Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E

Shi, Jiancheng; Jackson, Thomas J.; Tao, Jing; Du, Jinyang; Bindlish, Rajat; Lu, Lei; Chen, K.S.

doi:10.1016/j.rse.2008.07.015

Cited by 191 publications

(127 citation statements)

References 61 publications

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“…Such changes can be directly obtained by comparing measurements taken at different times [17][18][19]. Comparison methods include statistical regression models [20][21][22][23], inversion of physical models [24][25][26], or data assimilation [27,28]. Changes through time can be obtained through differencing, ratioing, trend analysis and direct change identification [29,30].…”

Section: Contents and Methods In Remote Sensing Of Environmental Changesmentioning

confidence: 99%

Remote sensing of environmental change over China: A review

Gong

2012

Chin. Sci. Bull.

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Section: Contents and Methods In Remote Sensing Of Environmental Changesmentioning

confidence: 99%

Remote sensing of environmental change over China: A review

Gong

2012

Chin. Sci. Bull.

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“…One is A parameter, and the other is B parameter. The two parameters are expressed in Equations (1) and (2) respectively [6].…”

Section: Basic Theorymentioning

confidence: 99%

“…These vegetation indices acquired from optical remote sensing have greatly helped us to understand the characteristics of vegetation at a regional and global scale. However, a major limitation of the NDVI and similar indices is that the optical sensors can only monitor a very thin layer of canopy [6]. The lack of information on the woody part of these optical vegetation indices will significantly influence their application in the study of the global carbon cycle.…”

Section: Introductionmentioning

confidence: 99%

“…These vegetation indices include microwave polarization difference temperatures (MPDT) at 37 GHz [7,8], the normalized microwave polarization difference index (MPDI) at frequencies 10 GHz and 36 GHz [9], the microwave emissivity difference vegetation index (EDVI) at 19 GHz and 37 GHz [10], the microwave vegetation indices (MVIs) at frequencies 6 GHz, 10 GHz and 19 GHz [6], and improved MVIs by considering measurements at different observation angles of WindSat [11,12]. The information of both leafy and woody parts of vegetation could be obtained from these microwave vegetation indices due to long wavelengths and the sensitivity of microwaves [6]. The long wavelength of microwaves is much larger than the diameter of cloud particles and makes it possible to acquire vegetation information in all weather conditions.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, the recently developed MVI in [6] will be taken as an example. The MVI is calculated from dual-frequency and dual-polarization of AMSR-E measurements.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Atmospheric Effect Analysis and Correction of the Microwave Vegetation Index

Shi

Letu

et al. 2017

Remote Sensing

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Microwave vegetation index (MVI) is a vegetation index defined in microwave bands. It has been developed based on observations from AMSR-E and widely used to monitor global vegetation. Recently, our study found that MVI was influenced by the atmosphere, although it was calculated from microwave bands. Ignoring the atmospheric influence might bring obvious uncertainty to the study of global vegetation. In this study, an atmospheric effect sensitivity analysis for MVI was carried out, and an atmospheric correction algorithm was developed to reduce the influence of the atmosphere. The sensitivity analysis showed that water vapor, clouds and precipitation were main parameters that had an influence on MVI. The result of the atmospheric correction on MVI was validated at both temporal and spatial scales. The validation showed that the atmospheric correction algorithm developed in this study could obviously improve the underestimation of MVI on most land surfaces. Seasonal patterns in the uncorrected MVI were obviously related to atmospheric water content besides vegetation changes. In addition, global maps of MVI showed significant differences before and after atmospheric correction in the northern hemisphere in the northern summer. The atmospheric correction will make the MVI more reliable and improve its performance in calculating vegetation biomass.

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Remote sensing of drought: Progress, challenges and opportunities

AghaKouchak

Farahmand

Melton

et al. 2015

Reviews of Geophysics

719

400

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This review surveys current and emerging drought monitoring approaches using satellite remote sensing observations from climatological and ecosystem perspectives. We argue that satellite observations not currently used for operational drought monitoring, such as near-surface air relative humidity data from the Atmospheric Infrared Sounder mission, provide opportunities to improve early drought warning. Current and future satellite missions offer opportunities to develop composite and multi-indicator drought models. While there are immense opportunities, there are major challenges including data continuity, unquantified uncertainty, sensor changes, and community acceptability. One of the major limitations of many of the currently available satellite observations is their short length of record. A number of relevant satellite missions and sensors (e.g., the Gravity Recovery and Climate Experiment) provide only a decade of data, which may not be sufficient to study droughts from a climate perspective. However, they still provide valuable information about relevant hydrologic and ecological processes linked to this natural hazard. Therefore, there is a need for models and algorithms that combine multiple data sets and/or assimilate satellite observations into model simulations to generate long-term climate data records. Finally, the study identifies a major gap in indicators for describing drought impacts on the carbon and nitrogen cycle, which are fundamental to assessing drought impacts on ecosystems.

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Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E

Cited by 191 publications

References 61 publications

Remote sensing of environmental change over China: A review

Remote sensing of environmental change over China: A review

Atmospheric Effect Analysis and Correction of the Microwave Vegetation Index

Remote sensing of drought: Progress, challenges and opportunities

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