s resilience in uncertain future.

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Towards agricultural soil carbon monitoring, reporting, and verification through the Field Observatory Network (FiON)

Cited by 17 publications

References 45 publications

The synergistic role of sulfuric acid, ammonia and organics in particle formation over an agricultural land

The synergistic role of sulfuric acid, ammonia and organics in particle formation over an agricultural land

AgriCarbon-EO: v1.0.1: Large Scale and High Resolution Simulation of Carbon Fluxes by Assimilation of Sentinel-2 and Landsat-8 Reflectances using a Bayesian approach

Climate actions at Häme University of Applied Science (HAMK), Finland

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