Soil moisture content inversion research using multi-source remote sensing data

2014 ◽  
Author(s):  
Chengcai Zhang ◽  
Zule Zhu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Remote Sensing Data ◽  
Sensing Data

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.

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 Land-Cover Dependent Relationships between Fire and Soil Moisture

Fire ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 55 ◽  
Author(s):  
Alexander J. Schaefer ◽  
Brian I. Magi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Fire Activity ◽  
Steep Decline ◽  
Human Use ◽  
Moisture Conditions ◽  
In Fire

For this study, we characterized the dependence of fire counts (FCs) on soil moisture (SM) at global and sub-global scales using 15 years of remote sensing data. We argue that this mathematical relationship serves as an effective way to predict fire because it is a proxy for the semi-quantitative fire–productivity relationship that describes the tradeoff between fuel availability and climate as constraints on fire activity. We partitioned the globe into land-use and land-cover (LULC) categories of forest, grass, cropland, and pasture to investigate how the fire–soil moisture (fire–SM) behavior varies as a function of LULC. We also partitioned the globe into four broadly defined biomes (Boreal, Grassland-Savanna, Temperate, and Tropical) to study the dependence of fire–SM behavior on LULC across those biomes. The forest and grass LULC fire–SM curves are qualitatively similar to the fire–productivity relationship with a peak in fire activity at intermediate SM, a steep decline in fire activity at low SM (productivity constraint), and gradual decline as SM increases (climate constraint), but our analysis highlights how forests and grasses differ across biomes as well. Pasture and cropland LULC are a distinctly human use of the landscape, and fires detected on those LULC types include intentional fires. Cropland fire–SM curves are similar to those for grass LULC, but pasture fires are evident at higher SM values than other LULC. This suggests a departure from the expected climate constraint when burning is happening at non-optimal flammability conditions. Using over a decade of remote sensing data, our results show that quantifying fires relative to a single physical climate variable (soil moisture) is possible on both cultivated and uncultivated landscapes. Linking fire to observable soil moisture conditions for different land-cover types has important applications in fire management and fire modeling.

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
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