Triple Collocation Analysis and In Situ Validation of the CYGNSS Soil Moisture Product

Deng, Xiaodong; Zhu, Luyao; Wang, Hongquan; Zhang, XianYun; Tong, Cheng; Li, Sinan; Wang, Ke

doi:10.1109/jstars.2023.3235111

Cited by 8 publications

(4 citation statements)

References 69 publications

(81 reference statements)

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“…These methods do not demand a high-quality reference dataset but instead rely on the availability of spatially and temporally corresponding datasets 23,24 . Collocation methods have found widespread application in assessing various geophysical variables, encompassing soil moisture 25,26 , precipitation 27,28 , ocean wind speed 29,30 , leaf area index 31 , total water storage 32 sea ice thickness and surface salinity 33 , and near-surface air temperature 34 .…”

Section: Background and Summarymentioning

confidence: 99%

A harmonized global gridded transpiration product based on collocation analysis

Li,

Han,

Liu

et al. 2024

Sci Data

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Transpiration (T) is pivotal in the global water cycle, responding to soil moisture, atmospheric stress, climate changes, and human impacts. Therefore, establishing a reliable global transpiration dataset is essential. Collocation analysis methods have been proven effective for assessing the errors in these products, which can subsequently be used for multisource fusion. However, previous results did not consider error cross-correlation, rendering the results less reliable. In this study, we employ collocation analysis, taking error cross-correlation into account, to effectively analyze the errors in multiple transpiration products and merge them to obtain a more reliable dataset. The results demonstrate its superior reliability. The outcome is a long-term daily global transpiration dataset at 0.1°from 2000 to 2020. Using the transpiration after partitioning at FLUXNET sites as a reference, we compare the performance of the merged product with inputs. The merged dataset performs well across various vegetation types and is validated against in-situ observations. Incorporating non-zero ECC considerations represents a significant theoretical and proven enhancement over previous methodologies that neglected such conditions, highlighting its reliability in enhancing our understanding of transpiration dynamics in a changing world.

show abstract

Section: Background and Summarymentioning

confidence: 99%

A harmonized global gridded transpiration product based on collocation analysis

Li,

Han,

Liu

et al. 2024

Sci Data

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“…Dong et al (2020a) further proposed the EIVD method, enabling ECC estimation using three datasets. Collocation methods have found widespread application in the evaluation of geophysical variable estimates, including soil moisture (Deng et al, 2023;Ming et al, 2022), precipitation (Dong et al, 2022;, ocean wind speed (Vogelzang et al, 2022;Ribal and Young, 2020), leaf area index (Jiang et al, 2017), total water storage (Yin and Park, 2021), sea ice thickness and surface salinity (Hoareau et al, 2018), and near-surface air temperature (Sun et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data

Li,

Liu,

Yang

et al. 2024

Earth Syst. Sci. Data

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Abstract. Land evapotranspiration (ET) plays a crucial role in Earth's water–carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land–atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits promising performance across various vegetation coverage types, as validated against in situ observations. The evaluation process yielded Pearson correlation coefficients (R) of 0.63 and 0.65, root-mean-square errors (RMSEs) of 0.81 and 0.73 mm d−1, unbiased root-mean-square errors (ubRMSEs) of 1.20 and 1.04 mm d−1, mean absolute errors (MAEs) of 0.81 and 0.73 mm d−1, and Kling–Gupta efficiencies (KGEs) of 0.60 and 0.65 on average at resolutions of 0.1 and 0.25°, respectively. In addition, comparisons indicate that CAMELE can effectively characterize the multiyear linear trend, mean average, and extreme values of ET. However, it exhibits a tendency to overestimate seasonality. In summary, we propose a reliable set of ET data that can aid in understanding the variations in the water cycle and has the potential to serve as a benchmark for various applications. The dataset is publicly available at https://doi.org/10.5281/zenodo.8047038 (Li et al., 2023b).

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“…Collocation methods have found widespread application in assessing various geophysical variables, encompassing soil moisture (Deng et al, 2023;Ming et al, 2022), precipitation (Dong et al, 2022;, ocean wind speed (Ribal & Young, 2020;Vogelzang et al, 2022), leaf area index (Jiang et al, 2017), total water storage (Yin & Park, 2021) sea ice thickness and surface salinity (Hoareau et al, 2018), and near-surface air temperature (Sun et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A harmonized global gridded transpiration product based on collocation analysis

Li,

Han,

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

Transpiration (T) is pivotal in the global water cycle, responding to soil moisture, atmospheric stress, climate changes, and human impacts. Therefore, establishing a reliable global transpiration dataset is essential. Different global transpiration products exhibit significant differences, necessitating the evaluation of errors. Collocation analysis methods have been proven effective for assessing the errors in these products, which can subsequently be used for multisource fusion. However, previous results did not consider error cross-correlation, rendering the results less reliable. In this study, we employ collocation analysis, taking error cross-correlation into account, to effectively analyze the errors in multiple transpiration products and merge them to obtain a more reliable dataset. The results demonstrate its superior reliability. The outcome of this research is a long-term daily global transpiration dataset at 0.1° resolution from 2000 to 2020. Using the transpiration after partitioning at FLUXNET sites as a reference, we compare the performance of the merged product with input datasets. The merged dataset performs well across various vegetation types and is validated against in-situ observations. Incorporating non-zero ECC considerations represents a significant theoretical and proven enhancement over previous methodologies that neglected such conditions, highlighting its reliability in enhancing our understanding of transpiration dynamics in a changing world.

show abstract

Triple Collocation Analysis and In Situ Validation of the CYGNSS Soil Moisture Product

Cited by 8 publications

References 69 publications

A harmonized global gridded transpiration product based on collocation analysis

A harmonized global gridded transpiration product based on collocation analysis

CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data

A harmonized global gridded transpiration product based on collocation analysis

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