Using multimodal remote sensing data to estimate regional-scale soil moisture content: A case study of Beijing, China

2022 ◽  
Vol 260 ◽  
pp. 107298
Author(s):  
Minghan Cheng ◽  
Binbin Li ◽  
Xiyun Jiao ◽  
Xiao Huang ◽  
Haiyan Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Beijing China

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

Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

2018 ◽  
Vol 65 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Rida Khellouk ◽  
Ahmed Barakat ◽  
Abdelghani Boudhar ◽  
Rachid Hadria ◽  
Hayat Lionboui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Arid Area ◽  
Optical Remote Sensing ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Semi Arid

scholarly journals A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

2020 ◽  
Vol 12 (3) ◽  
pp. 455 ◽  
Author(s):  
Yaokui Cui ◽  
Xi Chen ◽  
Wentao Xiong ◽  
Lian He ◽  
Feng Lv ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
The Tibetan Plateau ◽  
Sensing Data ◽  
Spatio Temporal ◽  
Input Variables

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

scholarly journals Urban Residential Land Suitability Analysis Combining Remote Sensing and Social Sensing Data: A Case Study in Beijing, China

2019 ◽  
Vol 11 (8) ◽  
pp. 2255 ◽  
Author(s):  
Huiping Huang ◽  
Qiangzi Li ◽  
Yuan Zhang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Suitability ◽  
Residential Land ◽  
Social Sensing ◽  
Sensing Data ◽  
Beijing China

With the degradation of the environment and the acceleration of urbanization, urban residential land has been undergoing rapid changes and has attracted great attention worldwide. Meanwhile, the quantitative evaluation of the suitability of urban residential land is essential for a better and more powerful understanding of urban residential land planning and improvement. Most urban land suitability studies rely solely on remote sensing data and GIS data to evaluate natural suitability, and few studies have focused on urban land suitability from a socioeconomic perspective. Consequently, this paper integrates remote sensing data (GaoFen-2 satellite image) and social sensing data (Tencent User Density data, Point-of-interest data and OpenStreetMap data) to establish an evaluation framework for analyzing the suitability of urban residential land in the Haidian District, Beijing, China, in which, ecological comfortability, locational livability and overall suitability were evaluated according to five attributes extracted from urban residential land via the factor analysis method. The evaluation results of this case study show that, compared with the suburban area in the northwest, the urban area tends to have lower ecological comfortability and higher locational livability. The overall suitability increases from southeast to northwest, consistent with the spatial distribution of ecological comfortability. This framework can potentially assist with the sustainable development of residential lands and urban land use planning.

Linking thermal UAS-based imagery and Cosmic-Ray Neutron Sensing data

2020 ◽  
Author(s):  
Veronika Döpper ◽  
Tobias Gränzig ◽  
Michael Förster ◽  
Birgit Kleinschmit
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Hydrological Modeling ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Cosmic Ray ◽  
Added Value ◽  
Innovative Technology ◽  
Sensing Data ◽  
Local Point

<p>Soil moisture content (SMC) is of fundamental importance to many hydrological, biological, biochemical and atmospheric processes. Common soil moisture measurements range from local point measurements to global remote sensing-based SMC datasets. Nevertheless, they always compromise between temporal and spatial resolution. Thus, it is still challenging to quantify spatially and temporally distributed SMC at a regional scale which is extremely relevant for hydrological modeling or agricultural management. The innovative technology Cosmic-Ray Neutron Sensing (CRNS) shows significant potential to fill this gap by quantifying the present hydrogen pools within footprints larger than 0.1 ha.</p><p>Owing to the difference in scale between the ground resolution of satellites used to retrieve soil moisture and the common point scale of ground-based soil moisture instruments, the large footprint of the CRNS poses a high potential for the validation of SMC remote sensing products. When linking the CRNS measurements with remote sensing data, the vertical and horizontal characteristics of its footprint need to be considered.</p><p>To examine the influence of the CRNS footprint characteristics on the linkage of CRNS and remote sensing data, we couple CRNS measurements with high-resolution UAS-based thermal imagery acquired at two sites in Bavaria and Brandenburg (Germany) using a radiometrically calibrated FLIR Tau 2 336 (FLIR Systems, Inc., Wilsonville, OR, USA) with a focal length of 9 mm. Within this context, we evaluate the added value of applying a horizontal weighting function to the spatially distributed thermal data in comparison to an unweighted mean when statistically representing the corrected neutron counting rates.</p><p>The project is part of the DFG-funded research group Cosmic Sense, which aims to provide interdisciplinary new representative insights into hydrological changes at the land surface.</p>

Monitoring and Mapping of Soil and Snow Water Across Scales with Cosmic-Ray Neutron Sensor Networks and Mobile Platforms

2020 ◽  
Author(s):  
Martin Schrön ◽  
Sascha E Oswald ◽  
Steffen Zacharias ◽  
Peter Dietrich ◽  
Sabine Attinger
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Cosmic Ray ◽  
Ongoing Research ◽  
Sensing Data ◽  
Research Activities ◽  
Snow Water

<p>Cosmic-ray neutron albedo sensing (CRNS) is a modern technology that can be used to non-invasively measure the average water content in the environment (i.e., in soil, snow, or vegetation). The sensor footprint encompasses an area of 10-15 hectares and extends tens of decimeters deep into the soil. This method might have the potential to bridge the scale gap between conventional in-situ sensors and remote-sensing data in both, the horizontal and the vertical domain.</p><p>Currently, more than 200 sensors are operated in the growing networks of national and continental observatories. While single CRNS stations are continuously monitoring the local water dynamics at fixed field sites, mobile CRNS platforms are used for on-demand soil moisture mapping at the regional scale. The sensors are rapidly operational on any ground- or airborne vehicle. The data is particularly useful to study hydrological extreme events, heatwaves, and snow melt/accumulation, and it is being applied in hydrological models and agricultural irrigation management.</p><p>In the presentation we will explore the potential of the CRNS method to support and complement in-situ and remote-sensing data for hydrological event monitoring. We will discuss ongoing research activities that are aimed at improving the operationality, frequency, and spatial extend of CRNS measurements. New measurement strategies that are currently explored are, for example: dense clusters of 20 CRNS stations fully covering a 100 hectare catchment; heat wave monitoring with mobile car-based CRNS; regular soil/snow water mapping using mobile CRNS on cars and trains; and airborne surveys using CRNS on gyrocopters.</p><p>Future CRNS observations could provide a valuable contribution to the multi-sensor approach, e.g. to help tracking and characterizing surface water movement, to map regional-scale soil moisture patterns, or to calibrate and evaluate satellite data.</p>

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