In-situ soil moisture sensing: Measurement scheduling and estimation using compressive sensing

2012 ◽  
Author(s):  
Xiaopei Wu ◽  
Mingyan Liu
Keyword(s):  
Soil Moisture ◽  
Compressive Sensing ◽  
Measurement Scheduling ◽  
Moisture Sensing

scholarly journals Measurement Scheduling for Soil Moisture Sensing: From Physical Models to Optimal Control

2010 ◽  
Vol 98 (11) ◽  
pp. 1918-1933 ◽  
Author(s):  
David I Shuman ◽  
Ashutosh Nayyar ◽  
Aditya Mahajan ◽  
Yuriy Goykhman ◽  
Ke Li ◽  
...  
Keyword(s):  
Optimal Control ◽  
Soil Moisture ◽  
Physical Models ◽  
Measurement Scheduling ◽  
Moisture Sensing

In-situ Soil Moisture Sensing

2015 ◽  
Vol 11 (2) ◽  
pp. 1-29 ◽  
Author(s):  
Xiaopei Wu ◽  
Qingsi Wang ◽  
Mingyan Liu
Keyword(s):  
Soil Moisture ◽  
Moisture Sensing

In-Situ and Remote Soil Moisture Sensing Technologies for Vadose Zone Hydrology

2013 ◽  
Vol 12 (2) ◽  
pp. vzj2013.03.0058 ◽  
Author(s):  
Ali Fares ◽  
Marouane Temimi ◽  
Kelly Morgan ◽  
Thijs J. Kelleners
Keyword(s):  
Soil Moisture ◽  
Vadose Zone ◽  
Vadose Zone Hydrology ◽  
Moisture Sensing

In-situ soil moisture sensing

2012 ◽  
Vol 8 (4) ◽  
pp. 1-30 ◽  
Author(s):  
Xiaopei Wu ◽  
Mingyan Liu ◽  
Yue Wu
Keyword(s):  
Soil Moisture ◽  
Moisture Sensing

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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.

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