HydroBlocks

SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US

Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems in data-sparse developing countries, and it shows how field-scale soil moisture can leverage and improve crop yield prediction and drought impact assessment.

SMAP-HydroBlocks

The first hyper-resolution satellite-based surface soil moisture dataset at 30-m resolution over the continental United States

Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems in data-sparse developing countries, and it shows how field-scale soil moisture can leverage and improve crop yield prediction and drought impact assessment.

HydroBlocks

HydroBlocks is a field-scale resolving land surface model for computationally efficient applications over continental extents. Learn more on the model development & applications [here](../../HydroBlocks).

HydroBlocks A field-scale resolving land surface model over continental extents HydroBlocks is a hyper-resolution field-scale resolving land surface model that accounts for the water, energy, and carbon balance to solve land surface processes at high spatial and temporal resolutions.

Two-way coupling between the sub-grid land surface and river networks in Earth system models

Although there have been significant advances in river routing and sub-grid heterogeneity (i.e., tiling) schemes in Earth system models over the past decade, there has yet to be a concerted effort to couple these two concepts. This paper aims to bridge this gap through the development of a two-way coupling between sub-grid tiles and river networks in a field-scale resolving land surface model. The scheme is implemented and tested over a 1 arc degree domain in Oklahoma, United States.

Field-scale Crop Yield Prediction

Field-scale satellite observations, physical models, and machine learning combined can enable crop yield prediction at high spatial resolution at data-scarse regions. Learn more about it [here](research/crop_yields_zambia).

Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates

Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Recent satellite missions measuring soil …

Cognitive Biases about Climate Variability in Smallholder Farming Systems in Zambia

Given the varying manifestations of climate change over time and the influence of climate perceptions on adaptation, it is important to understand whether farmer perceptions match patterns of environmental change from observational data. We use a …