Quantifying the benefits of incorporating vegetation heterogeneity in farm-scale soil moisture simulations

More than 80% of the world’s farms are small, with a farm size of less than 2 hectares. Under such highly fragmented agricultural systems, smallholder farmers require accurate, field-scale predictions of soil moisture and irrigation requirements that can capture local variability. Vegetation heterogeneity plays a crucial role in shaping soil moisture variability across various spatial scales. At the field scale, differences in crop types, cropping patterns, and management practices can substantially alter soil-water dynamics.

Traditional Land Surface Models (LSMs) simulate land surface processes with spatial, temporal, and physical consistency. However, their typically coarse spatial resolution (>10 km) fails to resolve the sub-grid heterogeneity relevant for small and marginal farms. While hyper-resolution (<100 m resolution) LSMs have the potential to simulate land surface processes at the field scale, many of their processes, including vegetation dynamics, are heavily parameterized. Previous studies have highlighted the sensitivity of soil moisture to Leaf Area Index (LAI), but the reliance of LSMs on lookup-table LAI parameterization introduces substantial errors in simulating crop phenology, yield, and growing-season length, especially in regions like India with a prevailing fragmented agricultural system. These errors arise from the assumption of uniform parameter values across different climatic regions and crop types, disregarding the scale effects of vegetation and soil heterogeneity. Addressing these limitations requires region-specific parameterization or using satellite-based LAI values in simulating soil moisture to enhance the accuracy of soil moisture predictions and improve the representation of vegetation dynamics in LSMs.

The present study evaluates the benefits of using satellite LAI data in generating a field-scale soil moisture simulation. Towards this, we set up HydroBlocks, a hyper-resolution LSM over Upper Bhima Basin, in India, to simulate 3-hourly 30 m resolution surface (0-5 cm)and root zone (0-30 cm) soil moisture. The Upper Bhima Basin is a sub-basin of the Krishna River, lying predominantly on the leeward side of the Western Ghats. The study area has the majority of its land under croplands and receives relatively low rainfall, making it an ideal location for studying soil moisture variability under varying vegetation conditions. In this study, we made four HydroBlocks simulation experiments with LAI data from different sources: 1) the default lookup table values, 2) monthly climatological  LAI data derived from MODIS for each land use land cover class, 3) assimilating MODIS  LAI data using the direct insertion technique, and 4) using the dynamic vegetation module. We compared the spatial and temporal dynamics of soil moisture simulation from different experiments. Further, we statistically evaluated both surface and root-zone soil moisture simulations against in situ observations.