Abstract. The drawbacks of low-coverage rate in global land inevitably exist in satellite-based daily soil moisture products because of the satellite orbit covering scopes and the limitations of soil moisture retrieving models. To solve this issue, Zhang et al. (2021a) generated seamless global daily soil moisture (SGD-SM 1.0) products for the years 2013–2019. Nevertheless, there are still several shortages in SGD-SM 1.0 products, especially in temporal range, sudden extreme weather conditions and sequential time-series information. In this work, we develop an improved seamless global daily soil moisture (SGD-SM 2.0) dataset for the years 2002–2022, to overcome the above-mentioned shortages. The SGD-SM 2.0 dataset uses three sensors, i.e. AMSR-E, AMSR2 and WindSat. Global daily precipitation products are fused into the proposed reconstructing model. We propose an integrated long short-term memory convolutional neural network (LSTM-CNN) to fill the gaps and missing regions in daily soil moisture products. In situ validation and time-series validation testify to the reconstructing accuracy and availability of SGD-SM 2.0 (R: 0.672, RMSE: 0.096, MAE: 0.078). The time-series curves of the improved SGD-SM 2.0 are consistent with the original daily time-series soil moisture and precipitation distribution. Compared with SGD-SM 1.0, the improved SGD-SM 2.0 outperforms on reconstructing accuracy and time-series consistency. The SGD-SM 2.0 products are recorded in https://doi.org/10.5281/zenodo.6041561 (Zhang et al., 2022).


/pdf/sgd-sm-2-0-an-improved-seamless-global-daily-soil-moisture-rs3z6o8a.pdf

SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022

While significant progress has been made in understanding global carbon (C) cycling, the mechanisms regulating belowground C fluxes and storage are still uncertain. New molecular technologies have the power to elucidate these processes, yet we have no widespread standardized implementation of molecular techniques. To address this gap, we introduce the Molecular Observation Network (MONet), a decadal vision from the Environmental Molecular Sciences Laboratory (EMSL), to develop a national network for understanding the molecular composition, physical structure, and hydraulic and biological properties of soil and water. These data are essential for advancing the next generation of multiscale Earth systems models. In this paper, we discuss the 1000 Soils Pilot for MONet, including a description of standardized sampling materials and protocols and a use case to highlight the utility of molecular-level and microstructural measurements for assessing the impacts of wildfire on soil. While the 1000 Soils Pilot generated a plethora of data, we focus on assessments of soil organic matter (SOM) chemistry via Fourier-transform ion cyclotron resonance-mass spectrometry and microstructural properties via X-ray Computed Tomography to highlight the effects of recent fire history in forested ecosystems on belowground C cycling. We observed decreases in soil respiration, microbial biomass, and potential enzyme activity in soils with high frequency burns. Additionally, the nominal oxidation state of carbon in SOM increased with burn frequency in surface soils. This results in a quantifiable shift in the molecular signature of SOM and shows that wildfire may result in oxidation of SOM and structural changes to soil pore networks that persist into deeper soils.

/pdf/one-thousand-soils-for-molecular-understanding-of-3uge4mzy.pdf

One thousand soils for molecular understanding of belowground carbon cycling

Knowledge about global patterns of the decomposition kinetics of distinct soil organic matter (SOM) pools is crucial to robust estimates of land-atmosphere carbon fluxes under climate change. However, the current Earth system models often adopt globally-consistent reference SOM decomposition rates (kref), ignoring effects from edaphic-climate heterogeneity. Here, we compile a comprehensive set of edaphic-climatic and SOM decomposition data from published incubation experiments and employ machine-learning techniques to develop models capable of predicting the expected sizes and kref of multiple SOM pools (fast, slow, and passive). We show that soil texture dominates the turnover of the fast pools, whereas pH predominantly regulates passive SOM decomposition. This suggests that pH-sensitive bacterial decomposers might have larger effects on stable SOM decomposition than previously believed. Using these predictive models, we provide a 1-km resolution global-scale dataset of the sizes and kref of these SOM pools, which may improve global biogeochemical model parameterization and predictions. 

/pdf/global-patterns-and-edaphic-climatic-controls-of-soil-carbon-dhlxitvi.pdf

Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments

Abstract While significant progress has been made in understanding global carbon (C) cycling, the mechanisms regulating belowground C fluxes and storage are still uncertain. New molecular technologies have the power to elucidate these processes, yet we have no widespread standardized implementation of molecular techniques. To address this gap, we introduce the Molecular Observation Network (MONet), a decadal vision from the Environmental Molecular Sciences Laboratory (EMSL), to develop a national network for understanding the molecular composition, physical structure, and hydraulic and biological properties of soil and water. These data are essential for advancing the next generation of multiscale Earth systems models. In this paper, we discuss the 1000 Soils Pilot for MONet, including a description of standardized sampling materials and protocols and a use case to highlight the utility of molecular-level and microstructural measurements for assessing the impacts of wildfire on soil. While the 1000 Soils Pilot generated a plethora of data, we focus on assessments of soil organic matter (SOM) chemistry via Fourier-transform ion cyclotron resonance-mass spectrometry and microstructural properties via X-ray Computed Tomography to highlight the effects of recent fire history in forested ecosystems on belowground C cycling. We observed decreases in soil respiration, microbial biomass, and potential enzyme activity in soils with high frequency burns. Additionally, the nominal oxidation state of carbon in SOM increased with burn frequency in surface soils. This results in a quantifiable shift in the molecular signature of SOM and shows that wildfire may result in oxidation of SOM and structural changes to soil pore networks that persist into deeper soils. 

/pdf/one-thousand-soils-for-molecular-understanding-of-3j4fy5a6.pdf

Remote and near surface sensing data are widely used in archaeology and heritage management for feature discovery, change detection and monitoring, as an input to predictive modelling, and in the planning process. While global and regional datasets are widely used for some purposes, data are regularly acquired specifically for archaeological projects because of the very high spatial resolution required for feature detection and assessments of archaeological significance and the need for data on subsurface features. The sensing data collected for archaeology cover limited areas and only a few types of sensors, known to produce data efficiently, are regularly employed. Precision agriculture is beginning to produce large quantities of varied sensing data across extensive landscape areas. This situation creates an opportunity to adapt and reuse precision agricultural data for archaeology and heritage work, extending covering and enhancing our understanding of archaeology in contemporary agricultural landscapes. Equally, there is potential for coordinated data collection, collecting data once for multiple applications, and to add value through analyses which bring together perspectives from multiple related domains to model long-term processes in anthropogenic soil systems. This article provides a high-level overview of policy and technological developments which create the potential for sensing data reuse, coordinated data collection, and collaborative analyses across archaeological, agricultural, and agri-environmental applications while underscoring the structural barriers which, at present, constrain this potential. It highlights examples where the development of interoperable data and workflows can promote tighter integration of archaeology and cultural heritage management with sustainable agricultural land management and support integrated decision making.

Remote Sensing Data to Support Integrated Decision Making in Cultural and Natural Heritage Management. Impasses and opportunities for collaboration in agricultural areas

Abstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study.
Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight.
Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators.
Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication.
We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices.
Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.


/pdf/reviews-and-syntheses-the-promise-of-big-diverse-soil-data-3jteftge.pdf

Marisa L. Younger

Papers

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential