Reliable and high-resolution subsurface characterization beyond the field scale is of great interest for precision agriculture and agro-ecological modelling because the shallow soil (~1-2 m depth) is responsible for the storage of moisture and nutrients that are accessible to crops. This can potentially be achieved with a combination of direct sampling and Electromagnetic Induction (EMI) measurements, which have shown great potential for soil characterization due to their noninvasive nature and high mobility. However, only a few studies have used EMI beyond the field scale because of the challenges associated with a consistent interpretation of EMI data from multiple fields and acquisition days. In this study, we performed a detailed EMI survey of an area of 1 km 2 divided in 51 agricultural fields where previous studies showed a clear connection between crop performance and soil properties. In total, nine apparent electrical conductivity (ECa) values were measured at each location with a depth of investigation ranging between 0-0.2 to 0-2.7 m. Based on the combination of ECa maps and available soil maps, an a priori interpretation was performed and four sub-areas with characteristic sediments and ECa were identified. Then, a supervised classification methodology was used to divide the ECa maps into areas with similar soil properties. In a next step, soil profile descriptions to a depth of 2 m were obtained at 100 sampling locations and 552 samples were analyzed for textural characteristics. The combination of the classified map and ground truth data resulted in a 1 m resolution soil map with eighteen units with a typical soil profile and texture information. It was found that the soil profile descriptions and texture of the EMI-based soil classes were significantly different when compared using a two-tailed t-test. Moreover, the high-resolution soil map corresponded well with patterns in crop health obtained from satellite imagery. It was concluded that this novel EMI 3 data processing approach provides a reliable and cost-effective tool to obtain high-resolution soil maps to support precision agriculture and agro-ecological modelling.

Agroecosystem models that simulate crop growth as a function of weather conditions and soil characteristics are among the most promising tools for improving crop yield and achieving more sustainable agricultural production systems. This study aims at using spatially distributed crop growth simulations to investigate how field-scale patterns in soil properties obtained using geophysical mapping affect the spatial variability of soil water content dynamics and growth of crops at the square kilometer scale. For this, a geophysics-based soil map was intersected with land use information. Soil hydraulic parameters were calculated using pedotransfer functions. Simulations of soil water content dynamics performed with the agroecosystem model AgroC were compared with soil water content measured at two locations, resulting in RMSE of 0.032 and of 0.056 cm 3 cm −3 , respectively. The AgroC model was then used to simulate the growth of sugar beet (Beta vulgaris L.), silage maize (Zea mays L.), potato (Solanum tuberosum L.), winter wheat (Triticum aestivum L.), winter barley (Hordeum vulgare L.), and winter rapeseed (Brassica napus L.) in the 1-by 1-km study area. It was found that the simulated leaf area index (LAI) was affected by the magnitude of simulated water stress, which was a function of both the crop type and soil characteristics. Simulated LAI was generally consistent with the observed LAI calculated from normalized difference vegetation index (LAI NDVI) obtained from RapidEye satellite data. Finally, maps of simulated agricultural yield were produced for four crops, and it was found that simulated yield matched well with actual harvest data and literature values. Therefore, it was concluded that the information obtained from geophysics-based soil mapping was valuable for practical agricultural applications.

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For the first time, we combine depth‐specific soil information obtained from the quantitative inversion of ground‐based multicoil electromagnetic induction data with the airborne hyperspectral vegetation mapping of 1 × 1‐m pixels including Sun‐induced fluorescence (F) to understand how subsurface structures drive above‐surface plant performance. Hyperspectral data were processed to quantitative F and selected biophysical canopy maps, which are proxies for actual photosynthetic rates. These maps showed within‐field spatial patterns, which were attributed to paleo‐river channels buried at around 1‐m depth. The soil structures at specific depths were identified by quantitative electromagnetic induction data inversions and confirmed by soil samples. Whereas the upper plowing layer showed minor correlation to the plant data, the deeper subsoil carrying vital plant resources correlated substantially. Linking depth‐specific soil information with plant performance data may greatly improve our understanding and the modeling of soil‐vegetation‐atmosphere exchange processes.

