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 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.

scholarly journals ADVANCES IN SOIL MOISTURE RETRIEVAL FROM NEAR-SURFACE MEASUREMENTS USING SATELLITE REMOTE SENSING

2018 ◽  
Vol XLII-5 ◽  
pp. 861-869
Author(s):  
R. Prajapati ◽  
D. Chakraborty ◽  
V. Kumar
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Spatial Resolution ◽  
Satellite Remote Sensing ◽  
Soil Moisture Content ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Generate Surface ◽  
Fine Resolution

<p><strong>Abstract.</strong> Soil moisture influences numerous environmental processes occurring over large spatial and temporal scales. It profoundly influences the hydrological and meteorological activity together with climate predictions and hazard analysis. Space-borne sensors are capable of retrieving the surface soil moisture over a region on a regular basis. Latent heat measurements of soil, reflectance based methods, microwave measurements and synergistic approaches are some of the techniques used since long for providing soil moisture estimates over regional and global scales. Due to the dynamic interaction of soil with crops, retrieval of surface soil moisture is always challenging. This paper gives a brief overview of advance in soil moisture retrieval techniques, and an attempt to generate surface soil moisture from fine-resolution satellite remote sensing data. The optical remote sensing explores the linear relationship between land surface reflectance and soil moisture content, and through development of empirical spectral vegetation indices. Another way to estimate soil moisture emerged by measuring amplitude of diurnal temperature, which is closely related to thermal conductivity and heat capacity of soil. Emergence of radiometric satellite measurements at fine resolution has reached at a higher level of technology these days. Microwave remote sensing techniques have a long legacy of providing surface soil moisture estimates with reasonable accuracy. The SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Passive and Active) missions launched in 2009 and 2015 respectively, are completely dedicated for providing soil moisture at global scale with a spatial resolution of 35<span class="thinspace"></span>km &amp; 3&amp;ndash;40<span class="thinspace"></span>km. These soil moisture products, however, provides data at highly coarser spatial resolution. The launch of Sentinels gave insight by providing active radar and optical data at higher resolution (&amp;sim;10<span class="thinspace"></span>m). Sentinel-1 is the first SAR (Synthetic Aperture Radar) constellation having 6-day revisit time providing data in C-band with dual polarisations. However, no algorithm or methodology is available to generate surface soil moisture product at a finer resolution from dual polarisations. Sentinel-1 data has been used to generate regional surface soil moisture image through modelling. The same has been also used for generating surface soil moisture map of IARI farm at New Delhi. Dubois, a bare surface model, was tested for its suitability for surface soil moisture retrieval of the farm. In addition, radar- based Soil moisture (SM) proxy method was used over Sentinel-1 data for the month of July 2018, and validated through actual surface soil moisture (gravimetric) measurements. Results were satisfactory for a range of 4&amp;ndash;16<span class="thinspace"></span>m<sup>3</sup><span class="thinspace"></span>m<sup>&amp;minus;3</sup> of soil moisture, with coefficient of determination (R<sup>2</sup>) as 0.45, RMSE of 2.35 and a p-value of 0.005. However, over a higher range of soil moisture (21&amp;ndash;33<span class="thinspace"></span>m<sup>3</sup><span class="thinspace"></span>m<sup>&amp;minus;3</sup>), which occurred after the rainfall, the R<sup>2</sup> value reduced to 0.22 with larger RMSE. Results suggested that SM-proxy approach might work well for a limited range (drier part) of soil moisture content, and not for the wet soil.</p>

scholarly journals Machinery-Induced Damage to Soil and Remaining Forest Stands—Case Study from Slovakia

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1289
Author(s):  
Zuzana Dudáková (Allmanová) ◽  
Michal Allman ◽  
Ján Merganič ◽  
Katarína Merganičová
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Bulk Density ◽  
Soil Moisture Content ◽  
Soil Bulk Density ◽  
Forest Stands ◽  
Forest Operations ◽  
Factorial Anova ◽  
Size Type ◽  
The Impact

The paper deals with the damage of the remaining stand and soil caused by harvesting using three ground-based forest operations methods (harvester-forwarder/cable skidder/animal-tractor). It compares the impact of the most common harvesting technologies applied in Slovakia and in Central Europe and thus contributes with valuable information to the knowledge on the suitability of their application in forests stands dominated by broadleaved tree species. Harvesting was performed in five forest stands located at the University Forest Enterprise of Technical University in Zvolen in central Slovakia from August to October 2019. Damage to remaining trees was assessed from the point of its size, type, and position of damage along stem. We expected lower damage of remaining trees in stands where harvesters were used because of the applied cut-to-length short wood system and fully mechanized harvesting system. In addition, we examined soil bulk density and soil moisture content in ruts, space between ruts, and in undisturbed stand to reveal the impact of harvesting machinery on soil. We expected greater soil bulk densities and lower soil moisture content in these stands due to the greatest weight of harvesters and in ruts created by machinery compared with undisturbed stand soil. The highest percentage of damaged remaining trees equal to 20.47% and 23.36% was recorded for harvester forest operations, followed by skidder (19.44%) and animal forest operations with 19.86% and 14.47%. Factorial ANOVA confirmed significant higher soil compaction in stands where harvesters were used (higer bulk density) than in stands where skidding was performed with the skidder and animal power. Higher soil moisture content was recorded in ruts created by harvesters and the skidder. The lowest soil moisture content was in undisturbed stands irrespective of the applied forest operation method.

