scholarly journals Estimation of Soil Moisture for Bare Soil Fields Using ALOS/PALSAR HH Polarization Data

2008 ◽  
Vol 17 (4) ◽  
pp. 171-177 ◽  
Author(s):  
Rei Sonobe ◽  
Hiroshi Tani ◽  
Xiufeng Wang ◽  
Masami Fukuda
Keyword(s):  
Soil Moisture ◽  
Bare Soil ◽  
Polarization Data ◽  
Alos Palsar

scholarly journals Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2102
Author(s):  
Mohamad Hamze ◽  
Nicolas Baghdadi ◽  
Marcel M. El Hajj ◽  
Mehrez Zribi ◽  
Hassan Bazzi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Bare Soil ◽  
Data Availability ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Alos Palsar ◽  
Times Series ◽  
L Band ◽  
Sar Data ◽  
Error Bias

Surface soil moisture (SSM) is a key variable for many environmental studies, including hydrology and agriculture. Synthetic aperture radar (SAR) data in the C-band are widely used nowadays to estimate SSM since the Sentinel-1 provides free-of-charge C-band SAR images at high spatial resolution with high revisit time, whereas the use of L-band is limited due to the low data availability. In this context, the main objective of this paper is to develop an operational approach for SSM estimation that mainly uses data in the C-band (Sentinel-1) with L-bands (ALOS/PALSAR) as additional data to improve SSM estimation accuracy. The approach is based on the use of the artificial neural networks (NNs) technique to firstly derive the soil roughness (Hrms) from the L-band (HH polarization) to then consider the L-band-derived Hrms and C-band SAR data (VV and VH polarizations) in the input vectors of NNs for SSM estimation. Thus, the Hrms estimated from the L-band at a given date is assumed to be constant for a given times series of C-band images. The NNs were trained and validated using synthetic and real databases. The results showed that the use of the L-band-derived Hrms in the input vector of NN in addition to C-band SAR data improved SSM estimation by decreasing the error (bias and RMSE), mainly for SSM values lower than 15 vol.% and regardless of Hrms values. Based on the synthetic database, the NNs that neglect the Hrms underestimate and overestimate the SSM (bias ranges between −8.0 and 4.0 vol.%) for Hrms values lower and higher than 1.5 cm, respectively. For Hrms <1.5 cm and most SSM values higher than 10 vol.%, the use of Hrms as an input in the NNs decreases the underestimation of the SSM (bias ranges from −4.5 to 0 vol.%) and provides a more accurate estimation of the SSM with a decrease in the RMSE by approximately 2 vol.%. Moreover, for Hrms values between 1.5 and 2.0 cm, the overestimation of SSM slightly decreases (bias decreased by around 1.0 vol.%) without a significant improvement of the RMSE. In addition, for Hrms >2.0 cm and SSM between 8 to 22 vol.%, the accuracy on the SSM estimation improved and the overestimation decreased by 2.2 vol.% (from 4.5 to 2.3 vol.%). From the real database, the use of Hrms estimated from the L-band brought a significant improvement of the SSM estimation accuracy. For in situ SSM less than 15 vol.%, the RMSE decreased by 1.5 and 2.2 vol.% and the bias by 1.2 and 2.6 vol.%, for Hrms values lower and higher than 1.5 cm, respectively.

scholarly journals Sensitivity Analysis of Bistatic Scattering for Soil Moisture Retrieval

2021 ◽  
Vol 13 (2) ◽  
pp. 188
Author(s):  
Tingting Li ◽  
Irena Hajnsek ◽  
Kun-Shan Chen
Keyword(s):  
Sensitivity Analysis ◽  
Soil Moisture ◽  
Quality Index ◽  
High Sensitivity ◽  
Bare Soil ◽  
Sensitivity Index ◽  
Scattering Coefficients ◽  
Observation Systems ◽  
Bistatic Scattering ◽  
Scattering Response

Soil moisture is one of the vital environmental variables in the land–atmosphere cycle. A study of the sensitivity analysis of bistatic scattering coefficients from bare soil at the Ku-band is presented, with the aim of deepening our understanding of the bistatic scattering features and exploring its potential in soil moisture retrieval. First, a well-established advanced integral method was adopted for simulating the bistatic scattering response of bare soil. Secondly, a sensitivity index and a normalized weight quality index were proposed to evaluate the effect of soil moisture on the bistatic scattering coefficient in terms of polarization and angular diversity, and the combinations thereof. The results of single-polarized VV data show that the regions with the maximum sensitivity and high quality index, simultaneously, to soil moisture are in the forward off-specular direction. However, due to the effect of surface roughness and surface autocorrelation function (ACF), the single-polarized data have some limitations for soil moisture inversion. By contrast, the results of two different polarization combinations, as well as a dual-angular simulation of one transmitter and two receivers, show significant estimation benefits. It can be seen that they all provide better ACF suppression capabilities, larger high-sensitivity area, and higher quality indices compared to single-polarized estimation. In addition, dual polarization or dual angular combined measurement provides the possibility of retrieving soil moisture in backward regions. These results are expected to contribute to the design of future bistatic observation systems.

