A Field Verification of an Algorithm for Retrieving Vegetation Water Content From Passive Microwave Observations

Sawada, Yohei; Tsutsui, Hiroyuki; Koike, Takao; Rasmy, Mohamed; Seto, Rie; Fujii, Hideyuki

doi:10.1109/tgrs.2015.2495365

Cited by 31 publications

(37 citation statements)

References 28 publications

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“…One of the advantages of PI is that the effect of physical temperatures on PI is minimal. Although the relationship between PI and VWC has been validated in the previous studies (e.g., [19,30]), it is highly affected by land surface conditions (i.e., SSM and surface soil roughness) [22] and single scattering albedos [31]. It is important to evaluate the robustness of the PI-VWC relationship with a wide variety of land surface conditions and vegetation types.…”

Section: Vegetation Retrievalmentioning

confidence: 99%

“…This GBMR has four channels which cover the frequency ranges of 6.925, 10.65, 36.5, and 89 GHz at both horizontal and vertical polarizations. The detailed information about this GBMR (e.g., the height of the sensors, the sizes of footprint, and the sidelobe levels) can be found in [22]. The incident angle was set to 55 degrees, which is same as for AMSR-E and AMSR2.…”

Section: In-situ Observationmentioning

confidence: 99%

“…We planted oat, wheat, soybean, corn, and olive. Observations of period I-III have already been used by [22] to develop the SSM and VWC retrieval algorithm. In this paper, we analyzed observations of period IV and V as well as those of period I-III to derive the ground truth of microwave radiative transfer on vegetated surfaces.…”

Section: In-situ Observationmentioning

confidence: 99%

“…The RTM has been developed and used by [8,9,17,22]. In this RTM, surface reflectivity is calculated by the advanced integral equation model (AIEM) [32] with an incorporated shadowing effect [17].…”

Section: Radiative Transfer Modelmentioning

confidence: 99%

“…Although these valuables have great diversity on Earth surface, it is impossible to determine every parameter of RTM from brightness temperatures which are observed by currently available satellites. Therefore, many algorithms assumed fixed conditions of single scattering albedos and surface soil roughness [12,14,15] although some efforts have been made to objectively estimate these parameters by introducing other satellite observations (i.e., visible/infrared satellite observations) (e.g., [21,22]). This assumption brings significant uncertainties of their retrievals [23,24].…”

Section: Introductionmentioning

confidence: 99%

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Ground Truth of Passive Microwave Radiative Transfer on Vegetated Land Surfaces

2017

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Abstract:In this paper, we implemented the in-situ observation of surface soil moisture (SSM), vegetation water content (VWC), and microwave brightness temperatures. By analyzing this in-situ observation dataset and the numerical simulation, we investigated the source of the uncertainty of the current algorithms for Advanced Microwave Scanning Radiometer for Earth observation system (AMSR-E) and AMSR2 to retrieve SSM and vegetation dynamics. Our findings are: (1) the microwave radiative transfer at C-band and X-band is not strongly affected by the shape of vegetation and the existing algorithm can be applied to a wide variety of plant types; (2) the diversity of surface soil roughness significantly affects the indices which are used by the current algorithms and addressing the uncertainty of surface soil roughness is necessary to improve the retrieval algorithms; (3) At C-band, SSM of the homogeneous vegetated land surfaces can be detected only when their VWC is less than approximately 0.25 (kg/m 2 ); (4) the state-of-the-art Radiative Transfer Model (RTM) can predict our observed dataset although we have some biases in simulating brightness temperatures at a higher frequency. The new in-situ observation dataset produced by this study can be the guideline for both developers and users of passive microwave land observations to consider the uncertainties of their products.

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Section: Vegetation Retrievalmentioning

confidence: 99%

Section: In-situ Observationmentioning

confidence: 99%

Section: In-situ Observationmentioning

confidence: 99%

Section: Radiative Transfer Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ground Truth of Passive Microwave Radiative Transfer on Vegetated Land Surfaces

2017

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Towards ecohydrological drought monitoring and prediction using a land data assimilation system: A case study on the Horn of Africa drought (2010–2011)

Sawada

Koike

2016

JGR Atmospheres

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Despite the importance of the ecological and agricultural aspects of severe droughts, no drought monitoring and prediction framework based on a land data assimilation system (LDAS) has been developed to monitor and predict vegetation dynamics in the middle of droughts. In this study, we applied a LDAS that can simulate surface soil moisture, root-zone soil moisture, and vegetation dynamics to the Horn of Africa drought in 2010-2011 caused by the precipitation deficit in two consecutive rainy seasons. We successfully simulated the ecohydrological drought quantified by the model-estimated soil moistures and leaf area index (LAI). The root-zone soil moisture and LAI are good indicators of prolonged droughts because they reflect the long-term effects of past precipitation deficit. The precipitation deficit in 2010 significantly affected the land surface condition of the next rainy season in 2011, which indicated the importance of obtaining accurate initial soil moisture and LAI values for prediction of multiseasonal droughts. In addition, the general circulation model-based seasonal meteorological prediction showed good performance in predicting land surface conditions of the Horn of Africa drought.In this study, we followed the strategy of Sawada et al. [2014] and used model-estimated soil moistures and LAI as drought indicators. The type of drought quantified by soil moistures and LAI is identified as an ecohydrological drought in this paper. SAWADA AND KOIKELDAS FOR ECOHYDROLOGICAL DROUGHT 8229

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