Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2849-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2849-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
24 Jun 2025
Data description paper |
| 24 Jun 2025
| 24 Jun 2025
A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021
Die Hu
School of Geodesy and Geomatics, Wuhan University, Wuhan, China
Yuan Wang
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Han Jing
Energy Administration of Yunnan Province, Yunnan, China
Linwei Yue
School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Qiang Zhang
Center of Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian, China
Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China
School of Geodesy and Geomatics, Wuhan University, Wuhan, China
Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, China
Key Laboratory of Polar Environment Monitoring and Public Governance, Ministry of Education, Wuhan University, Wuhan, China
Huanfeng Shen
School of Resource and Environmental Science, Wuhan University, Wuhan, China
Liangpei Zhang
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China
Related authors
No articles found.
Mofan Cheng, Zhuohong Li, Linxin Li, Wei He, Liangpei Zhang, and Hongyan Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-807, https://doi.org/10.5194/essd-2025-807, 2026
Preprint under review for ESSD
Short summary
Short summary
This study presents a quarterly land-cover and soil erosion dataset for the Loess Plateau from 2000 to 2024 with 100 time steps, achieving an overall accuracy of 81.44 % based on 40,000 annotated samples and a mean absolute error of 4.50 % relative to government survey data. The maps show forest expansion, cropland expansion, and bare land reduction, together with a 30 % decline in mean soil erosion.
Daju Wang, Peiyang Ren, Xiaosheng Xia, Lei Fan, Zhangcai Qin, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 16, 2465–2481, https://doi.org/10.5194/essd-16-2465-2024, https://doi.org/10.5194/essd-16-2465-2024, 2024
Short summary
Short summary
This study generated a high-precision dataset, locating forest harvested carbon and quantifying post-harvest wood emissions for various uses. It enhances our understanding of forest harvesting and post-harvest carbon dynamics in China, providing essential data for estimating the forest ecosystem carbon budget and emphasizing wood utilization's impact on carbon emissions.
Qinxin Zhao, Qinghua Xie, Xing Peng, Yusong Bao, Tonglu Jia, Linwei Yue, Haiqiang Fu, and Jianjun Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-2024, 903–908, https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-903-2024, https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-903-2024, 2024
Xiaobin Guan, Zhihao Sun, Dong Chu, Guanglei Xie, Yuchen Wang, and Huanfeng Shen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-465, https://doi.org/10.5194/essd-2023-465, 2023
Manuscript not accepted for further review
Short summary
Short summary
Although there are various XCO2 products, they are all limited by the spatial resolution or spatiotemporal coverage. In this study, the first global 0.05° XCO2 product (GCXCO2) for 21 years is generated by combining the OCO-2 satellite observations and models simulations. The dynamic normalization strategy is applied to enhance the temporal expansibility of stacking learning model, and the product is superior than the model simulations showing similar characteristic with OCO-2 observations.
Yonghong Zheng, Huanfeng Shen, Rory Abernethy, and Rob Wilson
Biogeosciences, 20, 3481–3490, https://doi.org/10.5194/bg-20-3481-2023, https://doi.org/10.5194/bg-20-3481-2023, 2023
Short summary
Short summary
Investigations in central and western China show that tree ring inverted latewood intensity expresses a strong positive relationship with growing-season temperatures, indicating exciting potential for regions south of 30° N that are traditionally not targeted for temperature reconstructions. Earlywood BI also shows good potential to reconstruct hydroclimate parameters in some humid areas and will enhance ring-width-based hydroclimate reconstructions in the future.
Yuan Wang, Qiangqiang Yuan, Tongwen Li, Yuanjian Yang, Siqin Zhou, and Liangpei Zhang
Earth Syst. Sci. Data, 15, 3597–3622, https://doi.org/10.5194/essd-15-3597-2023, https://doi.org/10.5194/essd-15-3597-2023, 2023
Short summary
Short summary
We propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless global XCO2 and XCH4 products. Results show that the proposed method achieves a satisfactory accuracy that distinctly exceeds that of CAMS-EGG4 and is superior or close to those of GOSAT and OCO-2. In particular, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission for COVID-19.
