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SSS8.4

EDI
Geoinformation Technologies in Sustainable Soil Management

Soil is one of the most important substances on Earth. Information on our planet’s soil resources is indispensable to a number of practical applications related to both society and ecosystems. Globally, Earth’s soils resources monitoring has developed into very important and urgent research directions, especially in the face of climate change.
Geoinformation technologies, primarily of Earth Observation (EO), Geographical Information Systems (GIS), GPS/GNSS along with Big Data, Cloud Computing, the Internet of Things, Deep Learning and Artificial Intelligence can successfully support sustainable soil management. The rapid growth in the geoinformation sector combined with the continuous availability of new geospatial data provides important support to analyze soil characteristics and their three-dimensional patterns. As such, it allows exploiting interdisciplinary information and datasets to better understand soil functions and monitoring land changes towards more sustainable soils management and sustainability, although the capacity to predict the physical and morphological features of soils and to integrate this information in prediction of other relevant soil properties is still limited.
In this session, we welcome contributions covering inter and transdisciplinary research through theoretical and applied studies, on the application of all range of geoinformation technologies as an opportunity for innovation and competitiveness for example in agricultural soil management, precision agriculture and precision livestock farming.

Convener: Dimitris Triantakonstantis | Co-conveners: Ilze Vircava, Claudio Zucca, Lukasz MendykECSECS, Manika GuptaECSECS, Blaz Repe, Salim LamineECSECS, Daniela Silva-FuzzoECSECS
Presentations
| Wed, 25 May, 08:30–10:00 (CEST)
 
Room -2.47/48

Wed, 25 May, 08:30–10:00

Chairpersons: Dimitris Triantakonstantis, Cenk Donmez, Spyridon E. Detsikas

08:30–08:33
Introduction

08:33–08:43
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EGU22-12008
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solicited
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Highlight
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Virtual presentation
George P. Petropoulos et al.

Frost is one of the most damaging hazards in agriculture as its impacts negatively cropland yield and agro-ecosystems, affecting price commodities of agricultural products. Locating the spatiotemporal patterns of frost events can be a challenging and costly task since a dense network of weather stations is required to accurately characterize frost distribution. The recent advancements in geoinformation technology have enhanced our ability to retrieve parameters that are critical to the development of frost conditions such as land surface temperature (LST). In addition, the availability of cloud-based imagery processing platforms allows to easily acquire and process LST data over large scales setting the EO field as the optimal mean for frost risk mapping. The present study imprints the frost’s spatial patterns analyzing geospatially referenced frost incident field-based data conducted by the Greek National of Agricultural Payments Agency (ELGA) during the period 2015-2020. In addition, a cloud-based methodological framework is introduced herein based on a time series analysis with LST data from ESA's Sentinel-3 LST operational product to map frost occurrence. The proposed approach was implemented for the same time period as that of the ELGA data. The frost frequency maps developed by the two approaches were  compared using appropriate geospatial data analysis methods in order to determine their correspondence. Results generally demonstrated the added value of EO data in identifying the frost risk degree and geographical extent for all the years analysed. Our proposed methodology has a promising potential to be used at operational scale to map frost risk conditions and to also support decision making in frost hazard mitigation.

KEYWORDS:  cloud-based platform, LST,  Sentinel 3, frost risk, geospatial analysis

How to cite: Petropoulos, G. P., Detsikas, S. E., and Sandric, I.: A methodological framework  for mapping frost occurrence utilizing a cloud-based platform & geospatial data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12008, https://doi.org/10.5194/egusphere-egu22-12008, 2022.

08:43–08:50
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EGU22-10139
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Highlight

An original GIS-based procedure was developed to map SOC stocks across four study areas representing different bioclimatic regions of Turkey, Northeastern Anatolia (NEA), Thrace (THR), Central Anatolia (CA), and Southeastern Anatolia (SEA), over a total surface of around 148,000 km2. A dataset of 4151 georeferenced topsoil (0-20 cm) soil organic carbon (SOC) point samples was used, along with climate, soil type, and land cover maps.

