scholarly journals A METHOD OF MICROWAVE SOIL MOISTURE INVERSION WITHOUT DEPENDENCE ON THE FIELD MEASUREMENT DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 459-465
Author(s):  
L. Han ◽  
L. Chen ◽  
Y. Zhang ◽  
X. Qin
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Measurement Data ◽  
Measured Data ◽  
Vegetation Coverage ◽  
Inversion Method ◽  
Sampling Point ◽  
Effective Roughness ◽  
Empirical Function ◽  
Soil Moisture Inversion

In the process of retrieving soil moisture (<i>M<sub>v</sub></i>) by active microwave, surface roughness is an important parameter affecting the accuracy of <i>m<sub>v</sub></i> retrieval.Using effective roughness to replace the original measurement value of surface roughness can effectively avoid the error. In the existing methods of soil water inversion, the value of fixed mean square root height (<i>S</i>) was used to retrieve the surface of different types and vegetation coverage, neglecting the difference of different ground surface. This paper proposed a LUT (look up table) soil moisture inversion method based on the pixel scale effective roughness. First, the effective roughness was obtained by using the measured soil moisture value of the sampling point based on the LUT method. And then the empirical function between the optimum roughness (<i>S</i>, Correlation length-<i>l</i>) and the backscattering coefficient of VV/HH polarization was obtained. The value of each pixel's <i>S</i> and <i>L</i> was obtained by using the empirical function. Finally, the soil moisture was retrieved by the LUT method. Using the measured data of the Linze sample area to verify, the results showed that the proposed method was superior to the LUT inversion method using the <i>S</i> fixed value without dependence on the measured data of the roughness. It was also proved that the inversion method proposed in this paper is not only applicable to the bare soil area, but still maintained a high precision in the soil moisture inversion results in the area with large vegetation coverage.

GNSS-IR Soil Moisture Inversion Method Based on Random Forest

2021 ◽  
pp. 133-144
Author(s):  
Yuhua Zhang ◽  
Lili Jing ◽  
Yanmin Zhao ◽  
Hongliang Ruan ◽  
Lei Yang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Random Forest ◽  
Inversion Method ◽  
Soil Moisture Inversion

Research on soil moisture inversion method based on GA-BP neural network model

2018 ◽  
Vol 40 (5-6) ◽  
pp. 2087-2103 ◽  
Author(s):  
Yue-ji Liang ◽  
Chao Ren ◽  
Hao-yu Wang ◽  
Yi-bang Huang ◽  
Zhong-tian Zheng
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Inversion Method ◽  
Bp Neural Network Model ◽  
Soil Moisture Inversion

scholarly journals Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 135
Author(s):  
Min Zhang ◽  
Fengkai Lang ◽  
Nanshan Zheng
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Satellite Data ◽  
Cloud Model ◽  
Growth Cycle ◽  
Wheat Growth ◽  
Vegetation Fraction ◽  
Effective Roughness ◽  
Water Cloud ◽  
Optical Satellite Data

The objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was removed from the total backscatter by using the modified water cloud model (MWCM), which takes the vegetation fraction (fveg) into account. The VV/VH polarization radar backscattering coefficients database was established by a numerical simulation based on the advanced integrated equation model (AIEM) and the cross-polarized ratio of the Oh model. Then the empirical relationship between the bare soil backscattering coefficient and both the soil moisture and the surface roughness was developed by regression analysis. The surface roughness in this paper was described by using the effective roughness parameter and the combined roughness form. The experimental results revealed that using effective roughness as the model input instead of in-situ measured roughness can obtain soil moisture with high accuracy and effectively avoid the uncertainty of roughness measurement. The accuracy of soil moisture inversion could be improved by introducing vegetation fraction on the basis of the water cloud model (WCM). There was a good correlation between the estimated soil moisture and the observed values, with a root mean square error (RMSE) of about 4.14% and the coefficient of determination (R2) about 0.7390.

scholarly journals An inversion method based on multi-angular approaches for estimating bare soil surface parameters from RADARSAT-1

2009 ◽  
Vol 6 (1) ◽  
pp. 207-241 ◽  
Author(s):  
M. R. Sahebi ◽  
J. Angles
Keyword(s):  
Surface Roughness ◽  
Dielectric Constant ◽  
Soil Moisture ◽  
Soil Surface ◽  
Bare Soil ◽  
Earth Observation ◽  
Radar Signal ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Soil Surface Roughness