scholarly journals Coupled stochastic dynamics of water table and soil moisture in bare soil conditions

2008 ◽  
Vol 44 (1) ◽  
Author(s):  
L. Ridolfi ◽  
P. D'Odorico ◽  
F. Laio ◽  
S. Tamea ◽  
I. Rodriguez-Iturbe
Keyword(s):  
Soil Moisture ◽  
Water Table ◽  
Stochastic Dynamics ◽  
Bare Soil ◽  
Soil Conditions

scholarly journals Evaluation of DSSAT soil-water balance module under cropped and bare soil conditions

2003 ◽  
Vol 46 (4) ◽  
pp. 489-498 ◽  
Author(s):  
Rogério Teixeira de Faria ◽  
Walter Truman Bowen
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Water Flux ◽  
Soil Water Potential ◽  
Bare Soil ◽  
Root Water Uptake ◽  
Soil Water Balance ◽  
Soil Conditions ◽  
Soil Water Flux

The performance of the soil water balance module (SWBM) in the models of DSSAT v3.5 was evaluated against soil moisture data measured in bare soil and dry bean plots, in Paraná, southern Brazil. Under bare soil, the SWBM showed a low performance to simulate soil moisture profiles due to inadequacies of the method used to calculate unsaturated soil water flux. Improved estimates were achieved by modifying the SWBM with the use of Darcy's equation to simulate soil water flux as a function of soil water potential gradient between consecutive soil layers. When used to simulate water balance for the bean crop, the modified SWBM improved soil moisture estimation but underpredicted crop yield. Root water uptake data indicated that assumptions on the original method limited plant water extraction for the soil in the study area. This was corrected by replacing empirical coefficients with measured values of soil hydraulic conductivity at different depths.

scholarly journals A Simulation Study of GNSS-R Polarimetric Scattering from the Bare Soil Surface Based on the AIEM

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xuerui Wu ◽  
Shuanggen Jin
Keyword(s):  
Soil Moisture ◽  
Circular Polarization ◽  
Soil Surface ◽  
Bare Soil ◽  
Satellite System ◽  
Equation Model ◽  
The Other ◽  
Scattering Coefficients ◽  
Remote Sensing Technique ◽  
Transmitted Signal

In the past two decades, global navigation satellite system-reflectometry (GNSS-R) has emerged as a new remote sensing technique for soil moisture monitoring. Some experiments showed that the antenna of V polarization is more favorable to receive the reflected signals, and the interference pattern technique (IPT) was used for soil moisture and retrieval of other geophysical parameters. Meanwhile, the lower satellite elevation angles are most impacted by the multipath. However, electromagnetic theoretical properties are not clear for GNSS-R soil moisture retrieval. In this paper, the advanced integral equation model (AIEM) is employed using the wave-synthesis technique to simulate different polarimetric scatterings in the specular directions. Results show when the incident angles are larger than 70°, scattering at RR polarization (the transmitted signal is right-hand circular polarization (RHCP), while the received one is also RHCP) is larger than that at LR polarization (the transmitted signal is RHCP, while the received one is left-hand circular polarization (LHCP)), while scattering at LR polarization is larger than that at RR polarization for the other incident angles (1°∼70°). There is an apparent dip for VV and VR scatterings due to the Brewster angle, which will result in the notch in the final receiving power, and this phenomenon can be used for soil moisture retrieval or vegetation corrections. The volumetric soil moisture (vms) effects on their scattering are also presented. The larger soil moisture will result in lower scattering at RR polarization, and this is very different from the scattering of the other polarizations. It is interesting to note that the surface correlation function only affects the amplitudes of the scattering coefficients at much less level, but it has no effects on the angular trends of RR and LR polarizations.

scholarly journals A Novel Approach to Interpret Soil Moisture Content for Economical Monitoring of Urban Landscape

2019 ◽  
Vol 11 (20) ◽  
pp. 5609
Author(s):  
Junwei Liu ◽  
Vinay Kumar Gadi ◽  
Ankit Garg ◽  
Suriya Prakash Ganesan ◽  
Anasua GuhaRay
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Temperature ◽  
Green Infrastructure ◽  
Urban Landscape ◽  
Statistical Modelling ◽  
Bare Soil ◽  
Field Monitoring ◽  
Skewed Distribution ◽  
Normal Distribution Function