C-Stock maps were elaborated independently in each study region. Average C-Stock values were assigned to “landscape” polygon units representing combinations of WRB soil type, land cover, and climate, based on the point data included in such units. The obtained values were extrapolated to similar landscape units for which point data were not available. This procedure allowed highlighting the effects on soil carbon of contemporary land cover. The effects of past and recent land use were incorporated by acquiring historical information on land management in traditional landscape systems (Anthroscapes) and its contribution to preserve the current soil carbon reserves. The overall total calculated C-Stock was 486.8 Tg with an average value of 31.5 Mg ha-1. Average SOC stock values per hectare were highest (47.1 Mg ha-1) in the cool-humid NEA, lowest (22.1 Mg ha-1) in the semi-arid SEA, and moderately low (27.3 and 25.6 Mg ha-1) in the dry continental CA and in the Mediterranean THR regions.

Averaging carbon stock data over landscape units (nested climate, soil, land cover information), instead of using polygons to summarize gridded data obtained by spatial interpolation made the output maps and data more easily interpretable and usable to support the development of sustainable land management policies and to link carbon sequestration to other ecosystem services targets. The findings can be used for the definition of realistic carbon sequestration and soil health targets considering the potential determined by local climate and soil conditions, and land use.

How to cite: Zucca, C. and the SOC in Turkish Landscapes international team: Soil organic carbon stocks in climatic and soil regions of Turkey mapped by a pedology-based GIS procedure, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10139, https://doi.org/10.5194/egusphere-egu22-10139, 2022.

08:50–08:57
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EGU22-11074
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ECS
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Virtual presentation
Jesús Barrena González et al.

Identifying the most appropriate interpolation method for a given area is a necessary step to properly map soil properties. The aim of this work was to evaluate the accuracy and usefulness of 9 interpolation methods (deterministic and geostatistical) with data of 12 soil properties: clay, silt, and sand content, pH, cation exchange capacity, and calcium, magnesium, sodium, nitrogen, phosphorus, potassium and organic matter content, that were measured at three different depth ranges, i.e., 0-5 cm, 5-10 cm and > 10 cm. Data gathered from more than 400 sampling points were used to map these soil properties in the Spanish region of Extremadura (ca. 41,000 km2 of land surface). Data showed a high variability, both in terms of the different parameters and the depth intervals. The coefficient of determination (r2) and root mean square error (RMSE) were used as statistics to determine the accuracy and decide the most suitable interpolation method in each case. The results were variable, and the most appropriate interpolation method varied according to the property of the soil and the depth under consideration. As some instances, for the 0-5 cm clay content data the best method was the ordinary kriging (0.714 r2 and 3.629 RMSE), while for the 5-10 cm data Spline with Tension (0.56 r2 and 5.855 RMSE) produced better results. In the case of pH values, however, the Completely Regularized Spline method yielded good results both for depths of 0-5 cm (0.678 r2 and 0.487 RMSE) and 5-10 cm (0.610 r2 and 0.603 RMSE), being preferable the ordinary kriging to the depths > 10 cm (0.667 r2 and 0.639 RMSE). In general, it was the Inverse Distance Weighting (IDW) method which showed the best results, followed by other deterministic methods such as Completely Regularized Spline (CRS) and Spline with Tension (SwT). Furthermore, Empirical Bayesian Kriging (EBK) was the geostatistical method that yielded the best results.  In view of the results obtained, the need to consider various interpolation methods when mapping soil properties becomes evident.

How to cite: Barrena González, J., Gabourel, A., and Lavado Contador, J. F.: Assessing of the accuracy of interpolation methods to map soil properties at regional scale in Extremadura (SW Spain), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11074, https://doi.org/10.5194/egusphere-egu22-11074, 2022.

08:57–09:04
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EGU22-9949
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Highlight
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On-site presentation
Dimitris Triantakonstantis et al.