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154, https://doi.org/10.5194/gmd-16-4137-2023, https://doi.org/10.5194/gmd-16-4137-2023, 2023
Short summary
Short summary
The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Qinghai Song, Yanjun Du, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yunpeng Wang, and Yongxian Su
Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, https://doi.org/10.5194/essd-15-2601-2023, 2023
Short summary
Short summary
We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
Qiang Zhang, Qiangqiang Yuan, Taoyong Jin, Meiping Song, and Fujun Sun
Earth Syst. Sci. Data, 14, 4473–4488, https://doi.org/10.5194/essd-14-4473-2022, https://doi.org/10.5194/essd-14-4473-2022, 2022
Short summary
Short summary
Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0 enlarges the temporal scope from 2002 to 2022. By fusing auxiliary precipitation information with the long short-term memory convolutional neural network (LSTM-CNN) model, SGD-SM 2.0 can consider sudden extreme weather conditions for 1 d in global daily soil moisture products and is significant for full-coverage global daily hydrologic monitoring, rather than averaging monthly–quarterly–yearly results.
Yinghong Jing, Xinghua Li, and Huanfeng Shen
Earth Syst. Sci. Data, 14, 3137–3156, https://doi.org/10.5194/essd-14-3137-2022, https://doi.org/10.5194/essd-14-3137-2022, 2022
Short summary
Short summary
Snow variation is a vital factor in global climate change. Satellite-based approaches are effective for large-scale environmental monitoring. Nevertheless, the high cloud fraction seriously impedes the remote-sensed investigation. Therefore, a recent 20-year cloud-free snow cover collection in China is generated for the first time. This collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues.
Y. Tao, W. Huang, W. Gan, and H. Shen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 209–215, https://doi.org/10.5194/isprs-annals-V-3-2022-209-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-209-2022, 2022
Xiaobin Guan, Huanfeng Shen, Yuchen Wang, Dong Chu, Xinghua Li, Linwei Yue, Xinxin Liu, and Liangpei Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-156, https://doi.org/10.5194/essd-2021-156, 2021
Preprint withdrawn
Short summary
Short summary
This study generated the first global 1-km continuous NDVI product (STFLNDVI) for 4-decades by fusing multi-source satellite products. Simulated and real-data assessments confirmed the satisfactory and stable accuracy of STFLNDVI regarding spatial details and temporal variations. STFLNDVI is an ideal solution to the trade-off between spatial resolution and time coverage in current NDVI products, which of great significance for long-term regional and global vegetation and climate change studies.
Qiang Zhang, Qiangqiang Yuan, Jie Li, Yuan Wang, Fujun Sun, and Liangpei Zhang
Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, https://doi.org/10.5194/essd-13-1385-2021, 2021
Short summary
Short summary
Acquired daily soil moisture products are always incomplete globally (just about 30 %–80 % coverage ratio) due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we generate long-term seamless global daily (SGD) AMSR2 soil moisture productions from 2013 to 2019. These productions are significant for full-coverage global daily hydrologic monitoring, rather than averaging as the monthly–quarter–yearly results.
