scholarly journals Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe

2021 ◽  
Vol 258 ◽  
pp. 112377
Author(s):  
Laura Almendra-Martín ◽  
José Martínez-Fernández ◽  
María Piles ◽  
Ángel González-Zamora
Keyword(s):  
Soil Moisture ◽  
Southern Europe ◽  
Gap Filling

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

Lepidium latifolium (perennial pepperweed).

2021 ◽  
Author(s):  
Sonja Stutz ◽  
Hariet Hinz ◽  
Chris Parker
Keyword(s):  
Soil Moisture ◽  
Environmental Factors ◽  
Native Species ◽  
Southern Europe ◽  
Native Range ◽  
Riparian Areas ◽  
Plant Composition ◽  
Salt Ions ◽  
Threatened And Endangered Species ◽  
Western Asia

Abstract L. latifolium is an erect, branching perennial native to southern Europe and western Asia. It was accidentally introduced into countries outside of its native range as a contaminant of seeds such as Beta vulgaris. L. latifolium exhibits a wide ecological adaptation to different environmental factors, tolerating a range of soil moisture and salinity conditions, which has allowed it to spread explosively in recent years in wetlands and riparian areas especially in the western USA. L. latifolium thrives in many lowland ecosystems and is extremely competitive, forming monospecific stands that can crowd out desirable native species and a number of threatened and endangered species. L. latifolium alters the ecosystem in which it grows, acting as a 'salt pump' which takes salt ions from deep in the soil profile and deposits them near the surface, thereby shifting plant composition and altering diversity.

scholarly journals CLIMFILL: A Framework for Intelligently Gap-filling Earth Observations

2021 ◽  
Author(s):  
Verena Bessenbacher ◽  
Sonia I. Seneviratne ◽  
Lukas Gudmundsson
Keyword(s):  
Remote Sensing ◽  
Surface Layer ◽  
Soil Moisture ◽  
Missing Values ◽  
Performance Metrics ◽  
Environmental Research ◽  
Dependence Structure ◽  
Gap Filling ◽  
Earth Observations ◽  
Wide Range

Abstract. Earth observations have many missing values. Their abundance and often complex patterns can be a barrier for combining different observational datasets and may cause biased estimates. To overcome this, missing values in geoscientific data are regularly infilled with estimates through univariate gap-filling techniques such as spatio-temporal interpolation. However, these mostly ignore valuable information that may be present in other dependent observed variables. Here we propose CLIMFILL, a multivariate gap-filling procedure that builds up upon simple interpolation by additionally applying a statistical imputation method that is designed to account for dependence across variables. In contrast to popular up-scaling approaches, CLIMFILL does not need a gap-free gridded "donor" variable for gap-filling. CLIMFILL is tested using gap-free ERA5 re-analysis data of ground temperature, surface layer soil moisture, precipitation, and terrestrial water storage to represent central interactions between soil moisture and climate. These observations were matched with corresponding remote sensing observations and masked where the observations have missing values. CLIMFILL successfully recovers the dependence structure among the variables across all land cover types and altitudes, thereby enabling subsequent mechanistic interpretations. Soil moisture-temperature feedback, which is underestimated in high latitude regions due to sparse satellite coverage, is adequately represented in the multivariate gap-filling. Univariate performance metrics such as correlation and bias are improved compared to spatiotemporal interpolation gap-fill for a wide range of missing values and missingness patterns. Especially estimates for surface layer soil moisture profit taking into account the multivariate dependence structure of the data. The framework al- lows tailoring the gap-filling process to different environmental conditions, domains, or specific use cases and hence can be used as a flexible tool for gap-filling a large range of remote sensing and in situ observations commonly used in climate and environmental research.