Carbon farming has been proposed as one of the primary pilots of the upcoming CAP by the European Commission and European Parliament. Subsidies will be tied to carbon farming, necessitating the development of practical methods to assess farmers' carbon balance. The so-called Activity Data are one of the most common gaps preventing a thorough assessment of carbon balance at both the micro and macro levels. In addition, the accuracy and completeness of existing estimates of the carbon sink capacity of agricultural soils remains until today under-addressed. These obstacles are now reflected in National GHG Inventory Reports, which use default emission factors for most Mediterranean Nations, resulting in Tier 1 reporting status. Thus, for climate change mitigation, a more precise evaluation of the changes in the carbon balance of soil in relation to agricultural management techniques could be extremely helpful. 
In this presentation we introduce the recently initiated EU-funded GEOCARBON project, (http://www.lifegeocarbon.eu) providing a detailed overview of the project’s research aims and objectives as well as the first results from its implementation. The project aims to address the lack of farming-level knowledge systems by enhancing existing earth-based data with a structured, harmonized geospatial framework system that is ready to be used as input to the Carbon Farming Calculation Tool. 
The project outputs will combine all existing knowledge databases to facilitate the development of an interactive Carbon Farming Calculation tool. The project's principal deliverable will be a demonstration high-resolution geospatial information system that will collect relevant data (e.g., climate, landscape elements, management practices, etc.). This will be used to determine the potential for climate change mitigation at farm level, as well as to design and implement targeted Carbon Farming strategies. The GEOCARBON project will geo-locate (at agricultural parcel level) a representative sample of farms and offer quantitative and qualitative statistics on earth-based data relevant to the agricultural sector.
The geospatial framework system that will be developed in GEOCARBON will be a critical step towards carbon precise calculation at farm level, employing IT-based decision support tools as part of a climate change mitigation plan. GEOCARBON deliverables are expected to enhance farmers' knowledge towards strengthening the so-called Activity data for the LULUCF sector. Farmers will be able to use a cost-effective mobile application on the field to record their management techniques (cultivation methods).

KEYWORDS: carbon farming, GEOCARBON, geoinformation, webgis

How to cite: Triantakonstantis, D., Bithas, K., Detsikas, S. E., Petropoulos, G. P., Calzolari, C., Vaccari, F., Pascual, J., Ros, M., and Guerrero, C.: Establishing a Spatial Soil Database Management System to Support Carbon Farming Geolocation: Introducing the LIFE GEOCARBON Preparatory Project, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9949, https://doi.org/10.5194/egusphere-egu22-9949, 2022.

09:04–09:11
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EGU22-10847
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ECS
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Virtual presentation
Demis Andrade Foronda

Salt-affected soils are characterized by an excess of soluble salts and/or sodium. The widely used US Salinity Lab (USSL) classification considers the exchangeable sodium percentage (ESP), electrical conductivity (EC) and pH. Breiman and Cutler's random forests (RF) algorithm chooses the most voted class over all the trees at training time. The aim was to model and predict the USSL salt-term categories from soluble ions by applying RF model classification. Topsoil samples (110) were collected from the High Valley of Cochabamba (Bolivia) and were analyzed to measure the soluble cations (Na+, K+, Ca2+, Mg2+) and anions (HCO3, Cl, CO32–, SO42–), in addition to the required salinity parameters to classify the samples according to the thresholds: ESP of 5%, ECe of 4 dSm-1 and pH of 8.2. No samples matched in the saline category. The overall out-of-bag error was 17.4% and according to the confusion matrix, the class errors for normal, saline-sodic and sodic soil were 0.12, 0.00 and 0.36, respectively. The variables with higher estimated importance and also selected by RF backward elimination were: Na+, Cl, Ca2+ and HCO3. Additional sampling might be useful in order to reduce the errors and misclassifications, as well as to improve the selection of variables.

How to cite: Andrade Foronda, D.: Random Forests to classify salt-affected soils from soluble salt ions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10847, https://doi.org/10.5194/egusphere-egu22-10847, 2022.