Cited articles
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017. a
Belgiu, M. and Stein, A.: Spatiotemporal image fusion in remote sensing, Remote Sens., 11, 818, https://doi.org/10.3390/rs11070818, 2019. a
Brandt, M., Wigneron, J.-P., Chave, J., Tagesson, T., Penuelas, J., Ciais, P., Rasmussen, K., Tian, F., Mbow, C., Al-Yaari, A., Rodriguez-Fernandez, N., Schurgers, G., Zhang, W., Chang, J., Kerr, Y., Verger, A., Tucker, C., Mialon, A., Rasmussen, L. V., Fan, L., and Fensholt, R.: Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands, Nat. Ecol. Evol., 2, 827–835, 2018. a
Buades, A., Coll, B., and Morel, J.-M.: A non-local algorithm for image denoising, in: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), vol. 2, 60–65, Ieee, https://doi.org/10.1109/CVPR.2005.38, 2005a. a
Buades, A., Coll, B., and Morel, J.-M.: A review of image denoising algorithms, with a new one, Multiscale Model. Sim., 4, 490–530, 2005b. a
Chen, J. M. and Black, T. A.: Defining leaf area index for non-flat leaves, Plant Cell Environ., 15, 421–429, 1992. a
Cheng, Q., Liu, H., Shen, H., Wu, P., and Zhang, L.: A spatial and temporal nonlocal filter-based data fusion method, IEEE T. Geosci. Remote Sens., 55, 4476–4488, 2017. a
Crow, W. T., Chan, S. T. K., Entekhabi, D., Houser, P. R., Hsu, A. Y., Jackson, T. J., Njoku, E. G., O'Neill, P. E., Shi, J., and Zhan, X.: An observing system simulation experiment for Hydros radiometer-only soil moisture products, IEEE T. Geosci. Remote Sens., 43, 1289–1303, 2005. a
Cui, Q., Shi, J., Du, J., Zhao, T., and Xiong, C.: An approach for monitoring global vegetation based on multiangular observations from SMOS, IEEE J. Sel. Top. Appl., 8, 604–616, 2015. a
Cui, T., Fan, L., Ciais, P., Fensholt, R., Frappart, F., Sitch, S., Chave, J., Chang, Z., Li, X., Wang, M., Liu, X., Ma, M., and Wigneron, J.-P.: First assessment of optical and microwave remotely sensed vegetation proxies in monitoring aboveground carbon in tropical Asia, Remote Sens. Environ., 293, 113619, https://doi.org/10.1016/j.rse.2023.113619, 2023. a
Didan, K.: MODIS/Aqua Vegetation Indices 16-Day L3 Global 0.05Deg CMG V061, EarthData [data set], https://doi.org/10.5067/MODIS/MYD13C1.061, 2021. a
Dou, Y., Tian, F., Wigneron, J.-P., Tagesson, T., Du, J., Brandt, M., Liu, Y., Zou, L., Kimball, J. S., and Fensholt, R.: Reliability of using vegetation optical depth for estimating decadal and interannual carbon dynamics, Remote Sens. Environ., 285, 113390, https://doi.org/10.1016/j.rse.2022.113390, 2023. a
Entekhabi, D., Njoku, E. G., O’Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman, S. W., Tsang, L., and Van Zyl, J.: The soil moisture active passive (SMAP) mission, Proc. IEEE, 98, 704–716, 2010. a
Fan, L., Wigneron, J.-P., Ciais, P., Chave, J., Brandt, M., Fensholt, R., Saatchi, S. S., Bastos, A., Al-Yaari, A., Hufkens, K., Qin, Y., Xiao, X., Chen, C., Myneni, R. B., Fernandez-Moran, R., Mialon, A., Rodriguez-Fernandez, N. J., Kerr, Y., Tian, F., and Peñuelas, J.: Satellite-observed pantropical carbon dynamics, Nat. Plants, 5, 944–951, 2019. a
Fan, L., Wigneron, J.-P., Ciais, P., Chave, J., Brandt, M., Sitch, S., Yue, C., Bastos, A., Li, X., Qin, Y., Yuan, W., Schepaschenko, D., Mukhortova, L., Li, X., Liu, X., Wang, M., Frappart, F., Xiao, X., Chen, J., Ma, M., Wen, J., Chen, X., Yang, H., van Wees, D., and Fensholt, R.: Siberian carbon sink reduced by forest disturbances, Nat. Geosci., 16, 56–62, 2023. a
