Plant water content and temperature of the Amazon forest from satellite microwave radiometry

Calvet, J.‐C.; Wigneron, Jean‐Pierre; Mougin, E.; Kerr, Yann H.; Brito, Jorge Luís Silva

doi:10.1109/36.295054

Cited by 56 publications

(26 citation statements)

References 29 publications

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“…Future research should include radiative transfer modeling of the various land surfaces and comparisons with retrieved emissivities to further understand the complex physics of surface emissivities, possibly to invert the radiative transfer equation to retrieve soil and vegetation parameters [Calvet et al, 1994[Calvet et al, , 1996.…”

Section: Correlation With Vegetation Phenologymentioning

confidence: 99%

Microwave land surface emissivities estimated from SSM/I observations

Prigent¹,

Rossow²,

Matthews³

1997

J. Geophys. Res.

210

155

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Section: Correlation With Vegetation Phenologymentioning

confidence: 99%

Microwave land surface emissivities estimated from SSM/I observations

Prigent¹,

Rossow²,

Matthews³

1997

J. Geophys. Res.

210

155

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show abstract

“…and also for cultural practices (trafficability in the fields). According to Brisson and Perrier [2], the interest in monitoring the soil surface water status for crop simulation models is threefold: 1) it allows a better estimation of transpiration processes (the maximum transpiration depends on the soil moisture status through an effect of microlocal advection: higher temperatures and lower values of air moisture over dry soils increase the climatic demand at plant level); 2) it makes it possible to estimate the water status of plants at the beginning of the crop cycle; 3) it allows modelling of the mulch (soil dry surface layer) which is of interest in drastic dry conditions during the emergence stage (in this case competition between rooting and mulch deeping may be observed).…”

Section: Introductionmentioning

confidence: 99%

“…Actually, the microwave R-S techniques cannot (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) to relate the soil permittivity to soil parameters (soil moisture, bulk density, % of sand and clay, etc. ) [15,53].…”

Section: Introductionmentioning

confidence: 99%

Use of passive microwave remote sensing to monitor soil moisture

Wigneron¹,

Schmugge

Chanzy³

et al. 1998

Agronomie

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-Surface soil moisture is a key variable to describe the water and energy exchanges at the land surface/atmosphere interface. However, soil moisture is highly variable both spatially and temporally. Passive microwave remotely sensed data have great potential for providing estimates of soil moisture with good temporal repetition (on a daily basis) and at regional scale (∼10 km). This paper reviews the various methods for remote sensing of soil moisture from microwave radiometric systems. Potential

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“…In forests, the situation is more complex: indeed, although the first studies of microwave emission from forests date back to the mid 1970's (Borodin et al, 1976), the retrieval of SMC and SD under trees continues to pose a challenge. Specific studies of transmissivity of forest canopies were described in (Pampaloni, 2004;Hallikainen et al, 1988;Calvet et al, 1994;Kurvonen et al, 1998;Kruopis et al, 1999;Pulliainen et al, 1999;Santi et al, 2009).…”

Section: E Santi Et Al: Hydroalgomentioning

confidence: 99%

An algorithm for generating soil moisture and snow depth maps from microwave spaceborne radiometers: HydroAlgo

Santi

Pettinato

Paloscia

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract.A systematic and timely monitoring of land surface parameters that affect the hydrological cycle at local and global scales is of primary importance in obtaining a better understanding of geophysical processes and in managing environmental resources as well as natural disasters. Soil moisture and snow water equivalent are two quantities that play a major role in these applications. In this paper an algorithm for hydrological purposes (called hereinafter HydroAlgo), which is able to generate maps of snow depth (SD) and soil moisture content (SMC) from AMSR-E data, has been developed and implemented within the framework of the JAXA ADEOS-II/AMSR-E and GCOM/AMSR-2 programs, as well as of a project of the Italian Space Agency that is devoted to civil protection from floods and landslides. As auxiliary output, the algorithm also generates maps of vegetation biomass (VB). An initial phase of pre-processing includes the improvement of spatial resolution, as well as masking for urban areas, water bodies, and dense vegetation. The algorithm was then split into two branches, the first of which focused on the retrieval of SMC and the second, on SD. Both parameters were retrieved using Artificial Neural Network (ANN) methods. The algorithm was calibrated using a wide set of experimental data collected on three sites: Mongolia and Australia (for SMC), and Siberia (for SD), integrated with model simulations. These results were then validated by comparing the algorithm outputs with experimental data collected on two additional sites: a part of a watershed in Northern Italy, and a large portion of Scandinavia. An additional test of the algorithm was also performed on a large scale, and included sites characterized by differing climatic and meteorological conditions.

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Plant water content and temperature of the Amazon forest from satellite microwave radiometry

Cited by 56 publications

References 29 publications

Microwave land surface emissivities estimated from SSM/I observations

Microwave land surface emissivities estimated from SSM/I observations

Use of passive microwave remote sensing to monitor soil moisture

An algorithm for generating soil moisture and snow depth maps from microwave spaceborne radiometers: HydroAlgo

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