09:11–09:18
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EGU22-10106
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Virtual presentation
Smart application for soil description
(withdrawn)
Nadezda Vasilyeva et al.
09:18–09:25
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EGU22-4555
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On-site presentation
Cenk Donmez et al.

Long-Term Field Experiments (LTEs) are agricultural experiments for monitoring soil and crop properties in changing climate conditions and different management practices. These trials were set up on various soil textures and types to reveal the effects of management and environment on crop production and soil resources. Although the LTEs are essential infrastructures for sustainable soil yield and use, LTE-related information was dispersed, thus not easy to find. To close this research gap, we compiled and analyzed the meta-information of the LTEs across Europe and their spatial representation in a geospatial data infrastructure, including a data repository and an LTE overview map developed within the framework of the BonaRes project (BonaRes 2021; Grosse et al. 2021). During the research, LTEs with a minimum duration of 20 years were identified, and the meta-information was collected by extensive literature review and factsheets. In total, 405 LTEs in Europe were identified, clustered in different categories (management operations, land use, duration, status, etc.), and these clusters were geospatially analysed to provide inputs for the agricultural industry, scientists and decision-makers. LTEs from 25 countries were utilized including Germany, where the oldest LTE started in 1843. The majority of the LTEs have the fertilization treatment, followed by crop rotation and tillage. The results will help to develop a mutual agricultural management framework by revealing the LTE potential internationally. The geospatial data structure will contribute to scaling up the management practices from site to landscape-level for increasing the adaptation of agricultural systems to climate change.

Key Words: Long-Term Field Experiments, BonaRes, Europe, soil science, GIS.

References

BonaRes (2021). Long-term Experiments in Europe. Overview of long-term Experiments. https://lte.bonares.de

Grosse, M., Ahlborn, M.C., Hierold, W. (2021). Metadata of agricultural long-term experiments in Europe exclusive of Germany. Data in Brief 38, https://doi.org/10.1016/j.dib.2021.107322

 

How to cite: Donmez, C., Grosse, M., Hierold, W., and Schmidt, M.: Assessing Long-Term Field Experiments of Europe through a geospatial data infrastructure, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4555, https://doi.org/10.5194/egusphere-egu22-4555, 2022.

09:25–09:32
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EGU22-3323
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Virtual presentation
Graciela Metternicht et al.

Digital terrain models (DTM) allow deriving topographic attributes that help predict soil properties within a landscape. A variety of DTMs, digital elevation models (DEMs), and digital surface models (DSM) derived from Earth Observation (EO) data are freely accessible via Internet for download and use: MERIT-DEM, SRTM v3 (SRTM Plus), GDEM v3 (ASTGTM), AW3D30 v3, Copernicus GLO-30, NASADEM HGT v1, SRTMGL1 up-sampled (ASF DAAC) and MDE-Ar v2.  However, information on their accuracy to represent terrain surfaces (particularly topographic attributes) can vary according to regions and geographies, which can impact soil cartography accuracy at sub-regional and catchment levels. This research evaluates the accuracy of the models mentioned above for estimating topographic attributes relevant to the cartography of soil vertic properties in the northern part of the Entre Ríos province, Argentina. To this end, east-west and north-south transects were used to collect 126 evenly distributed ground control points. The root mean squared error (RMSE) and symmetric mean absolute percentage error (sMAPE) served as the basis for comparing the performance of the terrain models. The sMAPE provides a percentage (or relative) error, facilitating a comparison of the accuracy with which each elevation value is predicted (in addition to the average error expressed by the RMSE value).

