The Impact of Model Based Despeckling on Soil Moisture Estimation

2009 ◽  
Author(s):  
Dusan Gleich ◽  
Peter Planinsic ◽  
Matej Kseneman ◽  
Zarko Cucej
Keyword(s):  
Soil Moisture ◽  
Model Based ◽  
The Impact ◽  
Moisture Estimation

Estimation régionale de l’humidité du sol par télédétection

1996 ◽  
Vol 76 (3) ◽  
pp. 325-334 ◽  
Author(s):  
J. B. Boisvert ◽  
Y. Crevier ◽  
T. J. Pultz
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Incidence Angle ◽  
Thermal Inertia ◽  
Estimation Accuracy ◽  
Large Area ◽  
Dry Period ◽  
The Impact ◽  
Moisture Estimation ◽  
Canadian Research

Several pilot projects have demonstrated that estimation of soil moisture over a large area can be done using remote sensing. Three main methods have been tested with some success: thermal inertia, passive microwave and synthetic aperture radar (SAR). The advantages and limitations of each approach were summarized. Most Canadian research has focused on SAR data. It has shown that several parameters can affect the accuracy of soil moisture estimation using radar such as incidence angle, roughness, polarization and frequency. The data collected during the SIR-C/X-SAR experiment in Altona, Manitoba, were used to evaluate the impact of incidence angle on soil moisture estimation accuracy. Incidence angle was the most significant factor to explain the signal variations over time. The effect of incidence angle (38° to 58°) on the signal was linear in October. Correlation between soil moisture and the signal was higher with surface (0–2.5cm) measurements in the wet period (April) but there was no significant correlation during the dry period (October). A statistical model using soil moisture and incidence angle in April showed that an increase of 1° in incidence angle could decreased the C-HH signal by 0.25 dB and the L-HH signal by 0.30 dB. Such variation would generate a change of 2% (C-HH) and 5% (L-HH) in soil moisture estimation. Key words: Radar, remote sensing, soil moisture, microwave

scholarly journals Evaluation of the Land GNSS-Reflected DDM Coherence on Soil Moisture Estimation from CYGNSS Data

2021 ◽  
Vol 13 (4) ◽  
pp. 570
Author(s):  
Zhounan Dong ◽  
Shuanggen Jin
Keyword(s):  
Soil Moisture ◽  
Global Navigation Satellite System ◽  
Land Surface ◽  
Satellite System ◽  
Navigation Satellite ◽  
Global Navigation ◽  
Incoherent Component ◽  
Global Navigation Satellite ◽  
The Impact ◽  
Moisture Estimation

With the development of spaceborne global navigation satellite system-reflectometry (GNSS-R), it can be used for terrestrial applications as a promising remote sensing tool, such as soil moisture (SM) retrieval. The reflected L-band GNSS signal from the land surface can simultaneously generate coherent and incoherent scattering, depending on surface roughness. However, the contribution of the incoherent component was directly ignored in previous GNSS-R land soil moisture content retrieval due to the hypothesis of its relatively small proportion. In this paper, a detection method is proposed to distinguish the coherence of land GNSS-R delay-Doppler map (DDM) from the cyclone global navigation satellite system (CYGNSS) mission in terms of DDM power-spreading features, which are characterized by different classification estimators. The results show that the trailing edge slope of normalized integrated time-delay waveform presents a better performance to recognize coherent and incoherent dominated observations, indicating that 89.6% of CYGNSS land observations are dominated by the coherent component. Furthermore, the impact of the land GNSS-Reflected DDM coherence on soil moisture retrieval is evaluated from 19-month CYGNSS data. The experiment results show that the influence of incoherent component and incoherent observations is marginal for CYGNSS soil moisture retrieval, and the RMSE of GNSS-R derived soil moisture reaches 0.04 cm3/cm3.

Soil Moisture Estimation over Soybean Fields Through A Model-Based Polarimetric Decomposition

2020 ◽  
Author(s):  
Tengfei Xiao ◽  
Minfeng Xing ◽  
Binbin He
Keyword(s):  
Soil Moisture ◽  
Vegetation Cover ◽  
Cloud Model ◽  
Estimation Method ◽  
Backscatter Signal ◽  
Volume Scattering ◽  
Model Based ◽  
Sar Data ◽  
Mean Square Errors ◽  
Moisture Estimation