Cosmic ray neutron (CRN) sensing allows for non-invasive soil moisture measurements at the field scale and relies on the inverse correlation between aboveground measured epithermal neutron intensity (1 eV−100 keV) and environmental water content. The measurement uncertainty follows Poisson statistics and thus increases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty, the neutron count rate is usually aggregated over 12 or 24 h time windows for stationary CRN probes. To obtain accurate soil moisture estimates with mobile CRN rover applications, the aggregation of neutron measurements is also necessary and should consider soil wetness and driving speed. To date, the optimization of spatial aggregation of mobile CRN observations in order to balance measurement accuracy and spatial resolution of soil moisture patterns has not been investigated in detail. In this work, we present and apply an easy-to-use method based on Gaussian error propagation theory for uncertainty quantification of soil moisture measurements obtained with CRN sensing. We used a 3rd order Taylor expansion for estimating the soil moisture uncertainty from uncertainty in neutron counts and compared the results to a Monte Carlo approach with excellent agreement. Furthermore, we applied our method with selected aggregation times to investigate how CRN rover survey design affects soil moisture estimation uncertainty. We anticipate that the new approach can be used to improve the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements.

IntroductionMaternal and perinatal mortality remain a challenge in resource-limited countries, particularly among the rural poor. To save lives at birth health facility delivery is recommended. However, increasing coverage of institutional deliveries may not translate into mortality reduction if shortage of qualified staff and lack of enabling working conditions affect quality of services. In Tanzania childbirth care is available in all facilities; yet maternal and newborn mortality are high. The study aimed to assess in a high facility density rural context whether a health system organization with fewer delivery sites is feasible in terms of population access.MethodsData on health facilities’ location, staffing and delivery caseload were examined in Ludewa and Iringa Districts, Southern Tanzania. Geospatial raster and network analysis were performed to estimate access to obstetric services in walking time. The present geographical accessibility was compared to a theoretical scenario with a 40% reduction of delivery sites.ResultsAbout half of first-line health facilities had insufficient staff to offer full-time obstetric services (45.7% in Iringa and 78.8% in Ludewa District). Yearly delivery caseload at first-line health facilities was low, with less than 100 deliveries in 48/70 and 43/52 facilities in Iringa and Ludewa District respectively. Wide geographical overlaps of facility catchment areas were observed. In Iringa 54% of the population was within 1-hour walking distance from the nearest facility and 87.8% within 2 hours, in Ludewa, the percentages were 39.9% and 82.3%. With a 40% reduction of delivery sites, approximately 80% of population will still be within 2 hours’ walking time.ConclusionsOur findings from spatial modelling in a high facility density context indicate that reducing delivery sites by 40% will decrease population access within 2 hours by 7%. Focused efforts on fewer delivery sites might assist strengthening delivery services in resource-limited settings.

Affordable and accurate weather monitoring systems are essential in low-income and developing countries and, more recently, are needed in small-scale research such as precision agriculture and urban climate studies. A variety of low-cost solutions are available on the market, but the use of non-standard technologies raises concerns for data quality. Research-grade all-in-one weather stations could present a reliable, cost effective solution while being robust and easy to use. This study evaluates the performance of the commercially available ATMOS41 all-in-one weather station. Three stations were deployed next to a high-performance reference station over a three-month period. The ATMOS41 stations showed good performance compared to the reference, and close agreement among the three stations for most standard weather variables. However, measured atmospheric pressure showed uncertainties >0.6 hPa and solar radiation was underestimated by 3%, which could be corrected with a locally obtained linear regression function. Furthermore, precipitation measurements showed considerable variability, with observed differences of ±7.5% compared to the reference gauge, which suggests relatively high susceptibility to wind-induced errors. Overall, the station is well suited for private user applications such as farming, while the use in research should consider the limitations of the station, especially regarding precise precipitation measurements.

With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multispectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi-frequency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.