The results show that out of the 8 models compared, the Copernicus GLO-30 offers the highest accuracy (RMSE=1.36; sMAPE=1.5%) for representing terrain surface features in the province of Entre Rios, whereas the highest RMSE (7.79) and sMAPE (11.2%) corresponded to the ASTGTM v3. The paper describes a simplified approach for extracting a digital terrain model (DTM) from the digital elevation information provided in the Copernicus GLO-30. Grid-spline interpolation and multilevel b-spline interpolation (from SAGA open GIS software) were applied to remove natural and built features. The output DTM was used to calculate plan and profile curvature index, multi-scale topographic position index (TPI), multiresolution index of valley bottom flatness (MrVBF), terrain ruggedness index (TRI), and topographic wetness index (TWI) that are important in modelling relationships between geomorphology, vertic soils, and surface hydrology in landscapes characterized by catenary sequences of Mollisols-Alfisols-Vertisols. A higher TRI was associated to increased local relief heterogeneity. Higher values of the MrVBF relate to broad flat valley bottoms and more extensive alluvial zones often confined between the slightly rolling and undulating plains, and peneplain landscapes. Lastly, the TWI was used to map potential areas for surface water accumulation that field verifications showed as corresponding with the location of vertic soils.

Integrating DTM-derived topographic attributes with other ancillary data enabled mapping the spatial distribution of soil vertic properties over the study area and associating their occurrence to specific landscape zones (ie. close to drainage networks). The approach and findings are relevant for showing where and how the landscapes of the Entre Rios province are affected by a combined impact of human activities (intensive agriculture) and a hydrographic network that boosts the processes of soil erosion and contaminant transport.

How to cite: Metternicht, G., del Valle, H., Tentor, F., Sione, W., Zamboni, P., and Aceñolaza, P.: Quality assessment of open access Digital Terrain Models to estimate topographic attributes relevant to soil vertic properties prediction. A case study of Entre Rios province (Argentina), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3323, https://doi.org/10.5194/egusphere-egu22-3323, 2022.

09:32–09:39
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EGU22-4590
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ECS
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On-site presentation
Valeria Medoro et al.

Zeoliva is a 3-years project financed by the Italian Ministry of Agricultural, Forestry and Food Policies (MIPAAF) and led by the University of Ferrara and the National Research Council of Bologna. The project goals are the improvement of soil quality and the contrasting of olive fruit fly infestation (Bactrocera oleae) by means of natural and sustainable methods which implies the reduction of chemical fertilizer and pesticide inputs in olive growing. To reach these objectives, natural zeolites (chabazite-rich tuff from central Italy) were used both as foliar treatments (micronized form, WP2) and as a soil amendment (granular form, WP3) in various experimental sites located in the Emilia-Romagna region (Italy). Two sites (Site A: organic, Site B: conventional) were dedicated to WP2 on adult olive trees to reduce the use of chemical pesticides (Dimethoate) or as an alternative to chemical traps (Spintorfly) in fighting Bactrocera oleae. In Site A, the tested treatments were: natural zeolite (ZN), NH4+ enriched zeolite (ZA) and conventional practices of Spinosad+Spyntor Fly on traps (SF); in Site B: zeolite+Dimethoate (ZN-DM), Dimethoate (DM) and no foliar treatment (CNT). Leaves and olives were analyzed by ICP-MS to identify which Major Elements and Rare Earth Elements (REE) can be used to label the origin of the final products for food traceability purposes. 

REE showed a higher trend for the treatments with zeolite (ZN and ZA in site A; ZN in site B) than in the control (CNT) and conventional treatments (SF or DM). Statistical analyses such as Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the dataset. On one hand, the PCA indicated differences between groups of plants treated with zeolite and those without, both in leaves and olives matrices; on the other hand, PLS-DA pointed out that some elements (Rb, Zr, Nb, La and Th in site A; Sm and Dy in site B) can be potentially suitable as markers for olives traceability.

Concerning WP3, experimentations (2019-2021) took place in three sites on young olive trees (⁓ 3 years old) in which 500 g of natural zeolites were added at transplant. Plants grown on soil amended with natural zeolites (ZN) were compared to those grown on unamended soil (CNT). Fertilizer inputs were reduced by 50% in ZN plants to demonstrate the beneficial effects of natural zeolites on soil N retention. At each site, soils and leaves were sampled three times in three replicates per treatment: March-April (Pre-Fertilization), May-June (Post-Fertilization) and October-November (Harvest) each year. Soil and leaves total N, soil NO3--N, NO2-N and anions in H2O extracts were monitored to evaluate differences between treatments.