<p>As one of the most important parameters in earth surface, soil moisture plays a crucial role in in many fields, such as agriculture, environment, hydrology, ecology and water management. With the development of earth observation technology, Synthetic Aperture Radar (SAR) provides a powerful method to estimate soil moisture at diverse spatial and temporal scales. However, in agricultural area, soil moisture estimated by SAR often obstructed by vegetation cover. Volume scattering and vegetation attenuation can complex the received SAR backscatter signal when microwave interacts with vegetation canopy. In this study, a model-based polarimetric decomposition and the two-way attenuation parameter in Water Cloud Model (WCM) were adopted to remove the effect of volume scattering and vegetation attenuation respectively. And a deorientation process of SAR data was applied to remove the influence of randomly distributed target angles before polarimetric decomposition. After that, the Dubois model was used to describe the underlying soil backscattering and retrieve soil moisture. Optimal surface roughness was adopted to parameterize the Dubois model due to the difficulty of soil roughness measurement under vegetation cover. This soil moisture estimation method was applied to soybean fields with time-series RADARSAT-2 SAR data. Validation based on in-situ measured soil moisture demonstrates that the proposed method is capable of estimating soil moisture over soybean fields, with Root Mean Square Errors (RMSEs) of 9.2 vol.% and 8.2 vol.% at HH and VV polarization respectively.</p>

scholarly journals Improving Estimation of Soil Moisture Content Using a Modified Soil Thermal Inertia Model

2020 ◽  
Vol 12 (11) ◽  
pp. 1719
Author(s):  
Zhenhua Liu ◽  
Li Zhao ◽  
Yiping Peng ◽  
Guangxing Wang ◽  
Yueming Hu
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Data Fusion ◽  
Spatial Resolution ◽  
Soil Moisture Content ◽  
Thermal Inertia ◽  
Original Model ◽  
Estimation Accuracy ◽  
Model Based ◽  
The Impact

There has been substantial research for estimating and mapping soil moisture content (SMC) of large areas using remotely sensed images by developing models of soil thermal inertia (STI). However, it is still a great challenge to accurately estimate SMC because of the impact of vegetation canopies and vegetation-induced shadows in mixed pixels on the estimates. In this study, a new method was developed to increase the estimation accuracy of SMC for an irrigated area located in YingKe of Heihe, China, using ASTER data. In the method, an original model of estimating bare STI was modified by decomposing a mixed pixel into three components, bare soil, vegetated soil, and shaded soil, as well as extracting their fractions using a spectral unmixing analysis and then deriving their fluxes. Moreover, the 90 m spatial resolution thermal images were scaled down to the 15 m spatial resolution by data fusion of a discrete wavelet transform (DWT) and re-sampling using the nearest neighbor method (NNM). The modified model was compared with the original model based on the mean absolute error (MAE) and relative root mean square error (RRMSE) between the SMC estimates and observations from 30 validation soil samples. The results indicated that compared to the original model based on the parallel dual layer, the modified STI model based on the serial dual layer statistically significantly decreased the MAE and RRMSE of the SMC estimates by 63.0–63.2% and 63.0–63.5%, respectively. The 15 m spatial resolution thermal bands obtained by the DWT data fusion provided more detailed information of SMC but did not significantly improve its estimation accuracy than the 15 m spatial resolution thermal bands by re-sampling using NNM. This implied that the novel method offered insights on how to increase the accuracy of retrieving SMC estimates in vegetated areas.

scholarly journals Soil Moisture Dynamics in Response to Precipitation and Thinning in a Semi-Dry Forest in Northern Mexico

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 105
Author(s):  
Argelia E. Rascón-Ramos ◽  
Martín Martínez-Salvador ◽  
Gabriel Sosa-Pérez ◽  
Federico Villarreal-Guerrero ◽  
Alfredo Pinedo-Alvarez ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Forest Management ◽  
Water Availability ◽  
Dry Forest ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Northern Mexico ◽  
The Difference ◽  
The Impact ◽  
Moisture Dynamics

Understanding soil moisture behavior in semi-dry forests is essential for evaluating the impact of forest management on water availability. The objective of the study was to analyze soil moisture based in storm observations in three micro-catchments (0.19, 0.20, and 0.27 ha) with similar tree densities, and subject to different thinning intensities in a semi-dry forest in Chihuahua, Mexico. Vegetation, soil characteristics, precipitation, and volumetric water content were measured before thinning (2018), and after 0%, 40%, and 80% thinning for each micro-catchment (2019). Soil moisture was low and relatively similar among the three micro-catchments in 2018 (mean = 8.5%), and only large rainfall events (>30 mm) increased soil moisture significantly (29–52%). After thinning, soil moisture was higher and significantly different among the micro-catchments only during small rainfall events (<10 mm), while a difference was not noted during large events. The difference before–after during small rainfall events was not significant for the control (0% thinning); whereas 40% and 80% thinning increased soil moisture significantly by 40% and 53%, respectively. Knowledge of the response of soil moisture as a result of thinning and rainfall characteristics has important implications, especially for evaluating the impact of forest management on water availability.

scholarly journals Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning

Agronomy ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 35
Author(s):  
Xiaodong Huang ◽  
Beth Ziniti ◽  
Michael H. Cosh ◽  
Michele Reba ◽  
Jinfei Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Canopy Cover ◽  
The United States ◽  
Optical Data ◽  
Study Region ◽  
Model Based ◽  
Key Indicator ◽  
Ground Measurement ◽  
L Band

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60.

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