scholarly journals STUDIES AND APPLICATION OF REMOTE SENSING RETRIEVAL METHOD OF SOIL MOISTURE CONTENT IN LAND PARCEL UNITS IN IRRIGATION AREA

2018 ◽  
Vol XLII-3 ◽  
pp. 2583-2589 ◽  
Author(s):  
H. Zhu ◽  
H. L. Zhao ◽  
Y. Z. Jiang ◽  
W. B. Zang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Spatial Scale ◽  
Soil Moisture Content ◽  
Agricultural Water ◽  
Retrieval Method ◽  
Irrigation Area ◽  
Sensing Data ◽  
Unit Scale

Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remote sensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remote sensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remote sensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remote sensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remote sensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data caused by the research units of pixels, and doesn’t involve compromises in the spatial scale and simulating precision like the grid simulation. When the application needs are met, the production efficiency of products can also be improved at a certain degree.

scholarly journals Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 877
Author(s):  
Jian Liu ◽  
Youshuan Xu ◽  
Henghui Li ◽  
Jiao Guo
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Remote Sensing Data ◽  
Optimal Combination ◽  
Optical Data ◽  
Support Vector ◽  
Sensing Data

As an important component of the earth ecosystem, soil moisture monitoring is of great significance in the fields of crop growth monitoring, crop yield estimation, variable irrigation, and other related applications. In order to mitigate or eliminate the impacts of sparse vegetation covers in farmland areas, this study combines multi-source remote sensing data from Sentinel-1 radar and Sentinel-2 optical satellites to quantitatively retrieve soil moisture content. Firstly, a traditional Oh model was applied to estimate soil moisture content after removing vegetation influence by a water cloud model. Secondly, support vector regression (SVR) and generalized regression neural network (GRNN) models were used to establish the relationships between various remote sensing features and real soil moisture. Finally, a regression convolutional neural network (CNNR) model is constructed to extract deep-level features of remote sensing data to increase soil moisture retrieval accuracy. In addition, polarimetric decomposition features for real Sentinel-1 PolSAR data are also included in the construction of inversion models. Based on the established soil moisture retrieval models, this study analyzes the influence of each input feature on the inversion accuracy in detail. The experimental results show that the optimal combination of R2 and root mean square error (RMSE) for SVR is 0.7619 and 0.0257 cm3/cm3, respectively. The optimal combination of R2 and RMSE for GRNN is 0.7098 and 0.0264 cm3/cm3, respectively. Especially, the CNNR model with optimal feature combination can generate inversion results with the highest accuracy, whose R2 and RMSE reach up to 0.8947 and 0.0208 cm3/cm3, respectively. Compared to other methods, the proposed algorithm improves the accuracy of soil moisture retrieval from synthetic aperture radar (SAR) and optical data. Furthermore, after adding polarization decomposition features, the R2 of CNNR is raised by 0.1524 and the RMSE of CNNR decreased by 0.0019 cm3/cm3 on average, which means that the addition of polarimetric decomposition features effectively improves the accuracy of soil moisture retrieval results.

Regional scale soil moisture content estimation based on multi-source remote sensing parameters

2019 ◽  
Vol 41 (9) ◽  
pp. 3346-3367 ◽  
Author(s):  
Mireguli Ainiwaer ◽  
Jianli Ding ◽  
Nijat Kasim ◽  
Jingzhe Wang ◽  
Jinjie Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Regional Scale

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>

Sign in / Sign up

Export Citation Format

Share Document