Using homosoils to enrich sparse soil data infrastructure: an example from Mali

Many areas in the world suffer from relatively sparse soil data availability. This results in inefficient implementation of soil-related studies and inadequate recommendations for improving soil management strategies. Commonly, this problem is tackled by collecting new soil data which are used to update legacy soil surveys. New soil data collection, however, is usually costly. In this paper, we demonstrate how to find homosoils with the objective of obtaining new soil data for a study area. Homosoils are soils that can be geographically distant but share similar soil-forming factors. We cluster the study area into five areas, and identify a homosoil to each area using distance metrics calculated in the character space spanned by the environmental covariates. In a case study in Mali, we found that large areas in India, Australia and America have similar soil-forming factors to the African Sahelian zone. We collected available soil data for these areas from the WoSIS database. Statistical analysis on the relationship between the homosoils corresponding to different areas of Mali and tree soil properties (clay, sand, pH) displayed the unique variability captured by homosoils. The homosoils could explain 8% of the variation found in the soil datasets. There was a strong association between pH and homosoils corresponding to the semi-arid conditions and sedimentary parent material of Mali, whereas homosoils corresponding to other areas of Mali showed moderate association either with clay or sand. The location and spread of the group centroids were statistically significantly different between depth-specific homosoils for the three soil properties. The approach developed in this paper shows the opportunity for identifying areas in the world with similar soils to populate areas with relatively low soil data density. The concept of homosoils is promising and we envision future applications such as transfer of soil models and agronomic experimental results between areas

Mapping soil organic carbon fractions for Australia, their stocks, and uncertainty

Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuous set of compounds with different chemical compositions, origins, and susceptibilities to decomposition that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contribution of three SOC fractions to the total SOC content of Australia's soils. The three SOC fractions, mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral models calibrated with measured SOC fractions. We transformed the data using an isometric-log-ratio (ilr) transformation to account for the closed compositional nature of SOC fractions. The resulting back-transformed ilr components were mapped across Australia. SOC fraction stocks for 0–30 cm were derived with maps of total organic carbon concentration, bulk density, coarse fragments, and soil thickness. Mapping was done by a quantile regression forest fitted with the ilr-transformed data and a large set of environmental variables as predictors. The resulting maps along with the quantified uncertainty show the unique spatial pattern of SOC fractions in Australia. MAOC dominated the total SOC with an average of 59 % ± 17 %, whereas 28 % ± 17 % was PyOC and 13 % ± 11 % was POC. The allocation of total organic carbon (TOC) to the MAOC fractions increased with depth. SOC vulnerability (i.e. POC/[MAOC+PyOC]) was greater in areas with Mediterranean and temperate climates. TOC and the distribution among fractions were the most influential variables in SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that different mechanisms will control SOC stabilisation and dynamics across the continent, as shown by the model covariates' importance metric. We estimated the total SOC stocks (0–30 cm) to be 13 Pg MAOC, 2 Pg POC, and 5 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and their stocks can be used for modelling SOC dynamics and forecasting changes in SOC stocks as a response to land use change, management, and climate change.</p

Remote sensing of the Earth's soil color in space and time

Soil color is a key indicator of soil properties and conditions, exerting influence on both agronomic and environmental variables. Conventional methods for soil color determination have come under scrutiny due to their limited accuracy and reliability. In response to these concerns, we developed an innovative system that leverages 35 years of satellite imagery in conjunction with in-situ soil spectral measurements. This approach enables the creation of a global soil color map with a fine spatial resolution of 30 m x 30 m. The system initially identifies bare earth areas worldwide using reflectance bands acquired from Landsat 4 through Landsat 8 between 1985 and 2020. Soil color was quantified using the CIE-XYZ coordinates, utilizing 8005 soil spectral measurements within the visible range (380–780 nm) as ground truth data. We established transfer functions to convert Landsat reflectance bands to standardized XYZ color coordinates. These transfer functions were subsequently applied to images of bare surfaces, covering approximately 38.5% of the Earth's surface. We validated the resulting global soil color map using statistical indices derived from an independent set of ground-truth spectral data, demonstrating a high degree of agreement. By creating the world's first global soil color map, we have set a baseline for future spatial and temporal monitoring of soil conditions, thus enhancing our understanding and management of our planet's vital soil resources

Active and healthy ageing for Parkinson's disease patient's support: a user's perspective within the i-PROGNOSIS framework

In this paper, the user requirements, along with the methodology adopted towards their identification within the i-PROGNOSIS framework (www.i-prognosis.eu), are presented. The latter are placed within the concept of active and healthy ageing (AHA), focusing on the case of Parkinson’s Disease (PD) patients’ support. The bases for the user requirements identification were face-to-face sessions, focus groups and a large scale Web-survey. Towards the efficient user requirements identification and i-PROGNOSIS components development, exemplified usage scenarios and related business processes the stakeholders of i-PROGNOSIS can perform, are discussed. Overall, 122 functional and non-functional requirements were identified, serving as a basis for the spiral development model of i-PROGNOSIS, revealing the beneficial role of the users in designing solutions within the AHA concept