Vegetation optical depth and scattering albedo retrieval using time series of dual-polarized L-band radiometer observations

Konings, Alexandra G.; Piles, María; Rötzer, Kathrina; McColl, Kaighin A.; Chan, S.; Entekhabi, Dara

doi:10.1016/j.rse.2015.11.009

Cited by 207 publications

(140 citation statements)

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“…The other important problem relevant to this in satellite soil moisture retrieval is the presence of dense vegetation covers, which causes uncertainty in soil moisture retrievals [89]. Therefore, retrieval models able to quantify the vegetation contribution to emissivity are needed for robust soil moisture retrievals, (see recent retrieval approaches for soil moisture and vegetation optical depth at L-band: [90,91]). …”

Section: Challenges In Operational Estimation Of Ssmmentioning

confidence: 99%

Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

et al. 2018

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Abstract:Global information on the spatio-temporal variation of parameters driving the Earth's terrestrial water and energy cycles, such as evapotranspiration (ET) rates and surface soil moisture (SSM), is of key significance. The water and energy cycles underpin global food and water security and need to be fully understood as the climate changes. In the last few decades, Earth Observation (EO) technology has played an increasingly important role in determining both ET and SSM. This paper reviews the state of the art in the use specifically of operational EO of both ET and SSM estimates. We discuss the key technical and operational considerations to derive accurate estimates of those parameters from space. The review suggests significant progress has been made in the recent years in retrieving ET and SSM operationally; yet, further work is required to optimize parameter accuracy and to improve the operational capability of services developed using EO data. Emerging applications on which ET/SSM operational products may be included in the context specifically in relation to agriculture are also highlighted; the operational use of those operational products in such applications remains to be seen.

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Section: Challenges In Operational Estimation Of Ssmmentioning

confidence: 99%

Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

et al. 2018

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“…Li et al, 2013) or vegetation optical depth (VOD; e.g. Konings et al (2016)). However, these products are rather used as input or boundary conditions for terrestrial carbon cycle models (burned area and land cover) or, in the case of land surface temperature and VOD, they have so far not been used in carbon cycle data assimilation studies.…”

Section: Examples Of Systematic Observations From Satellite Eo Datamentioning

confidence: 99%

Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

et al. 2017

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Abstract. The global carbon cycle is an important component of the Earth system and it interacts with the hydrology, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation and systematic and well errorcharacterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO 2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties).We briefly review the different existing data assimilation techniques and contrast them to model benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al. (2005), emphasising the rapid advance in relevant space-based observations.

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“…Several previous studies have investigated vegetation dynamics based on satellite passive microwave observations (Becker & Choudhury, 1988;Choudhury, Tucker, Golus, & Newcomb, 1987;Jones, Jones, Kimball, & McDonald, 2011;Konings et al, 2016;Min & Lin, 2006;Njoku & Chan, 2006;Cui, Shi, Du, Zhao, & Xiong, 2015;Shi et al, 2008). Unlike the optical remote sensing based vegetation indexes (sensitive to greenness, i.e.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel

Tian

Brandt

Liu

et al. 2016

Remote Sensing of Environment

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Monitoring long-term biomass dynamics in drylands is of great importance for many environmental applications including land degradation and global carbon cycle modeling. Biomass has extensively been estimated based on the normalized difference vegetation index (NDVI) as a measure of the vegetation greenness. The vegetation optical depth (VOD) derived from satellite passive microwave observations is mainly sensitive to the water content in total aboveground vegetation layer. VOD therefore provides a complementary data source to NDVI for monitoring biomass dynamics in drylands, yet further evaluations based on ground measurements are needed for an improved understanding of the potential advantages. In this study, we assess the capability of a long-term VOD dataset (1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) to capture the temporal and spatial variability of in situ measured green biomass (herbaceous mass and woody plant foliage mass) in the semi-arid Senegalese Sahel. Results show that the magnitude and peak time of VOD are sensitive to the woody plant foliage whereas NDVI seasonality is primarily governed by the green herbaceous vegetation stratum in the study area. Moreover, VOD is found to be more robust against typical NDVI drawbacks of saturation effect and dependence on plant structure (herbaceous and woody compositions) across the study area when used as a proxy for vegetation productivity. Finally, both VOD and NDVI well reflect the spatial and inter-annual dynamics of the in situ green biomass data; however, the seasonal metrics showing the highest degree of explained variance differ between the two data sources. While the observations in October (period of in situ data collection) perform best for VOD (r 2 = 0.88), the small growing season integral (sensitive to recurrent vegetation) have the highest correlations for NDVI (r 2 = 0.90). Overall, in spite of the coarse resolution, the study shows that VOD is an efficient proxy for estimating green biomass of the entire vegetation stratum in the semi-arid Sahel and likely also in other dryland areas.

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Vegetation optical depth and scattering albedo retrieval using time series of dual-polarized L-band radiometer observations

Cited by 207 publications

References 45 publications

Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel

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