Abstract. The radar signal recorded by earth observation (EO) satellites is known to be sensitive to soil moisture and soil surface roughness, which influence the onset of runoff. This paper focuses on the inversion of these parameters using a multi-angular approach based on RADARSAT-1 data with incidence angles of 35° and 47° (in mode S3 and S7). This inversion was done based on three backscatter models: Geometrical Optics Model (GOM), Oh Model (OM) and Modified Dubois Model (MDM), which are compared in order to obtain the best configuration. For roughness expressed in rms of heights, mean absolute errors of 1.23 cm, 1.12 cm and 2.08 cm, and for dielectric constant, mean absolute errors of 2.46, 4.95 and 3.31 were obtained for the MDM, GOM and the OM simulation, respectively. This means that the MDM provided the best results with minimum errors. Based on these results, the latter inversion algorithm was applied on the images and the final results are presented in two different maps showing pixel and homogeneous zones for surface roughness and soil moisture.

scholarly journals An inversion method based on multi-angular approaches for estimating bare soil surface parameters from RADARSAT-1

2010 ◽  
Vol 14 (11) ◽  
pp. 2355-2366 ◽  
Author(s):  
M. R. Sahebi ◽  
J. Angles
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Soil Surface ◽  
Bare Soil ◽  
Earth Observation ◽  
Radar Signal ◽  
Inversion Method ◽  
Accurate Data ◽  
Inversion Algorithm ◽  
Volumetric Soil Moisture

Abstract. The radar signal recorded by earth observation (EO) satellites is sensitive to soil moisture and surface roughness, which both influence the onset of runoff. This paper focuses on inversion of these parameters using a multi-angular approach based on RADARSAT-1 data with incidence angles of 35° and 47° (in mode S3 and S7). This inversion was performed with three backscatter models: Geometrical Optics Model (GOM), Oh Model (OM), and Modified Dubois Model (MDM), which were compared to obtain the best configuration. Mean absolute errors of 1.23, 1.12, and 2.08 cm for roughness expressed in rms height and for dielectric constant, mean absolute errors of 2.46 – equal to 3.88 (m3 m−3) in volumetric soil moisture, – 4.95 – equal to 8.72 (m3 m−3) in volumetric soil moisture – and 3.31 – equal to 6.03 (m3 m−3) in volumetric soil moisture – were obtained for the MDM, GOM, and OM simulation, respectively. These results indicate that the MDM provided the most accurate data with minimum errors. Therefore, the latter inversion algorithm was applied to images, and the final results are presented in two different maps showing pixel and homogeneous zones for surface roughness and soil moisture.

scholarly journals Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1292
Author(s):  
Hongchun Zhu ◽  
Zhilin Zhang ◽  
Aifeng Lv
Keyword(s):  
Soil Moisture ◽  
Correlation Coefficient ◽  
Microwave Radiation ◽  
Satellite Data ◽  
Measurement Data ◽  
Ground Truth ◽  
Error Variance ◽  
Vegetation Coverage ◽  
Qinghai Province ◽  
Ground Measurement

Evaluating the reliability of satellite-based and reanalysis soil moisture products is very important in soil moisture research. The traditional methods of evaluating soil moisture products rely on the verification of satellite inversion data and ground observation; however, the ground measurement data is often difficult to obtain. The triple collocation (TC) method can be used to evaluate the accuracy of a product without obtaining the ground measurement data. This study focused on the whole of Qinghai Province, China (31°–40° N, 89°–103° E), and used the TC method to obtain the error variance for satellite-based soil moisture data, the signal-to-noise ratio (SNR) of the same data, and the correlation between the same data and the ground-truth soil moisture, using passive satellite products: Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS), Fengyun-3B Microwave Radiation Imager (FY3B), Fengyun-3C Microwave Radiation Imager (FY3C), and Advanced Microwave Scanning Radiometer 2 (AMSR2); an active satellite product Advanced Scatterometer (ASCAT), and reanalysis data Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system. The TC results for the passive satellite data were then compared with the satellite-derived enhanced vegetation index (EVI) to explore the influence of vegetation coverage on the results. The following conclusions are drawn: (1) for the SMAP, SMOS, FY3B, FY3C, and AMSR2 satellite data, the spatial distributions of the TC-derived error variance, the SNR of the satellite-derived soil moisture, and the correlation coefficient between the satellite-derived and ground-truth soil moisture, were all relatively similar, which indirectly verified the reliability of the TC method; and (2) SMOS data have poor applicability for the estimation of soil moisture in Qinghai Province due to their insufficient detection capability in the Qaidam area, high error variance (median 0.0053), high SNR (median 0.43), and low correlation coefficient with ground-truth soil moisture (median 0.57).

scholarly journals A Cuboid Model for Assessing Surface Soil Moisture

2019 ◽  
Vol 11 (24) ◽  
pp. 3034 ◽  
Author(s):  
Xiufang Zhu ◽  
Yaozhong Pan ◽  
Junxia Wang ◽  
Ying Liu
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Coverage ◽  
Observation Data ◽  
Optical Remote Sensing ◽  
Moisture Index ◽  
Significance Level ◽  
Feature Parameters ◽  
Soil Moisture Inversion

