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

HE Beck, M Pan, DG Miralles… - Hydrology and Earth …, 2021 - hess.copernicus.org
Abstract Information about the spatiotemporal variability of soil moisture is critical for many
purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction …

Multi-layer high-resolution soil moisture estimation using machine learning over the United States

L Karthikeyan, AK Mishra - Remote Sensing of Environment, 2021 - Elsevier
The lack of proper understanding of multi-layer soil moisture (SM) profile (signals) remains a
persistent challenge in sustainable agricultural water management and food security …

Version 4 of the SMAP level‐4 soil moisture algorithm and data product

RH Reichle, Q Liu, RD Koster, WT Crow… - Journal of Advances …, 2019 - Wiley Online Library
Abstract The NASA Soil Moisture Active Passive (SMAP) mission Level‐4 Soil Moisture (L.
4_SM) product provides global, 3‐hourly, 9‐km resolution estimates of surface (0–5 cm) and …

Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products

H Kim, R Parinussa, AG Konings, W Wagner… - Remote Sensing of …, 2018 - Elsevier
Global-scale surface soil moisture (SSM) products retrieved from active and passive
microwave remote sensing provide an effective method for monitoring near-real-time SSM …

Global soil moisture data derived through machine learning trained with in-situ measurements

R Orth - Scientific Data, 2021 - nature.com
While soil moisture information is essential for a wide range of hydrologic and climate
applications, spatially-continuous soil moisture data is only available from satellite …

A machine learning-based approach for surface soil moisture estimations with google earth engine

F Greifeneder, C Notarnicola, W Wagner - Remote Sensing, 2021 - mdpi.com
Due to its relation to the Earth's climate and weather and phenomena like drought, flooding,
or landslides, knowledge of the soil moisture content is valuable to many scientific and …

Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the …

Q Yuan, H Xu, T Li, H Shen, L Zhang - Journal of Hydrology, 2020 - Elsevier
This study attempted to develop a point-surface collaborative inversion (PSCI) method for
the estimation of regional surface soil moisture (SSM) using a generalized regression neural …

Global evaluation of SMAP/Sentinel-1 soil moisture products

F Mohseni, SM Mirmazloumi, M Mokhtarzade… - Remote Sensing, 2022 - mdpi.com
SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product
derived from synergistic utilization of the radiometry observations of SMAP and radar …

Global scale map** of subsurface scattering signals impacting ASCAT soil moisture retrievals

W Wagner, R Lindorfer, S Hahn, H Kim… - … on Geoscience and …, 2024 - ieeexplore.ieee.org
Soil moisture retrievals from the Advanced Scatterometer (ASCAT) have so far relied on the
assumption that soil backscatter increases monotonically with soil moisture content …

Intercomparison of electromagnetic scattering models for delay-Doppler maps along a CYGNSS land track with topography

JD Campbell, R Akbar, A Bringer… - … on Geoscience and …, 2022 - ieeexplore.ieee.org
A comparison of three different electromagnetic scattering models for land surface delay-
Doppler maps (DDMs) obtained from global navigation satellite system reflectometry (GNSS …