Fanelli, A., Leo, A., and Ferri, M.: Remote sensing images data fusion: a wavelet transform approach for urban analysis, in: IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (Cat. No. 01EX482), 112–116, IEEE, https://doi.org/10.1109/DFUA.2001.985737, 2001. a
Feldman, A., Konings, A., Piles, M., and Entekhabi, D.: The Multi-Temporal Dual Channel Algorithm (MT-DCA) (Version 5), Zenodo [data set], https://doi.org/10.5281/zenodo.5619583, 2021. a
Feldman, A. F. and Entekhabi, D.: Smap vegetation optical depth retrievals using the multi-temporal dual-channel algorithm, in: IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, 5437–5440, 28 July–2 August 2019, Yokohama, Japan, IEEE, https://doi.org/10.1109/IGARSS.2019.8899014, 2019. a
Fernandez-Moran, R., Al-Yaari, A., Mialon, A., Mahmoodi, A., Al Bitar, A., De Lannoy, G., Rodriguez-Fernandez, N., Lopez-Baeza, E., Kerr, Y., and Wigneron, J.-P.: SMOS-IC: An alternative SMOS soil moisture and vegetation optical depth product, Remote Sens., 9, 457, https://doi.org/10.3390/rs9050457, 2017. a, b
Ferrazzoli, P., Guerriero, L., and Wigneron, J.-P.: Simulating L-band emission of forests in view of future satellite applications, IEEE T. Geosci. Remote Sens., 40, 2700–2708, 2002. a
Frappart, F., Wigneron, J.-P., Li, X., Liu, X., Al-Yaari, A., Fan, L., Wang, M., Moisy, C., Le Masson, E., Aoulad Lafkih, Z., Vallé, C., Ygorra, B., and Baghdadi, N.: Global monitoring of the vegetation dynamics from the Vegetation Optical Depth (VOD): A review, Remote Sens., 12, 2915, https://doi.org/10.3390/rs12182915, 2020. a, b
Friedl, M. and Sulla-Menashe, D.: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V061, EarthData [data set], https://doi.org/10.5067/MODIS/MCD12C1.061, 2022. a
Gao, F., Masek, J., Schwaller, M., and Hall, F.: On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance, IEEE T. Geosci. Remote Sens., 44, 2207–2218, 2006. a
Garcia, D.: Robust smoothing of gridded data in one and higher dimensions with missing values, Comput. Stat. Data An., 54, 1167–1178, 2010. a
Gharbia, R., Azar, A. T., Baz, A. E., and Hassanien, A. E.: Image fusion techniques in remote sensing, arXiv [preprint], https://doi.org/10.48550/arXiv.1403.5473, 2014. a
Gilboa, G. and Osher, S.: Nonlocal operators with applications to image processing, Multiscale Model. Sim., 7, 1005–1028, 2009. a
Hansen, M. C., DeFries, R. S., Townshend, J. R., and Sohlberg, R.: Global land cover classification at 1 km spatial resolution using a classification tree approach, Int. J. Remote Sens., 21, 1331–1364, 2000. a
Holtzman, N. M., Anderegg, L. D. L., Kraatz, S., Mavrovic, A., Sonnentag, O., Pappas, C., Cosh, M. H., Langlois, A., Lakhankar, T., Tesser, D., Steiner, N., Colliander, A., Roy, A., and Konings, A. G.: L-band vegetation optical depth as an indicator of plant water potential in a temperate deciduous forest stand, Biogeosciences, 18, 739–753, https://doi.org/10.5194/bg-18-739-2021, 2021. a
Hongtao, J., Huanfeng, S., Xinghua, L., Chao, Z., Huiqin, L., and Fangni, L.: Extending the SMAP 9-km soil moisture product using a spatio-temporal fusion model, Remote Sens. Environ., 231, 111224, https://doi.org/10.1016/j.rse.2019.111224, 2019. a
Hu, D., Wang, Y., Jing, H., Yue, L., Zhang, Q., Yuan, Q., Fan, L., Shen, H., and Zhang, L.: A global daily seamless 9-km Vegetation Optical Depth (VOD) product from 2010 to 2021, Zenodo [data set], https://doi.org/10.5281/zenodo.13334757, 2024. a, b
