scholarly journals Assessing the Impact of Horizontal Error Correlations in Background Fields on Soil Moisture Estimation

2003 ◽  
Vol 4 (6) ◽  
pp. 1229-1242 ◽  
Author(s):  
Rolf H. Reichle ◽  
Randal D. Koster
Keyword(s):  
Soil Moisture ◽  
Background Fields ◽  
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.

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 Towards Including Dynamic Vegetation Parameters in the EUMETSAT H SAF ASCAT Soil Moisture Products

2021 ◽  
Vol 13 (8) ◽  
pp. 1463
Author(s):  
Susan C. Steele-Dunne ◽  
Sebastian Hahn ◽  
Wolfgang Wagner ◽  
Mariette Vreugdenhil
Keyword(s):  
Soil Moisture ◽  
Interannual Variability ◽  
Incidence Angle ◽  
Pearson Correlation ◽  
The United States ◽  
Research Network ◽  
Window Width ◽  
High Frequency Variability ◽  
Vegetation Parameters ◽  
The Impact

The TU Wien Soil Moisture Retrieval (TUW SMR) approach is used to produce several operational soil moisture products from the Advanced Scatterometer (ASCAT) on the Metop series of satellites as part of the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The incidence angle dependence of backscatter is described by a second-order Taylor polynomial, the coefficients of which are used to normalize ASCAT observations to the reference incidence angle of 40∘ and for correcting vegetation effects. Recently, a kernel smoother was developed to estimate the coefficients dynamically, in order to account for interannual variability. In this study, we used the kernel smoother for estimating these coefficients, where we distinguished for the first time between their two uses, meaning that we used a short and fixed window width for the backscatter normalisation while we tested different window widths for optimizing the vegetation correction. In particular, we investigated the impact of using the dynamic vegetation parameters on soil moisture retrieval. We compared soil moisture retrievals based on the dynamic vegetation parameters to those estimated using the current operational approach by examining their agreement, in terms of the Pearson correlation coefficient, unbiased RMSE and bias with respect to in situ soil moisture. Data from the United States Climate Research Network were used to study the influence of climate class and land cover type on performance. The sensitivity to the kernel smoother half-width was also investigated. Results show that estimating the vegetation parameters with the kernel smoother can yield an improvement when there is interannual variability in vegetation due to a trend or a change in the amplitude or timing of the seasonal cycle. However, using the kernel smoother introduces high-frequency variability in the dynamic vegetation parameters, particularly for shorter kernel half-widths.

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

Soil moisture estimation over grass-covered areas using AIRSAR

1994 ◽  
Vol 15 (11) ◽  
pp. 2323-2333 ◽  
Author(s):  
D. S. LIN ◽  
E. F. WOOD ◽  
K. Beven ◽  
S. SAATCHI
Keyword(s):  
Soil Moisture ◽  
Moisture Estimation

scholarly journals Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

2007 ◽  
Vol 8 (1) ◽  
pp. 68-87 ◽  
Author(s):  
Margaret A. LeMone ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Mukul Tewari ◽  
Bart Geerts ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Latent Heat ◽  
Land Surface ◽  
Field Experiments ◽  
Time Of Day ◽  
Horizontal Distribution ◽  
Heat Fluxes ◽  
Surface Model ◽  
Green Vegetation ◽  
The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

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