This study proposes a cuboid model for soil moisture assessment. In the model, the three edges were the meteorological, soil, and vegetation feature parameters highly related to soil moisture, and the edge lengths represented the degree of influence of each feature parameter on soil moisture. Soil moisture is assessed by the cuboid diagonal, which is referred to as the cuboid soil moisture index (CSMI) in this paper. The model was applied and validated in the Huang-Huai-Hai Plain. The results showed that (1) the difference in land surface temperature between day and night (ΔLST), land surface water index (LSWI), and accumulated precipitation (AP) were most closely correlated with soil moisture observation data in our study area, and were therefore selected as soil, crop, and meteorological system parameters to participate in CSMI calculations, respectively. (2) CSMI-1, with a cuboid length coefficient of 2/1/2, was the best model. The correlation of soil moisture derived from CSMI-1 with observed values was 0.64, 0.60, and 0.52 at depths of 10 cm, 20 cm, and 50 cm, respectively. (3) CSMI-1 had good applicability to the evaluation of soil moisture under different vegetation coverage. When the normalized difference vegetation index (NDVI)was 0–0.7, CSMI-1 was highly correlated with soil moisture at a significance level of 0.01. (4) The three-dimensional (3D) CSMI model can be easily converted to a two-dimensional (2D) model to adapt to different surface conditions (as long as the weight coefficient of one parameter is set to 0). Irrigation information (if available) can be considered as artificial recharge precipitation added in the AP to improve the accuracy of soil moisture inversion. This study provides a reference for soil moisture inversion using optical remote sensing images by integrating soil, vegetation, and meteorological feature parameters.

A Semiempirical Modeling of Soil Moisture, Vegetation, and Surface Roughness Impact on CYGNSS Reflectometry Data

2020 ◽  
pp. 1-17
Author(s):  
Simon H. Yueh ◽  
Rashmi Shah ◽  
M. Julian Chaubell ◽  
Akiko Hayashi ◽  
Xiaolan Xu ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Soil Moisture

scholarly journals High-Precision Soil Moisture Mapping Based on Multi-Model Coupling and Background Knowledge, Over Vegetated Areas Using Chinese GF-3 and GF-1 Satellite Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2123 ◽  
Author(s):  
Leran Han ◽  
Chunmei Wang ◽  
Tao Yu ◽  
Xingfa Gu ◽  
Qiyue Liu
Keyword(s):  
Soil Moisture ◽  
High Precision ◽  
Satellite Data ◽  
Solution Method ◽  
Pearson Correlation ◽  
Optimal Solution ◽  
Background Knowledge ◽  
Optical Data ◽  
Inversion Method ◽  
Synthetic Aperture Radar Data

This paper proposes a combined approach comprising a set of methods for the high-precision mapping of soil moisture in a study area located in Jiangsu Province of China, based on the Chinese C-band synthetic aperture radar data of GF-3 and high spatial-resolution optical data of GF-1, in situ experimental datasets and background knowledge. The study was conducted in three stages: First, in the process of eliminating the effect of vegetation canopy, an empirical vegetation water content model and a water cloud model with localized parameters were developed to obtain the bare soil backscattering coefficient. Second, four commonly used models (advanced integral equation model (AIEM), look-up table (LUT) method, Oh model, and the Dubois model) were coupled to acquire nine soil moisture retrieval maps and algorithms. Finally, a simple and effective optimal solution method was proposed to select and combine the nine algorithms based on classification strategies devised using three types of background knowledge. A comprehensive evaluation was carried out on each soil moisture map in terms of the root-mean-square-error (RMSE), Pearson correlation coefficient (PCC), mean absolute error (MAE), and mean bias (bias). The results show that for the nine individual algorithms, the estimated model constructed using the AIEM (mv1) was significantly more accurate than those constructed using the other models (RMSE = 0.0321 cm³/cm³, MAE = 0.0260 cm³/cm³, and PCC = 0.9115), followed by the Oh model (m_v5) and LUT inversion method under HH polarization (mv2). Compared with the independent algorithms, the optimal solution methods have significant advantages; the soil moisture map obtained using the classification strategy based on the percentage content of clay was the most satisfactory (RMSE = 0.0271 cm³/cm³, MAE = 0.0225 cm³/cm³, and PCC = 0.9364). This combined method could not only effectively integrate the optical and radar satellite data but also couple a variety of commonly used inversion models, and at the same time, background knowledge was introduced into the optimal solution method. Thus, we provide a new method for the high-precision mapping of soil moisture in areas with a complex underlying surface.

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