scholarly journals Sensitivity in the Overland Reintensification of Tropical Cyclone Erin (2007) to Near-Surface Soil Moisture Characteristics

2011 ◽  
Vol 139 (12) ◽  
pp. 3848-3870 ◽  
Author(s):  
Clark Evans ◽  
Russ S. Schumacher ◽  
Thomas J. Galarneau
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Tropical Cyclone ◽  
Moisture Content ◽  
Tropical Cyclones ◽  
Soil Moisture Content ◽  
Soil Conditions ◽  
Seasonal Signal ◽  
Along Track ◽  
The Impact

Abstract This study investigates the impact of abnormally moist soil conditions across the southern Great Plains upon the overland reintensification of North Atlantic Tropical Cyclone Erin (2007). This is tested by analyzing the contributions of three soil moisture–related signals—a seasonal signal, an along-track rainfall signal, and an early postlandfall rainfall signal—to the intensity of the vortex. In so doing, a suite of nine convection-permitting numerical simulations using the Advanced Research Weather Research and Forecasting model (WRF-ARW) is used. Of the signals tested, soil moisture contributions from the anomalously wet months preceding Erin are found to have the greatest positive impact upon the intensity of the vortex, though this impact is on the order of that from climatological soil moisture conditions. The greatest impact of the early rainfall signal contributions is found when it is added to the seasonal signal. Along-track rainfall during the simulation period has a minimal impact. Variations in soil moisture content result in impacts upon the boundary layer thermodynamic environment via boundary layer mixing. Greater soil moisture content results in weaker mixing, a shallower boundary layer, and greater moisture and instability. Differences in the intensity of convection that develops and its accompanying latent heat release aloft result in greater warm-core development and surface vortex intensification within the simulations featuring greater soil moisture content. Implications of these findings to the tropical cyclone development process are discussed. Given that the reintensification is shown to occur in, apart from land, an otherwise favorable environment for tropical cyclone development and results in a vortex with a structure similar to developing tropical cyclones, these findings provide new insight into the conditions under which tropical cyclones develop.

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

Soil moisture content retrieval based on apparent thermal inertia for Xinjiang province in China

2011 ◽  
Vol 33 (12) ◽  
pp. 3870-3885 ◽  
Author(s):  
Frank Veroustraete ◽  
Qin Li ◽  
Willem W. Verstraeten ◽  
Xi Chen ◽  
Anming Bao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Thermal Inertia ◽  
Content Retrieval

scholarly journals A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine

2021 ◽  
Vol 13 (11) ◽  
pp. 2099
Author(s):  
Felix Greifeneder ◽  
Claudia Notarnicola ◽  
Wolfgang Wagner
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Moisture Content ◽  
Spatial Resolution ◽  
Soil Moisture Content ◽  
Surface Soil ◽  
High Spatial Resolution ◽  
Google Earth ◽  
Surface Soil Moisture ◽  
Google Earth Engine

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 professional users. Remote-sensing offers the unique possibility for continuous measurements of this variable. Especially for agriculture, there is a strong demand for high spatial resolution mapping. However, operationally available soil moisture products exist with medium to coarse spatial resolution only (≥1 km). This study introduces a machine learning (ML)—based approach for the high spatial resolution (50 m) mapping of soil moisture based on the integration of Landsat-8 optical and thermal images, Copernicus Sentinel-1 C-Band SAR images, and modelled data, executable in the Google Earth Engine. The novelty of this approach lies in applying an entirely data-driven ML concept for global estimation of the surface soil moisture content. Globally distributed in situ data from the International Soil Moisture Network acted as an input for model training. Based on the independent validation dataset, the resulting overall estimation accuracy, in terms of Root-Mean-Squared-Error and R², was 0.04 m3·m−3 and 0.81, respectively. Beyond the retrieval model itself, this article introduces a framework for collecting training data and a stand-alone Python package for soil moisture mapping. The Google Earth Engine Python API facilitates the execution of data collection and retrieval which is entirely cloud-based. For soil moisture retrieval, it eliminates the requirement to download or preprocess any input datasets.

scholarly journals Shading decreases and delays NDVI and flowering of prostrate arctic shrubs

2021 ◽  
Author(s):  
Jeremy May ◽  
Steve Oberbauer ◽  
Steven L. Unger ◽  
Matthew J. Simon ◽  
Katlyn R. Betway ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Surface Temperature ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Moisture Content ◽  
Trophic Levels ◽  
Food Sources ◽  
The Arctic ◽  
The Impact

Increases in shrub growth and canopy cover are well documented community responses to climate warming in the Arctic. An important consequence of larger deciduous shrubs is shading of prostrate plant species, many of which are important sources of nectar and berries. Here we present the impact of a shading experiment on two prostrate shrubs, Vaccinium vitis-idaea and Arctous alpina, in northern Alaska over two growing seasons. We implemented three levels of shading (no shade, 40% shade, and 80% shade) in dry heath and moist acidic tundra. Plots were monitored for soil moisture content, surface temperature, normalized difference vegetation index (NDVI), and flowering. Shading was shown to, on average, lower surface temperature (0.7 to 5.3 ˚C) and increase soil moisture content (0.5 to 5.6%) in both communities. Both species- and plot-level NDVI values were delayed in timing of peak values (7 to 13 days) and decreased at the highest shading. Flower abundance of both species was lower in shaded plots and peak flowering was delayed (3 to 8 days) compared to controls. Changes in timing may result in phenological mismatches and can impact other trophic levels in the Arctic as both the flowers and resulting berries are important food sources for animals.

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