L-Band Radar Soil Moisture Retrieval Without Ancillary Information
A radar-only retrieval algorithm for soil moisture mapping is applied to L-band scatterometer measurements from the Aquarius. The algorithm is based on a nonlinear relation between L-band backscatter and volumetric soil moisture and does not require ancillary information on the surface, e.g., land c...
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| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
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Institute of Electrical and Electronics Engineers
2015
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
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| LEADER | 11785caa a22012017a 4500 | ||
|---|---|---|---|
| 001 | PAPER-24595 | ||
| 003 | AR-BaUEN | ||
| 005 | 20250428091116.0 | ||
| 008 | 190411s2015 xx ||||fo|||| 00| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-84947982795 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 100 | 1 | |a Bruscantini, C.A. | |
| 245 | 1 | 0 | |a L-Band Radar Soil Moisture Retrieval Without Ancillary Information |
| 260 | |b Institute of Electrical and Electronics Engineers |c 2015 | ||
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| 504 | |a Santi, E., Paloscia, S., Pettinato, S., Fontanelli, G., A prototype ann based algorithm for the soil moisture retrieval from L-band in view of the incoming SMAP mission (2014) Proc. 13th Spec. Meeting Microw. Radiom. Remote Sens. Environ. (MicroRad), pp. 5-9. , Mar | ||
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| 506 | |2 openaire |e Política editorial | ||
| 520 | 3 | |a A radar-only retrieval algorithm for soil moisture mapping is applied to L-band scatterometer measurements from the Aquarius. The algorithm is based on a nonlinear relation between L-band backscatter and volumetric soil moisture and does not require ancillary information on the surface, e.g., land classification, vegetation canopy, surface roughness, etc. It is based on the definition of three limiting cases or end-members: 1) smooth bare soil; 2) rough bare soil; and 3) maximum vegetation condition. In this study, an estimation method is proposed for the end-member parameters that is iterative and only uses space-borne measurements. The major advantages of the algorithm include an analytic formulation (direct inversion possible), and the fact that there is no requirement for ancillary information. Ancillary data often misclassify the surface and the parameterizations linking surface classification to model parameter values are often highly uncertain. The retrieval algorithm is tested using 3 years of space-borne scatterometer observations from the Aquarius/SAC-D. Retrieved soil moisture accuracy is assessed in several ways: comparison of global spatial patterns with other available soil moisture products (two reanalysis modeling products and retrievals based on the Aquarius radiometer), extended triple collocation (ETC) and time series analysis over selected target areas. In general, low bias and standard deviation are observed with levels comparable to independent radiometer-based retrievals. The errors, however, increase across areas with high vegetation density. The results are promising and applicable to forthcoming L-band radar missions such as SMAP-NASA (2015) and SAOCOM-CONAE (2016). © 2015 IEEE. |l eng | |
| 593 | |a Department of Remote Sensing, Institute of Astronomy and Space Physics (IAFE), Ciudad de Buenos Aires, 1428, Argentina | ||
| 593 | |a Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02143, United States | ||
| 650 | 1 | 7 | |2 spines |a RADAR |
| 650 | 1 | 7 | |2 spines |a RADAR |
| 650 | 1 | 7 | |2 spines |a RADAR |
| 690 | 1 | 0 | |a AQUARIUS/SAC-D |
| 690 | 1 | 0 | |a MICROWAVE REMOTE SENSING |
| 690 | 1 | 0 | |a RADAR ROUGHNESS |
| 690 | 1 | 0 | |a RADAR VEGETATION INDEX (RVI) |
| 690 | 1 | 0 | |a SCATTEROMETER |
| 690 | 1 | 0 | |a SOIL MOISTURE |
| 690 | 1 | 0 | |a ALGORITHMS |
| 690 | 1 | 0 | |a CLASSIFICATION (OF INFORMATION) |
| 690 | 1 | 0 | |a ITERATIVE METHODS |
| 690 | 1 | 0 | |a METEOROLOGICAL INSTRUMENTS |
| 690 | 1 | 0 | |a MOISTURE |
| 690 | 1 | 0 | |a NASA |
| 690 | 1 | 0 | |a RADAR MEASUREMENT |
| 690 | 1 | 0 | |a RADIOMETERS |
| 690 | 1 | 0 | |a SOIL MOISTURE |
| 690 | 1 | 0 | |a SOILS |
| 690 | 1 | 0 | |a SPACE-BASED RADAR |
| 690 | 1 | 0 | |a SURFACE ROUGHNESS |
| 690 | 1 | 0 | |a TIME SERIES ANALYSIS |
| 690 | 1 | 0 | |a UNCERTAINTY ANALYSIS |
| 690 | 1 | 0 | |a VEGETATION |
| 690 | 1 | 0 | |a NONLINEAR RELATIONS |
| 690 | 1 | 0 | |a RETRIEVAL ALGORITHMS |
| 690 | 1 | 0 | |a SCATTEROMETER MEASUREMENTS |
| 690 | 1 | 0 | |a SOIL MOISTURE MAPPING |
| 690 | 1 | 0 | |a SOIL MOISTURE RETRIEVALS |
| 690 | 1 | 0 | |a SURFACE CLASSIFICATION |
| 690 | 1 | 0 | |a VEGETATION CONDITION |
| 690 | 1 | 0 | |a VOLUMETRIC SOIL MOISTURES |
| 690 | 1 | 0 | |a SOIL SURVEYS |
| 690 | 1 | 0 | |a ALGORITHM |
| 690 | 1 | 0 | |a AQUARIUS |
| 690 | 1 | 0 | |a MICROWAVE IMAGERY |
| 690 | 1 | 0 | |a REMOTE SENSING |
| 690 | 1 | 0 | |a SATELLITE DATA |
| 690 | 1 | 0 | |a SOIL MOISTURE |
| 700 | 1 | |a Konings, A.G. | |
| 700 | 1 | |a Narvekar, P.S. | |
| 700 | 1 | |a McColl, K.A. | |
| 700 | 1 | |a Entekhabi, D. | |
| 700 | 1 | |a Grings, Francisco Matías | |
| 700 | 1 | |a Karszenbaum, H. | |
| 773 | 0 | |d Institute of Electrical and Electronics Engineers, 2015 |g v. 8 |h pp. 5526-5540 |k n. 12 |p IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. |x 19391404 |t IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | |
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