Preservation of green infrastructure (GI) needs continuous monitoring of soil moisture. Moisture content in soil is generally interpreted on the basis electrical conductivity (EC), soil temperature and relative humidity (RH). However, validity of previous approaches to interpret moisture content in urban landscape was rarely investigated. There is a need to relate the moisture content with other parameters (EC, temperature and RH) to economize the sensor installation. This study aims to quantify the dynamics of the above-mentioned parameters in an urban green space, and to further develop correlations between moisture content and other parameters (EC, temperature and RH). An integrated field monitoring and statistical modelling approach were adopted to achieve the objective. Four distinct sites comprising treed (younger and mature tree), grassed and bare soil were selected for investigation. Field monitoring was conducted for two months to measure four parameters. This was followed by statistical modelling by artificial neural networks (ANN). Correlations were developed for estimating soil moisture as a function of other parameters for the selected sites. Irrespective of the type of site, EC was found to be the most significant parameter affecting soil moisture, followed by RH and soil temperature. This correlation with EC is found to be stronger in vegetated soil as compared to that without vegetation. The correlations of soil temperature with water content do not have a conclusive trend. A considerable increase in temperature was not found due to the subsequent drying of soil after rainfall. A normal distribution function was found from the uncertainty analysis of soil moisture in the case of treed soil, whereas soil moisture was observed to follow a skewed distribution in the bare and grassed soils.

Soil Hydraulic Parameters and Surface Soil Moisture of a Tilled Bare Soil Plot Inversely Derived from L-Band Brightness Temperatures

2014 ◽  
Vol 13 (1) ◽  
pp. vzj2013.04.0075 ◽  
Author(s):  
M. Dimitrov ◽  
J. Vanderborght ◽  
K. G. Kostov ◽  
K. Z. Jadoon ◽  
L. Weihermüller ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Bare Soil ◽  
Hydraulic Parameters ◽  
Surface Soil Moisture ◽  
Brightness Temperatures ◽  
Soil Hydraulic Parameters ◽  
L Band ◽  
Soil Hydraulic

scholarly journals Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

2020 ◽  
Vol 10 (16) ◽  
pp. 5540 ◽  
Author(s):  
Maria Casamitjana ◽  
Maria C. Torres-Madroñero ◽  
Jaime Bernal-Riobo ◽  
Diego Varga
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Surface Soil ◽  
Vegetation Indices ◽  
Bare Soil ◽  
Multispectral Images ◽  
Surface Soil Moisture ◽  
Mountain Environments ◽  
Tropical Mountain ◽  
Moisture Analysis

Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be effective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI; the normalized difference vegetation index, NDVI; the normalized difference water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ field measurements. The results showed that on bare soil, the indices that best fit the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates significantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.

The effects of biochar on the physical properties of bare soil

2012 ◽  
Vol 103 (1) ◽  
pp. 5-11 ◽  
Author(s):  
Francesca Ventura ◽  
Fiorenzo Salvatorelli ◽  
Stefano Piana ◽  
Linda Pieri ◽  
Paola Rossi Pisa
Keyword(s):  
Soil Moisture ◽  
Bulk Density ◽  
Soil Water ◽  
Agricultural Soil ◽  
Water Retention ◽  
Bare Soil ◽  
Soil Water Retention ◽  
Experimental Scheme ◽  
Dark Colour ◽  
Key Factor

ABSTRACTThe pyrolysis conversion of vegetable residues into energy and biochar, and its incorporation in agricultural soil, reduces CO2emission and provides a longterm soil carbon sequestration. Moreover, biochar application in soil seems to increase nutrient stocks in the rooting layer, improving crop yield. Compared with the numerous studies assessing the positive effect of biochar on yield, however, little research has been published elucidating the mechanisms responsible for the reported benefits. Few studies cited soil moisture as the key factor, attributing the increased yield to the higher soil water availability.The aim of this study was to investigate the effect of biochar on the physical and hydraulic properties of a bare Padana Plain (Cadriano, Bologna) agricultural soil. A preliminary plot experiment in 2009 explored the influence of 10 and 30 kg ha–1of biochar on soil moisture, without effects from plants. Results of the first experiment suggested using higher biochar rates in a similar experimental scheme. During the second experiment, 30 and 60 t ha–1doses were investigated. Soil water content, bulk density, electrical conductivity and soil water retention were measured. The comparison between treated soils and the control indicates that the biochar rate is directly correlated to electrical conductibility and inversely correlated with bulk density. The effect on the density of soil can be very positive in case of heavy soils. The dark colour of the char increased the surface temperature with respect to the control, while no differences were detected at 7·5 cm depth. No influences were found on other soil characteristics, including soil pH, moisture and water retention.

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