Soil Moisture

Landcover-specific calibration of the optical trapezoid model (OPTRAM) for soil moisture monitoring in the Central Valley, California

The Optical TRApezoid Model (OPTRAM) has been extensively utilized to map high-resolution surface soil moisture (top 0–5 cm) using …

Neda Mohamadzadeh, Morteza Sadeghi, Noemi Vergopolan, Lan Liang, Uditha Bandara, Craig Altare, Marcellus M. Caldas

Landcover-specific calibration of the optical trapezoid model (OPTRAM) for soil moisture monitoring in the Central Valley, California

The Benefits of Using State-Of-The-Art Digital Soil Properties Maps to Improve the Modeling of Soil Moisture in Land Surface Models

We assessed how advanced soil properties maps can improve soil moisture modeling using a Land Surface Model. The results showed that using contemporary soil properties maps and pedotransfer functions to estimate soil properties improved soil moisture modeling performance, especially when soil properties data is available at different soil layers.

Chengcheng Xu, Laura Torres-Rojas, Noemi Vergopolan, Nathaniel W. Chaney

The Benefits of Using State-Of-The-Art Digital Soil Properties Maps to Improve the Modeling of Soil Moisture in Land Surface Models

A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the U.S. Midwest

This study provides a framework for direct integration of soil moisture observations collected from soil sensors and satellite imagery into process-based crop models for improving the representation of agricultral systems. The performance of this framework was evaluated across 19 site-years for crop yield, NDVI, soil moisture, tile flow drainage and nitrate leaching.

Marissa Kivi, Noemi Vergopolan, Hamze Dokoohaki

A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the U.S. Midwest

High-Resolution Soil Moisture Data Reveal Complex Multi-Scale Spatial Variability Across the United States

Soil moisture (SM) space and time variability critically influences freshwater resources, agriculture, ecosystem dynamics, climate and land-atmosphere interactions, and it can also trigger hazards such as droughts, floods, landslides, and aggravate wildfires. Here, we present the first continental assessment of how SM varies at the local scales using SMAP-HydroBlocks. This study maps the SM spatial variability, characterizes the landscape drivers, and quantifies how this variability persists across larger spatial scales. Results revealed striking SM spatial variability across the United States. However, this SM variability does not persist at coarser spatial scales resulting in extensive information loss. This information loss implicates inaccuracies when predicting non-linear SM-dependent hydrological, ecological, and biogeochemical processes using coarse-scale models and satellite estimates.

Noemi Vergopolan, Justin Sheffield, Nathaniel W. Chaney, Ming Pan, Hylke E. Beck, Craig R. Ferguson, Laura Torres-Rojas, Felix Eigenbrod, Wade Crow, Eric F. Wood

High-Resolution Soil Moisture Data Reveal Complex Multi-Scale Spatial Variability Across the United States

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

We introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. Its largest benefit of SMAP-HB is the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products.

Noemi Vergopolan, Nathaniel W. Chaney, Ming Pan, Justin Sheffield, Hylke E. Beck, Craig R. Ferguson, Laura Torres-Rojas, Sara Sadri, Eric F. Wood

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.

Noemi Vergopolan, Sitian Xiong, Lyndon Estes, Niko Wanders, Nathaniel W. Chaney, Eric F. Wood, Megan Konar, Kelly Caylor, Hylke E. Beck, Nicolas Gatti, Tom Evans, Justin Sheffield

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

Hyper-Resolution Hydrologic and Land Surface Modeling

HydroBlocks is a field-scale resolving land surface model for computationally efficient hydrologic applications over continental extents. Learn about model development & applications here.

Last updated on Sep 11, 2023

Hyper-Resolution Hydrologic and Land Surface Modeling

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.

Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, Eric F. Wood

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Field-scale Crop Yield Prediction

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.

Last updated on Sep 11, 2023