Soil and leaves N content was not significantly different between ZN and CNT in most cases at each site. Given that in ZN treatment the N input was reduced by 50% and that crop N uptake was similar, it can be assumed that fewer N losses occurred in ZN treatments thanks to the presence of natural zeolites.

How to cite: Medoro, V., ferretti, G., Rotondi, A., Galamini, G., Morrone, L., Faccini, B., and Coltorti, M.: Pesticides reduction, improvement of soil quality and olive traceability by means of zeolite-rich tuff: the Zeoliva project, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4590, https://doi.org/10.5194/egusphere-egu22-4590, 2022.

09:39–09:46
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EGU22-11002
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ECS
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Virtual presentation
Damini Sarma et al.

Soil properties play an inevitable role in hydrological modelling and there by affecting the quantification of energy and water fluxes both regionally and locally. However, estimating soil properties through field experiments is still a challenge and the literature values do not represent the regional or local areas. The Earth observation datasets can provide the soil surface information. However, the current datasets are at coarse resolution and cannot be utilized for field level agriculture. This study inversely derives the soil hydraulic properties at 30 m resolution by downscaling various microwave Earth observation datasets. The downscaling of soil moisture is achieved with utilization of LANDSAT land surface temperature and normalized difference vegetation index. This product will help in better estimation of hydrological fluxes and also soil management for various hydrological applications and site-specific studies. The accuracy of the algorithm used in the present study is validated at the test site. The method proposed in this study can be implemented in regions where in-situ acquisition of soil properties is not feasible.

How to cite: Sarma, D., Gupta, M., Thomas, J., and Srivastava, P.: Comparison of soil hydraulic properties determined from multiple Earth observation datasets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11002, https://doi.org/10.5194/egusphere-egu22-11002, 2022.

09:46–09:53
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EGU22-2534
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Virtual presentation
Zhihan Yang et al.

As a crucial process of global carbon cycle, soil respiration (RS) is one of the largest out flux of carbon dioxide from terrestrial ecosystems to atmosphere. The temperature sensitivity of RS (Q10) was considered as a benchmark in describing terrestrial soil carbon-climate responses. However, the spatiotemporal and dominant factors of Q10 were not explored well at regional scale. To bridge the knowledge gap, we derived a gridded dataset of Q10 from 1994 to 2016 across China (data-derived Q10) by using a random forest (RF) model with the linkage of 515 field observations and environmental variables. The model efficiency of RF was 0.5 with root mean squared error (RMSE) of 0.62. Spatially, data-derived Q10 varied a lot from 1.54 to 4.17 with an average of 2.52, and were higher in cold regions. Temporally, the annual change of data-derived Q10 was not significant (p = 0.28). To investigate the dominant factors, we used partial correlation analysis to detect the relationships between data-derived Q10 and annual mean temperature (MAT), annual mean precipitation (MAP) and soil organic carbon (SOC). Generally, SOC was the most dominant factor which covered 46 % of land surface across China, followed by MAT (29 %) and MAP (25 %). However, there was a strong spatial heterogeneity of the proportions of dominant factors in different climatic zones, ecosystem types, and climatic conditions. Among different ecosystems, the percentage of areas dominated by MAT in grasslands (34 %) and wetlands (31 %) were higher than that of other ecosystem types (less than 25 %). Under different MAP gradients, it can be observed that the percentage of areas dominated by MAP was higher when MAP is extremely high (> 1600 mm) or extremely low (0 ~ 200 mm), which were 31 % and 29 %, respectively, higher than that at 800 ~ 1000 mm (16 %). In our results, percentage of areas dominated by MAT was higher in cold regions. As MAT increased, the percentage of areas dominated by MAT gradually decreased, and it was 33 % at MAT <-5℃, higher than when MAT at 15 ~ 20℃ (23 %). Similarly, this phenomenon was more intuitive along the Q10 gradient, the percentage of areas dominated by MAT gradually increased from 22 % (Q10 < 2) to 56 % (Q10 > 3.5). Also, this phenomenon could be observed across different climatic zones. Except for the smallest tropical regions, from subtropical to temperate to plateau regions, the local temperature gradually decreased while the percentage of areas dominated by MAT also gradually increased (from 24 % to 36 %). Our results showed that in colder regions, the temperature influenced Q10 more significantly, which may indicate that future Q10 variations in cold regions may be more notable than in warm regions in a warming climate. This study was supported by National Natural Science Foundation of China (31800365), State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (SKLGP2021K024).

