Remote Sensing

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](https://waterai.earth/publication/2021_vergopolan_smaphydroblocks). 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.

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.

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.

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.

PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018

We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) observation-based climatologies (1979–2018) of the occurrence and peak intensity of precipitation (P) at daily and 3-hourly …

A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP

Soil Moisture (SM) is a direct measure of agricultural drought. While there are several global SM indices, none of them directly use SM observations in a near-real-time capacity and as an operational tool. This paper presents a near-real-time global …

Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

This study represents the most comprehensive global-scale precipitation dataset evaluation to date. We evaluated 13 uncorrected precipitation datasets using precipitation observations from 76 086 gauges, and 9 gauge-corrected ones using hydrological modeling for 9053 catchments. Our results highlight large differences in estimation accuracy, and hence, the importance of precipitation dataset selection in both research and operational applications.

The impact of deforestation on the hydrological cycle in Amazonia as observed from remote sensing

Given widespread Amazonian deforestation, numerous studies have focused on how the regional hydrological cycle - in terms of precipitation (P) recycling from evapotranspiration (ET) - is impacted by deforestation. Nevertheless, climate macroscale and …