Performance of a Physically Based Gap-Filling Technique of in-situ Soil Moisture, in Comparison with Machine Learning

2020 ◽  
Author(s):  
Seulchan Lee ◽  
Hyunho Jeon ◽  
Jongmin Park ◽  
Minha Choi
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Low Voltage ◽  
Time Domain Reflectometry ◽  
Limited Information ◽  
Gap Filling ◽  
Flux Tower ◽  
Physically Based ◽  
Filling Method

<p>As the importance of Soil Moisture (SM) has been recognized in various fields, including agricultural practices, natural hazards, and climate predictions, ground-based SM sensors such as Frequency Domain Reflectometry (FDR), Time Domain Reflectometry (TDR) are being widely used. However, gaps in in-situ SM data are still unavoidable due not only to sensor failure or low voltage supply, but to environmental conditions. Since it is essential to acquire accurate and continuous SM data for its application purpose, the gaps in the data should be handled properly. In this study, we propose a physically based gap-filling method in a mountainous region, in which in-situ SM measurements and flux tower are located. This method is developed only with in-situ SM and precipitation data, by considering variation characteristics of SM: increases rapidly with precipitation and decreases asymptotically afterward. SM data from the past is used to build Look-Up-Tables (LUTs) that contains the amount and speed of increment and decrement of SM, with and without precipitation, respectively. Based on the developed LUTs, the gaps are filled successively from where the gaps started. At the same time, we also introduce a machine learning-based gap-filling framework for the comparison. Ancillary data from the flux tower (e.g. net radiation, relative humidity) was used as input for training, with the same period as in the physically based method. The trained models are then used to fill the gaps. We found that both proposed methods are able to fill the gaps of in-situ SM reasonably, with capabilities to capture the characteristics of SM variation. Results from the comparison indicate that the physically based gap-filling method is very accurate and efficient when there’s limited information, and also suitable to be used for prediction purposes.</p>

scholarly journals Gap Filling of High‐Resolution Soil Moisture for SMAP/Sentinel‐1: A Two‐Layer Machine Learning‐Based Framework

2019 ◽  
Vol 55 (8) ◽  
pp. 6986-7009 ◽  
Author(s):  
Hanzi Mao ◽  
Dhruva Kathuria ◽  
Nick Duffield ◽  
Binayak P. Mohanty
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
High Resolution ◽  
Gap Filling

A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations

2012 ◽  
Vol 30 ◽  
pp. 139-142 ◽  
Author(s):  
Guojie Wang ◽  
Damien Garcia ◽  
Yi Liu ◽  
Richard de Jeu ◽  
A. Johannes Dolman
Keyword(s):  
Soil Moisture ◽  
Three Dimensional ◽  
Gap Filling ◽  
Filling Method

scholarly journals Potential Applicability of SMAP in ECV Soil Moisture Gap-Filling: A Case Study in Europe

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 133114-133127
Author(s):  
Yangxiaoyue Liu ◽  
Yaping Yang ◽  
Wenlong Jing
Keyword(s):  
Soil Moisture ◽  
Gap Filling ◽  
Potential Applicability

Mechanisms of European summer drying under climate change

2021 ◽  
pp. 1-51
Author(s):  
Alexandre Tuel ◽  
Elfatih A. B. Eltahir
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Regional Scale ◽  
Coupled Model ◽  
Absolute Humidity ◽  
Winter Precipitation ◽  
Stationary Waves ◽  
Southern Europe ◽  
Low Level ◽  
The North

AbstractThe geography of Europe as a continental landmass, located between the arid Sahara and the cold high latitudes (both are dry in terms of absolute humidity), dictates the reliance during summer of Southern Europe (south of 45°N) on stored water from winter and spring, and of Northwestern Europe on a small concentrated low-level moisture jet from the North Atlantic. In a recent study, we explained the projected winter precipitation decline over the Mediterranean under climate change as due to shifts in upper tropospheric stationary waves and to the regional-scale land-water warming contrast. Here, based on the analysis of observations and output from Coupled Model Intercomparison Project phase 5 models,we expand this theory further, documenting howthe winter precipitation decline expands into Southern Europe during spring, dictated by similar dynamical mechanisms, depleting soil moisture and setting the stage for drier summers via soil moisture-precipitation feedbacks. Over Northwestern Europe, an anomalous anticyclonic circulation west of the British Isles displaces the low-level moisture jet northwards, limiting moisture supply, and reducing low-level relative humidity (RH) and rainfall. Finally, we discuss how this comprehensive perspective of European summer climate change can help better understand the variations across model projections, and pave the way for their reduction.

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