How to cite: Yang, Z., Tang, X., Zhou, T., and Luo, X.: Various dominant factors of temperature sensitivity of soil respiration across China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2534, https://doi.org/10.5194/egusphere-egu22-2534, 2022.

09:53–10:00
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EGU22-11703
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ECS
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On-site presentation
Spyridon E. Detsikas et al.

Large-scale soil moisture monitoring is a critical element of sustainable intensification of agricultural land. Remote sensing provides the way forward required for nationwide soil moisture monitoring coverage. On the other, utilising cosmic-ray neutron probes is a relatively new approach for obtaining larger area soil moisture and various relevant operational monitoring networks have been established worldwide utilising this technology to measure operationally this parameter.

This study compares retrievals of soil moisture between the COSMOS-UK cosmic-ray soil moisture observation network and the Synthetic-Aperture-Radar Soil Water Index (SCAT-SAR SWI) product across selected COSMOS-UK sites. A further objective has been to investigate the true footprint and the variations within the footprint detectable area at the COSMOS-UK sites using as a case study one such site located in Riseholme, UK. At the selected experimental site extensive fieldwork was conducted in July 2017 that allowed objectively examining the agreement between the truth data of the TDT soil moisture sensors and the COSMOS-UK product for soil moisture.

It was found that the true footprint of this COSMOS-UK station was representative for an area smaller than the general assumed footprint of 600m diameter, as generally proposed in various relevant investigations. The COSMOS network slightly overestimated soil moisture content measured by the Time Domain Transmissometry (TDT) sensor probes installed in the area.  Results of our study contribute towards efforts to assess the COSMOS-UK soil moisture measurement footprint demonstrating the added value of geospatial analysis techniques for this purpose.

Results showed a strong correlation between the true data of the Time Domain Transmissometry soil moisture sensors and the COSMOS and SCAT-SAR products for soil moisture. In addition, the true footprint of this COSMOS-UK station was discovered to be reflective of a smaller area than the usually accepted footprint of 600m diameter, as proposed in many relevant studies.

Results of our study contribute towards efforts to assess the COSMOS-UK soil moisture measurement footprint demonstrating the added value of geospatial analysis techniques for this purpose. Further scrutiny of the technique is required to establish its applicability to different areas and ecosystems. Such an investigation would require exploring the prediction accuracy of the technique for other sites would have other contributing features such as slopes, land cover differentiation and penetrating vegetation such as hedgerows which could drastically affect the footprint of the probes. All the above are topics of key importance to be taken up by future studies exploiting neutron probe data in the context of soil moisture retrievals.

Keywords: COSMOS UK; SCAT-SAR SWI; Soil Moisture Monitoring; Spatial Analysis; Remote Sensing

How to cite: Detsikas, S. E., Howells, O. D., Ioannou, Z., Petropoulos, G. P., Triantakonstantis, D., Srivastava, P. K., and Stavroulakis, G.: Soil Surface Moisture retrievals from EO and cosmic ray- based approach for selected sites in the UK, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11703, https://doi.org/10.5194/egusphere-egu22-11703, 2022.