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 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.

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.

scholarly journals Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

2020 ◽  
Vol 12 (16) ◽  
pp. 2587
Author(s):  
Yan Nie ◽  
Ying Tan ◽  
Yuqin Deng ◽  
Jing Yu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Regions ◽  
Drought Index ◽  
Dynamic Monitoring ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Rapid Acquisition ◽  
Semi Arid ◽  
Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

Multi-Stage Inversion Method to Retrieve Soil Moisture from Passive Microwave Measurements over the Mackenzie River Basin

2013 ◽  
Vol 12 (3) ◽  
pp. vzj2012.0134 ◽  
Author(s):  
Naira Chaouch ◽  
Robert Leconte ◽  
Ramata Magagi ◽  
Marouane Temimi ◽  
Reza Khanbilvardi
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Passive Microwave ◽  
Inversion Method ◽  
Microwave Measurements ◽  
Multi Stage ◽  
Mackenzie River Basin ◽  
Mackenzie River

Investigating the Effects of Meteorological Data Rainfall and Temperature on GNSS-R Soil Moisture Inversion

2021 ◽  
Author(s):  
Yajie Shi ◽  
Yueji Liang ◽  
Chao Ren ◽  
Jianmin Lai ◽  
Qin Ding ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Meteorological Data ◽  
Soil Moisture Inversion

scholarly journals Synthetic Aperture Radar (SAR) Compact Polarimetry for Soil Moisture Retrieval

2019 ◽  
Vol 11 (19) ◽  
pp. 2227 ◽  
Author(s):  
Amine Merzouki ◽  
Heather McNairn ◽  
Jarrett Powers ◽  
Matthew Friesen
Keyword(s):  
Soil Moisture ◽  
Agricultural Sector ◽  
Temporal Frequency ◽  
Test Site ◽  
Equation Model ◽  
Inversion Method ◽  
Large Area ◽  
Eastern Canada ◽  
Medium Resolution ◽  
Satellite Sensors

Soil moisture is a factor for risk analysis in the agricultural sector, yet access to temporally and spatially detailed data is challenging for much of the world’s agricultural extend. Significant effort has been focused on developing methodologies to estimate soil moisture from microwave satellite sensors. Canada’s RADARSAT Constellation Mission (RCM) is capable of acquiring imagery in a number of modes with a Compact Polarimetry (CP) configuration at different spatial resolutions (1 to 100 m). RCM offers greater polarization diversity, wide swaths and improved temporal frequency (4-day exact revisit time); all important considerations for large area monitoring of agricultural resources. The major goal of this study was to examine whether CP could accurately estimate surface soil moisture over bare fields. A methodology was developed using the calibrated Integral Equation Model (IEM) multi-polarization inversion approach. RADARSAT-2 data was acquired between 2012 and 2017 over a test site in eastern Canada. CP backscatter for two RCM modes (medium resolution 30 m and 50 m (MR30 and MR50)) was simulated using 63 RADARSAT-2 fully polarimetric images. A simple transfer function was developed between RH (right circular-horizontal) and HH (horizontal-horizontal) intensity, as well as RV (right circular-vertical) and VV (vertical-vertical). These HH- and VV-like intensities were then used in the multi-polarization inversion scheme to retrieve soil moisture. CP soil moisture retrievals were validated against soil moisture measurements from a long term in-situ network instrumented with five soil moisture stations. Retrieved and measured soil moisture were well correlated (R > 0.70) with an unbiased root mean square error (ubRMSE) less than 0.06 m3/m3. Overall, the developed method clearly captured the dry down and wetting trends observed through the five years study period. However, results demonstrated that the inversion method introduced a consistent bias (~0.10 m3/m3). Comparison of CP soil moisture estimates to those from the Soil Moisture Active Passive (SMAP) passive microwave satellite confirmed this bias. This study demonstrates the potential of C-band CP data to deliver accurate soil moisture products over wide swaths for regional and national soil moisture monitoring.

Sign in / Sign up

Export Citation Format

Share Document