Ground Truth of Passive Microwave Radiative Transfer on Vegetated Land Surfaces

Sawada, Yohei; Tsutsui, Hiroyuki; Koike, Takao

doi:10.3390/rs9070655

Cited by 7 publications

(25 citation statements)

References 37 publications

(78 reference statements)

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“…It should be noted that the performance of the SMAP soil moisture product also exhibited some improvements under the lower vegetation condition, with the ubRMSE dropping from 0.06 m 3 /m 3 to 0.05 m 3 /m 3 and the R rising from 0.45 to 0.53. These results were also consistent with the consensus that the L-band is more sensitive to the surface soil moisture, especially in densely vegetated regions [2,37,56,57]. Figure 12.…”

Section: Discussionsupporting

confidence: 90%

“…For SMAP, the MD was nearly unaffected by the VWC. These results further indicated that the L-band is less affected by the vegetation and more sensitive to the surface soil moisture [2,37,56,57]. Regarding RMSE and ubRMSE (Figure 13b,c), FY-3C also showed some positive correlation with VWC.…”

Section: Discussionmentioning

confidence: 64%

“…For instance, Calvet, et al [56] indicated that a statistical soil moisture retrieval algorithm using C-and X-bands did not perform well with approximately 0-3 kg/m 2 VWC. Sawada, et al [57] conducted in-situ observations of microwave brightness temperature, VWC, and soil moisture and revealed that there are few correlations between microwave signal and surface soil moisture when the VWC is larger than 0.3 kg/m 2 .…”

Section: Discussionmentioning

confidence: 99%

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Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

Zhu

Pearson

et al. 2019

Water

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Soil moisture (SM) products derived from passive satellite missions are playing an increasingly important role in agricultural applications, especially crop monitoring and disaster warning. Evaluating the dependability of satellite-derived soil moisture products on a large scale is crucial. In this study, we assessed the level 2 (L2) SM product from the Chinese Fengyun-3C (FY-3C) radiometer against in-situ measurements collected from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) during a one-year period from 1 January 2016 to 31 December 2016 across Henan in China. In contrast, we also investigated the skill of the Advanced Microwave Scanning Radiometer 2 (AMSR2) and Soil Moisture Active/Passive (SMAP) SM products simultaneously. Four statistical parameters were used to evaluate these products’ reliability: mean difference, root-mean-square error (RMSE), unbiased RMSE (ubRMSE), and the correlation coefficient. Our assessment results revealed that the FY-3C L2 SM product generally showed a poor correlation with the in-situ SM data from CASMOS on both temporal and spatial scales. The AMSR2 L3 SM product of JAXA (Japan Aerospace Exploration Agency) algorithm had a similar level of skill as FY-3C in the study area. The SMAP L3 SM product outperformed the FY-3C temporally but showed lower performance in capturing the SM spatial variation. A time-series analysis indicated that the correlations and estimated error varied systematically through the growing periods of the key crops in our study area. FY-3C L2 SM data tended to overestimate soil moisture during May, August, and September when the crops reached maximum vegetation density and tended to underestimate the soil moisture content during the rest of the year. The comparison between the statistical parameters and the ground vegetation water content (VWC) further showed that the FY-3C SM product performed much better under a low VWC condition (<0.3 kg/m2) than a high VWC condition (>0.3 kg/m2), and the performance generally decreased with increased VWC. To improve the accuracy of the FY-3C SM product, an improved algorithm that can better characterize the variations of the ground VWC should be applied in the future.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 64%

Section: Discussionmentioning

confidence: 99%

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Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

Zhu

Pearson

et al. 2019

Water

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show abstract

Section: Discussionsupporting

confidence: 91%

Evaluation of Fengyun-3C Soil Moisture Products Using In situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

Zhu¹,

Pearson²,

Wu³

et al. 2018

Preprint

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Soil moisture (SM) products derived from passive satellite missions are playing an increasingly important role in agricultural applications, especially in crop monitoring and disaster warning. Evaluating the dependability of those products before they can be used on a large scale is crucial. In this study, we assessed the level 2 (L2) SM product from the Chinese Fengyun-3C (FY-3C) radiometer against in situ measurements collected from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) during a one-year period from January 1 to December 31, 2016 in Henan, which is an agricultural province in China. Four statistical parameters were used to evaluate the products’ reliability: mean difference, root-mean-square error (RMSE), unbiased RMSE (ubRMSE), and the correlation coefficient. These statistical indicators revealed that the FY-3C L2 SM product generally did not agree with the in situ SM data from CASMOS. The time-series analysis further indicated that the correlations and estimated error were highly related to the growing periods of the crops in our study area. FY-3C L2 SM data tended to overestimate soil moisture during May, August, and September, when the crops reach their maximum vegetation density, and tended to underestimate the soil moisture content during the rest of the year. The averaged correlation coefficient between FY-3C SM and the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index was 0.55, which demonstrates that the vegetation water content of the crops considerably influences the SM product. To improve the accuracy of the FY-3C SM product, an improved algorithm that can filter out the influences of the crops should be applied in the future.

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“…In Site I (0.125°E, 10.125°N; see Figure 1b), annual precipitation is approximately 1,190 (mm/year). In the synthetic truth, LAI varies temporally between 0.5 and 2 so that the site is moderately vegetated and surface soil moisture can be observed from the satellite especially in the dry seasons (see Sawada et al, 2017, for the sensitivity of microwave brightness temperature to surface soil moisture in the vegetated area). Figure 2a shows the response of each parameter to the gap between simulation and observation.…”

Section: 1029/2020jd032688mentioning

confidence: 99%

Machine Learning Accelerates Parameter Optimization and Uncertainty Assessment of a Land Surface Model

Sawada

2020

JGR Atmospheres

Self Cite

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The performance of land surface models (LSMs) significantly affects the understanding of atmospheric and related processes. Many of the LSMs' soil and vegetation parameters were highly uncertain so that it is crucially important to efficiently optimize them. Here I present a globally applicable and computationally efficient method for parameter optimization and uncertainty assessment of the LSM by combining Markov Chain Monte Carlo (MCMC) with machine learning. First, I performed the long-term (decadal-scale) ensemble simulation of the LSM, in which each ensemble member has different parameters' values, and calculated the gap between simulation and observation, or the cost function, for each ensemble member. Second, I developed the statistical machine learning-based surrogate model, which is computationally cheap but accurately mimics the relationship between parameters and the cost function, by applying the Gaussian process regression to learn the model simulation. Third, I applied MCMC by repeatedly evaluating the surrogate model to get the posterior probabilistic distribution of parameters. Using satellite passive microwave brightness temperature observations, both synthetic and real-data experiments in the Sahel region of west Africa were performed to optimize unknown soil and vegetation parameters of the LSM. The primary findings are (1) the proposed method is 50,000 times as fast as the direct application of MCMC to the full LSM; (2) the skill of the LSM to simulate both soil moisture and vegetation dynamics can be improved; and (3) I successfully quantify the characteristics of equifinality by obtaining the full nonparametric probabilistic distribution of parameters.

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Ground Truth of Passive Microwave Radiative Transfer on Vegetated Land Surfaces

Cited by 7 publications

References 37 publications

Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

Evaluation of Fengyun-3C Soil Moisture Products Using In situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

Machine Learning Accelerates Parameter Optimization and Uncertainty Assessment of a Land Surface Model

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