JabRef references

Ma W, Wei Z, Wang P and Asanuma J (2020), "Transpiration and evaporation of grassland using land surface modelling", Hydrological Processes., jun, 2020. Wiley.

[Abstract] [BibTeX] [DOI]

Abstract: Partitioning transpiration (T) from evapotranspiration (ET) is a key process for understanding the interaction between land surfaces and the atmosphere. This paper reports daily partitioning results for a grassland over a 10‐year period, obtained using the Community Land Model 3.5 (CLM3.5) land surface model. Hourly forcing data were collected from a long‐term observation system located in the northeast of Japan (http://doi.org/10.24575/0001.198108). To test the model behavior, total ET was validated using eddy correlation measurements combined with the energy balance method. The results were compared with previous research using an isotope approach for partitioning. The results demonstrate that our model can capture the dynamics of ET and its components at this location. Evaporation (E), originating from the ground and canopy, varied inter‐annually, and from 2006 to 2015, average annual E was approximately 285 mm/year from the ground and 45 mm/year from the canopy. Average, T, was approximately 302 mm/year, accounting for approximately 48% of the total ET. Inter‐annual results demonstrate that the water flux transported by vegetation ranges from 17 to 83% during the April–October period. A sensitivity test conducted with forcing data indicates air temperature, incident solar radiation, and longwave radiation exhibited a notable effect on all ET components. Relative humidity exhibited the only negative feedback to both evaporation and transpiration, contrary to the other forcing parameters. Our study reemphasized the effectiveness of CLM3.5 in partitioning T from ET and in understanding the complex interaction between land surfaces and the atmosphere.

BibTeX:

@article{Ma2020,
  author = {Wenchao Ma and Zhongwang Wei and Pei Wang and Jun Asanuma},
  title = {Transpiration and evaporation of grassland using land surface modelling},
  journal = {Hydrological Processes},
  publisher = {Wiley},
  year = {2020},
  doi = {10.1002/hyp.13792}
}

Chen F, Crow WT, Cosh MH, Colliander A, Asanuma J, Berg A, Bosch DD, Caldwell TG, Collins CH, Jensen KH, Mart\inez-Fernández J, McNairn H, Starks PJ, Su Z and Walker JP (2019), "Uncertainty of Reference Pixel Soil Moisture Averages Sampled at SMAP Core Validation Sites", Journal of Hydrometeorology., aug, 2019. Vol. 20(8), pp. 1553-1569. American Meteorological Society.

[Abstract] [BibTeX] [DOI]

Abstract: Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active Passive (SMAP) mission, spatial averages of point-scale soil moisture observations often fail to accurately capture the true average of the reference pixels. Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals. Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed. The estimated uncertainty (due to spatial sampling limitations) of mean CSASM over time is found to be large, translating into relatively large sampling uncertainty levels for SMAP retrieval bias when calculated against CSASM. As a result, CSASM-based SMAP bias estimates are statistically insignificant at nearly all SMAP CVS. In addition, observations from temporary networks suggest that these (already large) bias uncertainties may be underestimated due to undersampled spatial variability. The unbiased root-mean-square error (ubRMSE) of CSASM is estimated via two approaches: classical sampling theory and triple collocation, both of which suggest that CSASM ubRMSE is generally within the range of 0.01–0.02 m3 m−3. Although limitations in both methods likely lead to underestimation of ubRMSE, the results suggest that CSASM captures the temporal dynamics of the footprint-scale soil moisture relatively well and is thus a reliable reference for SMAP ubRMSE calculations. Therefore, spatial sampling errors are revealed to have very different impacts on efforts to estimate SMAP bias and ubRMSE metrics using CVS data.

BibTeX:

@article{chen19,
  author = {Fan Chen and Wade T. Crow and Michael H. Cosh and Andreas Colliander and Jun Asanuma and Aaron Berg and David D. Bosch and Todd G. Caldwell and Chandra Holifield Collins and Karsten Høgh Jensen and Jose Mart\inez-Fernández and Heather McNairn and Patrick J. Starks and Zhongbo Su and Jeffrey P. Walker},
  title = {Uncertainty of Reference Pixel Soil Moisture Averages Sampled at SMAP Core Validation Sites},
  journal = {Journal of Hydrometeorology},
  publisher = {American Meteorological Society},
  year = {2019},
  volume = {20},
  number = {8},
  pages = {1553--1569},
  doi = {10.1175/JHM-D-19-0049.1}
}

Kaihotsu I, Asanuma J, Aida K and Oyunbaatar D (2019), "Evaluation of the AMSR2 L2 soil moisture product of JAXA on the Mongolian Plateau over seven years (2012 - 2018)", SN Applied Sciences., oct, 2019. Vol. 1(11) Springer Science and Business Media LLC.

