22 research outputs found
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Long-term impact of cover crop and reduced disturbance tillage on soil pore size distribution and soil water storage
We studied the long-term impact of contrasting tillage and cover cropping systems on soil structure and hydraulic properties. Complete water retention and conductivity curves for the top (0-5 cm) and subsurface (20-25 cm) soils were characterized and contrasted. Dynamic water storage and retention were evaluated using numerical simulations in HYDRUS-2D software. Compared with standard-till (ST) and no-cover-crop (NO) systems, soils under no-till (NT) and cover cropping (CC) systems showed improved soil structure in terms of pore size distribution (PSD). Changes in hydraulic conductivity (K) under these systems led to an increased infiltration rate and water retention. However, NT and CC plots had lower water content at field capacity (33 kPa suction) and lower plant-available water (PAW) compared with ST and NO plots. Numerical simulations, however, showed that NT and CC plots have higher water storage (albeit marginal in magnitude) and water availability following irrigation. Because the numerical simulations considered retention and conductivity functions simultaneously and dynamically through time, they allow the capture of hydraulic states that are arguably more relevant to crops. The study concludes that the long-term practices of NT and CC systems were beneficial in terms of changes to the PSD. NT and CC systems also marginally improved soil water conductivity and storage at the plot scale
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Machine Learning Based Soil Moisture Retrieval from Unmanned Aircraft System Multispectral Remote Sensing
We developed machine learning models to retrieve surface soil moisture (0-4 cm) from high resolution multispectral imagery, terrain attributes, and local climate covariates. Using a small unmanned aircraft system (UAS) equipped with a multispectral sensor we captured high resolution imagery in part to create a high-resolution digital elevation model (DEM) as well as quantify relative vegetation photosynthetic status. We tested four different machine learning algorithms. The boosted regression tree algorithm provided the best accuracy model with mean absolute error of 3.8 % volumetric water content. The most important variables for the prediction of soil moisture were precipitation, reflectance in the red wavelengths, potential evapotranspiration, and topographic position indices (TPI). Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing data and machine learning. Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture information is important