Optimal model-based temperature inputs for global soil moisture and vegetation optical depth retrievals from SMAP

Xiao, Yao; Li, Xiaojun; Fan, Lei; De Lannoy, Gabrielle; Peng, Jian; Frappart, Frédéric; Ebtehaj, Ardeshir; de Rosnay, Patricia; Xing, Zanpin; Yu, Ling; Dong, Guanyu; Yueh, Simon H.; Colliander, Andress; Wigneron, Jean-Pierre

doi:10.1016/j.rse.2024.114240

Cited by 3 publications

(1 citation statement)

References 93 publications

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“…The daily L-VOD and SM retrievals with RMSE-TB exceeding 6 K were systematically excluded to ensure the reliability of the dataset. Then, monthly values for L-VOD and SM were derived by computing the median values of high-quality retrievals from both ascending and descending orbits, with a requirement of at least four valid retrievals per month (Xiao et al, 2024). To derive robust annual L-VOD and SM retrievals, we calculated the median of the corresponding monthly products.…”

Section: Annual Agc and Sm Dataset During 2014-2020mentioning

confidence: 99%

Dominant role of the non‐forest woody vegetation in the post 2015/16 El Niño tropical carbon recovery

Fan,

Cui,

Wigneron

et al. 2024

Global Change Biology

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The extreme dry and hot 2015/16 El Niño episode caused large losses in tropical live aboveground carbon (AGC) stocks. Followed by climatic conditions conducive to high vegetation productivity since 2016, tropical AGC are expected to recover from large losses during the El Niño episode; however, the recovery rate and its spatial distribution remain unknown. Here, we used low‐frequency microwave satellite data to track AGC changes, and showed that tropical AGC stocks returned to pre‐El Niño levels by the end of 2020, resulting in an AGC sink of Pg C year−1 during 2014–2020. This sink was dominated by strong AGC increases ( Pg C year−1) in non‐forest woody vegetation during 2016–2020, compensating the forest AGC losses attributed to the El Niño event, forest loss, and degradation. Our findings highlight that non‐forest woody vegetation is an increasingly important contributor to interannual to decadal variability in the global carbon cycle.

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Section: Annual Agc and Sm Dataset During 2014-2020mentioning

confidence: 99%

Dominant role of the non‐forest woody vegetation in the post 2015/16 El Niño tropical carbon recovery

Fan,

Cui,

Wigneron

et al. 2024

Global Change Biology

Self Cite

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Satellite-based monitoring of China's above-ground biomass carbon sink from 2015 to 2021

Fang,

Fan,

Ciais

et al. 2024

Agricultural and Forest Meteorology

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An Assessment of the Seasonal Uncertainty of Microwave L-Band Satellite Soil Moisture Products in Jiangsu Province, China

Yi,

Li,

Xing

et al. 2024

Remote Sensing

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Accurate surface soil moisture (SM) data are crucial for agricultural management in Jiangsu Province, one of the major agricultural regions in China. However, the seasonal performance of different SM products in Jiangsu is still unknown. To address this, this study aims to evaluate the applicability of four L-band microwave remotely sensed SM products, namely, the Soil Moisture Active Passive Single-Channel Algorithm at Vertical Polarization Level 3 (SMAP SCA-V L3, hereafter SMAP-L3), SMOS-SMAP-INRAE-BORDEAUX (SMOSMAP-IB), Soil Moisture and Ocean Salinity in version IC (SMOS-IC), and SMAP-INRAE-BORDEAUX (SMAP-IB) in Jiangsu at the seasonal scale. In addition, the effects of dynamic environmental variables such as the leaf vegetation index (LAI), mean surface soil temperature (MSST), and mean surface soil wetness (MSSM) on the performance of the above products are investigated. The results indicate that all four SM products exhibit significant seasonal differences when evaluated against in situ observations between 2016 and 2022, with most products achieving their highest correlation (R) and unbiased root-mean-square difference (ubRMSD) scores during the autumn. Conversely, their performance significantly deteriorates in the summer, with ubRMSD values exceeding 0.06 m3/m3. SMOS-IC generally achieves better R values across all seasons but has limited temporal availability, while SMAP-IB typically has the lowest ubRMSD values, even reaching 0.03 m3/m3 during morning observation in the winter. Additionally, the sensitivity of different products’ skill metrics to environmental factors varies across seasons. For ubRMSD, SMAP-L3 shows a general increase with LAI across all four seasons, while SMAP-IB exhibits a notable increase as the soil becomes wetter in the summer. Conversely, wet conditions notably reduce the R values during autumn for most products. These findings are expected to offer valuable insights for the appropriate selection of products and the enhancement of SM retrieval algorithms.

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Optimal model-based temperature inputs for global soil moisture and vegetation optical depth retrievals from SMAP

Cited by 3 publications

References 93 publications

Dominant role of the non‐forest woody vegetation in the post 2015/16 El Niño tropical carbon recovery

Dominant role of the non‐forest woody vegetation in the post 2015/16 El Niño tropical carbon recovery

Satellite-based monitoring of China's above-ground biomass carbon sink from 2015 to 2021

An Assessment of the Seasonal Uncertainty of Microwave L-Band Satellite Soil Moisture Products in Jiangsu Province, China

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