Spatio-temporal soil drying in southeastern South America: the importance of effective sampling frequency and observational errors on drydown time scale estimates-Reference-Cited by-同舟云学术

Spatio-temporal soil drying in southeastern South America: the importance of effective sampling frequency and observational errors on drydown time scale estimates

Published:2020-08-15 Issue:20 Volume:41 Page:7958-7992
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Ruscica Romina C.¹²³^ORCID,Polcher Jan⁴^ORCID,Salvia M. Mercedes⁵,Sörensson Anna A.¹²³^ORCID,Piles Maria⁶^ORCID,Jobbágy Esteban G.⁷^ORCID,Karszenbaum Haydee⁵

Affiliation:

1. Facultad De Ciencias Exactas Y Naturales, Universidad De Buenos Aires (FCEN/UBA) , Argentina

2. Centro De Investigaciones Del Mar Y La Atmósfera (CIMA/UBA-CONICET) , Buenos Aires, Argentina

3. Institut Franco-Argentin d’Etudes Sur Le Climat Et Ses Impacts (UMI 3351-IFAECI/CNRS-IRD-CONICET-UBA) , Buenos Aires, Argentina

4. Laboratoire De Météorologie Dynamique Du CNRS/IPSL Ecole Polytechnique , Paris, France

5. Quantitative Remote Sensing Group, Institute for Astronomy and Space Physics (IAFE, UBA-CONICET) , Buenos Aires, Argentina

6. Image Processing Lab (IPL), Universitat De València , Valencia, Spain

7. Grupo De Estudios Ambientales, IMASL-CONICET/Universidad Nacional De San Luis , San Luis, Argentina

Funder

International Inter-American Institute for Global Change Research (IAI) CRN 3035, which is supported by the U.S. National Science Foundation

National Science Foundation

LEFE/INSU, CNRS, France

ANPCyTs, Argentina

MCIU/AEI/FEDER, UE

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2020.1767825

Reference90 articles.

1. Estimation of Landscape Soil Water Losses from Satellite Observations of Soil Moisture

2. Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets

3. Climate change in Argentina: trends, projections, impacts and adaptation

4. MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

5. Assimilation of SMOS Retrievals in the Land Information System

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