Soil moisture is a critical component in many meteorological, hydrological, and agricultural applications, and understanding its spatial and temporal dynamics is vital for the understanding of these processes. Satellite-based remote sensing offers the ability to synoptically capture this spatiotemporal information over large areas, compared to more site-based in-situ field measurements. In this study, we use Sentinel-1 SAR imagery of the River Thames catchment, United Kingdom, over the period 2015 - 2020. A backscatter normalisation process is applied to account for the use of multiple satellite viewing geometries. A change-detection algorithm utilising backscatter power is then applied to the timeseries, to estimate relative surface soil moisture (rSSM) across the study area. To determine information across the large river watershed, smaller sub-catchments, and intra-field scales, the rSSM time series is replicated at multiple spatial scales (1 km, 500m, 250m, and 100m). Although positive biases are present during the growing season of arable farmland, comparison with rainfall data and in-situ soil moisture probes shows there is good agreement with the temporal cycle of soil moisture. These data are being used to evaluate natural flood management by land use and management across a wide area to better understand relationships between surface wetness and water storage in relation to land cover and underlying geology for the Landwise project (Landwise-NFM.org).

Effective knowledge exchange between farmers and other stakeholders, such as agricultural extensionists and soil scientists, is essential for increasing opportunities for sustainable soil fertility management. To achieve this, it is necessary to understand local farmers' conceptualisation of soil fertility. This study visualizes farmers' perceptions of soil fertility as mental models, in order to explore the expansion of their soil knowledge and the extent of their comprehension of the relationship between soil properties that are seen and measured and soil processes. Aggregated mental models of fertile and low fertility soils were created from data collected from 59 farmer interviews at two villages in Kitui County, Kenya. The share of respondents of each concept were shown to analyse the knowledge gaps among farmers and between villages. The mental models revealed that farmers recognize the important roles of soil texture, water availability and farm management in soil fertility. Their knowledge related to their lived experience of the actual productivity of soils, which resulted in a strongly different perspective of fertile and low fertility soil. The differences of perception between the villages were also recognized as the result of differences in land availability. Although the farmers who mentioned soil processes were very few, farmers had the potential to integrate further soil scientific knowledge. Consequently, using the mental model approach to visualize farmers' perceptions produced benefits by clarifying understanding of farmers' knowledge and identifying gaps where soil science and extension work could help to expand farmers' knowledge.