scholarly journals A remote sensing and modeling integrated approach for constructing continuous time series of daily actual evapotranspiration

2022 ◽  
Vol 260 ◽  
pp. 107320
Author(s):  
Hassan Awada ◽  
Simone Di Prima ◽  
Costantino Sirca ◽  
Filippo Giadrossich ◽  
Serena Marras ◽  
...  
2020 ◽  
Author(s):  
Mahdi Motagh ◽  
Sigrid Roessner ◽  
Bahman Akbari ◽  
Robert Behling ◽  
Magdalena Stefanova Vassileva ◽  
...  

<p>Between mid-March and the beginning of April 2019, extremely high precipitation affected the whole Iran, leading to widespread flash flooding and landslides. Approximately 10 million people were affected, among them 2 million were in humanitarian needs. The event caused 78 fatalities, more than 1000 injuries and widespread damage in 25 out of the 31 provinces.</p><p>In this work, we use both high resolution – spatial and temporal – optical and radar satellite remote sensing to characterize spatiotemporal pattern of landslide occurrence related to the main hydro-meteorological triggering events in Golestan province, North Iran. Large-area landslide detection has been performed in a semi-automated way using time series of optical Planet Scope and Sentinel-2A/B data. The obtained satellite remote sensing based results were evaluated by field surveys conducted in September 2019 in cooperation between the GFZ Potsdam and the Forest, Range and Watershed Management Organization of Iran (FRWM) being responsible for landslide hazard and risk assessment as well as the design and implementation of mitigation measures.</p><p>Moreover, we report on our deformation monitoring using Sentinel-1/B based differential interferometric synthetic aperture radar (DInSAR) on hot-spots areas to investigate whether any of the catastrophic landslides that happened in spring of 2019 have shown precursory signs in form of preparatory deformation. In particular, we present our detailed investigation for Hossein Abad Kalpush landslide, located at the border between Golestan and Semnan provinces. In April 2019, this slide slipped at an unprecedented scale, causing total destruction of one part of the village nearby with complete destruction of 250 houses. Using an integrated approach exploring satellite imagery, in-situ measurements and field survey, we perform detailed time-series analysis of the evolution of Hossein Abad Kalpush landslide and examine the role of meteorological and anthropogenic influencing factors in controlling the behaviour of this landslide.</p>


2021 ◽  
Vol 11 (15) ◽  
pp. 6923
Author(s):  
Rui Zhang ◽  
Zhanzhong Tang ◽  
Dong Luo ◽  
Hongxia Luo ◽  
Shucheng You ◽  
...  

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.


2012 ◽  
Vol 8 (1) ◽  
pp. 89-115 ◽  
Author(s):  
V. K. C. Venema ◽  
O. Mestre ◽  
E. Aguilar ◽  
I. Auer ◽  
J. A. Guijarro ◽  
...  

Abstract. The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.


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