Calculation of land subsidence and changes in soil moisture and salinity using remote sensing techniques

2021 ◽  
Vol 80 (12) ◽  
Author(s):  
Mohammad Roohi ◽  
Mehdi Faeli ◽  
Maryam Irani ◽  
Ehsan Shamsaei
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Subsidence ◽  
Remote Sensing Techniques

scholarly journals Soil moisture remote-sensing applications for identification of flood-prone areas along transport infrastructure

2018 ◽  
Vol 77 (14) ◽  
Author(s):  
Anna-Klara Ahlmer ◽  
Marco Cavalli ◽  
Klas Hansson ◽  
Alexander J. Koutsouris ◽  
Stefano Crema ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Urban Areas ◽  
Heavy Precipitation ◽  
Drainage Density ◽  
Transport Infrastructure ◽  
Flood Prediction ◽  
The Road ◽  
Sensing Applications ◽  
Remote Sensing Techniques

AbstractThe expected increase in precipitation and temperature in Scandinavia, and especially short-time heavy precipitation, will increase the frequency of flooding. Urban areas are the most vulnerable, and specifically, the road infrastructure. The accumulation of large volumes of water and sediments on road-stream intersections gets severe consequences for the road drainage structures. This study integrates the spatial and temporal soil moisture properties into the research about flood prediction methods by a case study of two areas in Sweden, Västra Götaland and Värmland, which was affected by severe flooding in August 2014. Soil moisture data are derived from remote-sensing techniques, with a focus on the soil moisture-specific satellites ASCAT and SMOS. Furthermore, several physical catchments descriptors (PCDs) are analyzed and the result shows that larger slopes and drainage density, in general, mean a higher risk of flooding. The precipitation is the same; however, it can be concluded that more precipitation in most cases gives higher soil moisture values. The lack, or the dimensioning, of road drainage structures seems to have a large impact on the flood risk as more sediment and water can be accumulated at the road-stream intersection. The results show that the method implementing soil moisture satellite data is promising for improving the reliability of flooding.

scholarly journals Single- and Multiscale Remote Sensing Techniques, Multifractals, and MODIS-Derived Vegetation and Soil Moisture

2008 ◽  
Vol 7 (2) ◽  
pp. 533-546 ◽  
Author(s):  
S. Lovejoy ◽  
A. M. Tarquis ◽  
H. Gaonac'h ◽  
D. Schertzer
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Remote Sensing Techniques

scholarly journals Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

2019 ◽  
Vol 11 (20) ◽  
pp. 2356 ◽  
Author(s):  
Angela Lausch ◽  
Jussi Baade ◽  
Lutz Bannehr ◽  
Erik Borg ◽  
Jan Bumberger ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Cost Effective ◽  
Global Scale ◽  
Soil Characteristics ◽  
Monitoring Methods ◽  
In Situ Techniques ◽  
The Face ◽  
Remote Sensing Techniques

In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.

scholarly journals Rapid Land Subsidence in Tianjin, China Derived from Continuous GPS Observations (2010–2019)

2020 ◽  
Vol 382 ◽  
pp. 241-247
Author(s):  
Ruibin Zhao ◽  
Guoquan Wang ◽  
Xiao Yu ◽  
Xiaohan Sun ◽  
Yan Bao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Subsidence ◽  
Ground Truth ◽  
Hebei Province ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Remote Sensing Techniques ◽  
Continuous Gps ◽  
Gps Observations ◽  
Regional Reference

Abstract. We have delineated ten years of urban subsidence derived from continuous GPS stations operated by the Crustal Movement Observational Network of China (CMONOC) within and adjacent to the municipality of Tianjin. A method for obtaining accurate site velocities with respect to a stable regional reference frame is described. CMONOC stations in Jizhou (JIXN) and Baodi (TJBD) districts recorded minor subsidence of approximately 1 to 2 mm yr−1 during the period from 2010 to 2019. One station in Wuqing (TJWQ) district and one station in Binhai (TJBH) district recorded steady subsidence of approximately 5 and 2 cm yr−1 from 2010 to 2019, respectively. One station in Cangzhou (HECX) of Hebei Province, adjacent to Tianjin, recorded steady subsidence of approximately 2.4 cm yr−1 during 2010–2014 and more rapid subsidence of 4 cm yr−1 since 2015. TJWQ recorded the most rapid land subsidence and the most significant seasonal ground oscillations (uplift and subsidence) among these five stations. This study indicates that subsidence rates in Tianjin vary significantly in space and time. Particular attention should be paid, therefore, to extrapolate or infer a rate of subsidence for an area on the basis of a subsidence rate obtained from previous GPS observations or proximal GPS sites. The subsidence time series presented in this study provide reliable “ground truth” and constraints for calibrating or validating subsidence estimations from numerical modeling and repeated surveys using other remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR).

Soil Moisture Estimation Using Active and Passive Remote Sensing Techniques

2016 ◽  
pp. 37-56
Author(s):  
Ming Pan ◽  
Xing Yuan ◽  
Hui Lu ◽  
Xiaodong Li ◽  
Guanghua Qin
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Passive Remote Sensing ◽  
Remote Sensing Techniques ◽  
Moisture Estimation

scholarly journals Error Propagation of Remote Sensing Rainfall Estimates in Soil Moisture Prediction from a Land Surface Model

2010 ◽  
Vol 11 (3) ◽  
pp. 705-720 ◽  
Author(s):  
Efthymios Serpetzoglou ◽  
Emmanouil N. Anagnostou ◽  
Anastasios Papadopoulos ◽  
Efthymios I. Nikolopoulos ◽  
Viviana Maggioni
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Error Propagation ◽  
Land Surface Model ◽  
Surface Model ◽  
Summer Period ◽  
Error Sources ◽  
Remote Sensing Techniques ◽  
Rainfall Estimates

Abstract The study presents an in-depth investigation of the properties of remotely sensed rainfall error propagation in the prediction of near-surface soil moisture from a land surface model (LSM). Specifically, two error sources are compared: rainfall forcing due to estimation error by remote sensing techniques and the representation of land–atmospheric processes due to LSM uncertainty [the Community Land Model, version 3.5 (CLM3.5), was used in this particular study]. CLM3.5 is forced by three remotely sensed precipitation products, namely, two satellite-based estimates—NASA’s Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis and NOAA’s Climate Prediction Center morphing technique (CMORPH)—and a rain gauge-adjusted radar–rainfall product from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. The error analysis is performed for the warm seasons of 2004 and 2006 and is facilitated through the use of in situ measurements of soil moisture, rainfall, and other meteorological variables from a network of stations capturing the state of Oklahoma (Oklahoma Mesonet). The study also presents a rigorous benchmarking of the Mesonet network as to its accuracy in deriving area rainfall estimates at the resolution of satellite products (0.25° and 3 h) through comparisons against the most definitive measurements of a smaller-yet-denser network of rain gauges in southwestern Oklahoma (Micronet). The study compares error statistics between modeling and precipitation error sources and between the various remote sensing techniques. Results are contrasted between the relatively moist summer period of 2004 to the drier summer period of 2006, indicating model and error propagation dependencies. An intercomparison between rainfall and modeling error shows that the two error sources are of similar magnitudes in the case of high modeling accuracy (i.e., 2004), whereas the contribution of rainfall forcing error to the uncertainty of soil moisture prediction can be lower when the model’s efficiency skill is relatively low (i.e., 2006).

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