Derivation of a global soil moisture and vegetation database from passive microwave signals

2003 ◽  
Author(s):  
Richard A. M. De Jeu ◽  
Manfred Owe
Keyword(s):  
Soil Moisture ◽  
Passive Microwave ◽  
Global Soil ◽  
Vegetation Database ◽  
Microwave Signals

scholarly journals SOIL MOISTURE RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS: APPLICATION TO PASSIVE MICROWAVE REMOTE SENSING

2018 ◽  
Vol XLII-3 ◽  
pp. 583-586 ◽  
Author(s):  
Z. Hu ◽  
L. Xu ◽  
B. Yu
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Soil Moisture ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Massive Data ◽  
Passive Microwave Remote Sensing ◽  
Global Soil

A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN). Soil moisture plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF) model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global soil moisture retrieval. It is very well supported by graphics processing unit (GPU) acceleration, which can meet the demand of massive data inversion. Once the model trained, a global soil moisture map can be predicted in less than 10 seconds. What's more, the method of soil moisture retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global soil moisture can achieve a better performance than the support vector regression (SVR) for soil moisture retrieval.

Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations

2016 ◽  
Vol 180 ◽  
pp. 453-464 ◽  
Author(s):  
A. Al-Yaari ◽  
J.P. Wigneron ◽  
Y. Kerr ◽  
R. de Jeu ◽  
N. Rodriguez-Fernandez ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Passive Microwave ◽  
Regression Equations ◽  
Global Soil

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

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

2020 ◽  
Vol 12 (17) ◽  
pp. 2861
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Vital Role ◽  
Product System ◽  
Global Soil ◽  
One Stop ◽  
Satellite Sensors ◽  
Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

scholarly journals Evaluation of post-retrieval de-noising of active and passive microwave satellite soil moisture

2015 ◽  
Vol 163 ◽  
pp. 127-139 ◽  
Author(s):  
Chun-Hsu Su ◽  
Sugata Y. Narsey ◽  
Alexander Gruber ◽  
Angelika Xaver ◽  
Daniel Chung ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Passive Microwave

scholarly journals Potential of bias correction for downscaling passive microwave and soil moisture data

2015 ◽  
Vol 120 (13) ◽  
pp. 6460-6479 ◽  
Author(s):  
Kurt C. Kornelsen ◽  
Michael H. Cosh ◽  
Paulin Coulibaly
Keyword(s):  
Soil Moisture ◽  
Bias Correction ◽  
Passive Microwave ◽  
Soil Moisture Data

scholarly journals Development of a hydrometeorological forcing data set for global soil moisture estimation

2005 ◽  
Vol 25 (13) ◽  
pp. 1697-1714 ◽  
Author(s):  
A. A Berg ◽  
J. S. Famiglietti ◽  
M. Rodell ◽  
R. H. Reichle ◽  
U. Jambor ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Set ◽  
Global Soil ◽  
Moisture Estimation

The Computation of the Effective Temperature of the Land Surface Covered with Canopy

2014 ◽  
Vol 607 ◽  
pp. 830-834
Author(s):  
Hong Zhang Ma ◽  
Su Mei Liu
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Effective Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Passive Microwave ◽  
Vegetation Canopy ◽  
Surface Soil Moisture ◽  
Remote Sensors ◽  
Energy Exchanges

—Surface soil moisture is an important parameter in describing the water and energy exchanges at the land surface/atmosphere interface. Passive microwave remote sensors have great potential for monitoring surface soil moisture over land surface. The objective of this study is going to establish a model for estimating the effective temperature of land surface covered with vegetation canopy and to investigate how to compute the microwave radiative brightness temperature of land surface covered with vegetation canopy in considering of the canopy scatter effect.

Potential of Mapping Global Soil from SMAP Soil Moisture Product: A Pilot Study

2021 ◽  
pp. 1-1
Author(s):  
Long Zhao ◽  
Kun Yang ◽  
Jie He ◽  
Hui Zheng ◽  
Donghai Zheng
Keyword(s):  
Soil Moisture ◽  
Pilot Study ◽  
Global Soil
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