[Abstract] [BibTeX] [DOI]

Abstract: This study evaluated the Advanced Microwave Scanning Radiometer 2 (AMSR2) L2 soil moisture product (ver. 3) using in situ hydrological observational data, acquired over 7 years (2012–2018), from a 50 × 50 km flat area of the Mongolian Plateau covered with bare soil, pasture and shrubs. Although AMSR2 slightly underestimated soil moisture content at 3-cm depth, satisfactory timing was observed in both the response patterns and the in situ soil moisture data, and the differences between these factors were not large. In terms of the relationship between AMSR2 soil moisture from descending orbits and in situ measured soil moisture at 3-cm depth, the values of the RMSE (m3/m3) and the bias (m3/m3) varied from 0.028 to 0.063 and from 0.011 to − 0.001 m3/m3, respectively. The values of the RMSE and bias depended on rainfall condition. The mean value of the RMSE for the 7-year period was 0.042 m3/m3, i.e., lower than the target accuracy 0.050 m3/m3. The validation results for descending orbits were found slightly better than for ascending orbits. Comparison of the Soil Moisture and Ocean Salinity (SMOS) soil moisture product with the AMSR2 L2 soil moisture product showed that AMSR2 could observe surface soil moisture with nearly same accuracy and stability. However, the bias of the AMSR2 soil moisture measurement was slightly negative and poorer than that of SMOS with deeper soil moisture measurement. It means that AMSR2 cannot effectively measure soil moisture at 3-cm depth. In situ soil temperature at 3-cm depth and surface vegetation (normalized difference vegetation index) did not influence the underestimation of AMSR2 soil moisture measurements. These results suggest that a possible cause of the underestimation of AMSR2 soil moisture measurements is the difference between the depth of the AMSR2 observations and in situ soil moisture measurements. Overall, this study proved the AMSR2 L2 soil moisture product has been useful for monitoring daily surface soil moisture over large grassland areas and it clearly demonstrated the high-performance capability of AMSR2 since 2012.

BibTeX:

@article{kaihotsu19,
  author = {Ichirow Kaihotsu and Jun Asanuma and Kentaro Aida and Dambaravjaa Oyunbaatar},
  title = {Evaluation of the AMSR2 L2 soil moisture product of JAXA on the Mongolian Plateau over seven years (2012 - 2018)},
  journal = {SN Applied Sciences},
  publisher = {Springer Science and Business Media LLC},
  year = {2019},
  volume = {1},
  number = {11},
  doi = {10.1007/s42452-019-1488-y}
}

Bindlish R, Cosh M, Jackson T, Koike T, Fujii H, Chan S, Asanuma J, Berg A, Bosch D, Caldwell T, Collins C, Mcnairn H, Martinez-Fernandez J, Prueger J, Rowlandson T, Seyfried M, Starks P, Thibeault M, Van Der Velde R, Walker J and Coopersmith E (2018), "GCOM-W AMSR2 soil moisture product validation using core validation sites.", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Vol. 11(1), pp. 209-219.

[Abstract] [BibTeX] [DOI]

Abstract: Abstract:
The Advanced microwave scanning radiometer 2 (AMSR2) is part of the global change observationmission-water (GCOM-W). AMSR2 has filled the gap in passive microwave observations left by the loss of theAMSR-earth observing system (AMSR-E) after almost ten years of observations. Both missions provide brightness temperature observations that are used to retrieve soil moisture estimates at the near surface. A merged AMSR-E and AMSR2 data product will help build a consistent long-term dataset; however, before this can be done, it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on the validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites around the world. A total of three soil moisture products that rely on different algorithms were evaluated; the Japan Aerospace Exploration Agency (JAXA) soil moisture algorithm, the land parameter retrieval model (LPRM), and the single channel algorithm (SCA). JAXA, SCA, and LPRM soil moisture estimates capture the overall climatological features. The spatial features of the three products have similar overall spatial structure. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture with a satisfactory performance matrix when compared to in situ observations [unbiased root mean square error (ubRMSE) = 0.059 m3/m3, Bias = -0.083 m3 /m3, R = 0.465]. The SCA performs well over low and moderately vegetated areas (ubRMSE = 0.053 m3/m3, Bias = -0.039 m3/m3, R = 0.549). The LPRM product has a large dynamic range compared to in situ observations with a wet bias (ubRMSE = 0.094 m3 /m3, Bias = 0.091 m3/m3, R = 0.577). Some of the error is due to the difference in observation depth between the in situ sensors (5 cm) and satellite estimates (1 cm). Results indicate that overall the JAXA and SCA have the best performance based upon the metrics considered.

BibTeX:

@article{bindish17,
  author = {Bindlish, R. and Cosh, M.H. and Jackson, T.J. and Koike, T. and Fujii, H. and Chan, S.K. and Asanuma, J. and Berg, A. and Bosch, D.D. and Caldwell, T. and Collins, C.H. and Mcnairn, H. and Martinez-Fernandez, J. and Prueger, J. and Rowlandson, T. and Seyfried, M. and Starks, P. and Thibeault, M. and Van Der Velde, R. and Walker, J.P. and Coopersmith, E.J.},
  title = {GCOM-W AMSR2 soil moisture product validation using core validation sites.},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2018},
  volume = {11},
  number = {1},
  pages = {209-219},
  doi = {10.1109/JSTARS.2017.2754293}
}

Chan S, Bindlish R, O'Neill P, Jackson T, Njoku E, Dunbar S, Chaubell J, Piepmeier J, Yueh S, Entekhabi D, Colliander A, Chen F, Cosh M, Caldwell T, Walker J, Berg A, McNairn H, Thibeault M, Mart?nez-Fern?ndez J, Uldall F, Seyfried M, Bosch D, Starks P, Collins CH, Prueger J, van der Velde R, Asanuma J, Palecki M, Small E, Zreda M, Calvet J, Crow W and Kerr Y (2018), "Development and assessment of the SMAP enhanced passive soil moisture product", Remote Sensing of Environment. Vol. 204(Supplement C), pp. 931 - 941.