Jackson, T. J.: III. Measuring surface soil moisture using passive microwave remote sensing, Hydrol. Process., 7, 139–152, 1993. a
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Martinuzzi, J., Font, J., and Berger, M.: Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission, IEEE T. Geosci. Remote Sens., 39, 1729–1735, 2001. a
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M.-J., Font, J., Reul, N., Gruhier, C., Juglea, S. E., Drinkwater, M. R., Hahne, A., Martin-Neira, M., and Mecklenburg, S.: The SMOS mission: New tool for monitoring key elements ofthe global water cycle, Proc. IEEE, 98, 666–687, 2010. a
Kerr, Y. H., Waldteufel, P., Richaume, P., Wigneron, J.-P., Ferrazzoli, P., Mahmoodi, A., Al Bitar, A., Cabot, F., Gruhier, C., Juglea, S. E., Leroux, D., Mialon, A., and Delwart, S.: The SMOS soil moisture retrieval algorithm, IEEE T. Geosci. Remote Sens., 50, 1384–1403, 2012. a
Konings, A. G., Piles, M., Das, N., and Entekhabi, D.: L-band vegetation optical depth and effective scattering albedo estimation from SMAP, Remote Sens. Environ., 198, 460–470, 2017. a
Kumar, S. V., Holmes, T. R., Andela, N., Dharssi, I., Vinodkumar, H., Hain, C., Peters-Lidard, C. D., Mahanama, S. P., Arsenault, K. R., Nie, W., and Getirana, A.: The 2019–2020 Australian drought and bushfires altered the partitioning of hydrological fluxes, Geophys. Res. Lett., 48, e2020GL091411, https://doi.org/10.1029/2020GL091411, 2021. a
Lawrence, H., Wigneron, J.-P., Richaume, P., Novello, N., Grant, J., Mialon, A., Al Bitar, A., Merlin, O., Guyon, D., Leroux, D., Bircher, S., and Kerr, Y.: Comparison between SMOS Vegetation Optical Depth products and MODIS vegetation indices over crop zones of the USA, Remote Sensing of Environment, 140, 396–406, 2014. a
Le Vine, D. M., Lagerloef, G. S., and Torrusio, S. E.: Aquarius and remote sensing of sea surface salinity from space, Proc. IEEE, 98, 688–703, 2010. a
Li, X., Wigneron, J.-P., Frappart, F., Fan, L., Wang, M., Liu, X., Al-Yaari, A., and Moisy, C.: Development and validation of the SMOS-IC version 2 (V2) soil moisture product, in: IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, 4434–4437, 26 September–2 October 2020, Waikoloa, Hawaii, USA, IEEE, https://doi.org/10.1109/IGARSS39084.2020.9323324, 2020. a
Li, X., Wigneron, J.-P., Frappart, F., Fan, L., Ciais, P., Fensholt, R., Entekhabi, D., Brandt, M., Konings, A. G., Liu, X., Wang, M., Al-Yaari, A., and Moisy, C.: Global-scale assessment and inter-comparison of recently developed/reprocessed microwave satellite vegetation optical depth products, Remote Sens. Environ., 253, 112208, https://doi.org/10.1016/j.rse.2020.112208, 2021. a
Li, X., Wigneron, J.-P., Fan, L., Frappart, F., Yueh, S. H., Colliander, A., Ebtehaj, A., Gao, L., Fernandez-Moran, R., Liu, X., Wang, M., Ma, H., Moisy, C., and Ciais, P.: A new SMAP soil moisture and vegetation optical depth product (SMAP-IB): Algorithm, assessment and inter-comparison, Remote Sens. Environ., 271, 112921, https://doi.org/10.1016/j.rse.2022.112921, 2022a. a, b
Li, X., Wigneron, J.-P., Frappart, F., De Lannoy, G., Fan, L., Zhao, T., Gao, L., Tao, S., Ma, H., Peng, Z., Liu, X., Wang, H., Wang, M., Moisy, C., and Ciais, P.: The first global soil moisture and vegetation optical depth product retrieved from fused SMOS and SMAP L-band observations, Remote Sensi. Environ., 282, 113272, https://doi.org/10.1016/j.rse.2022.113272, 2022b. a
Liu, Y. Y., Dorigo, W. A., Parinussa, R., de Jeu, R. A., Wagner, W., McCabe, M. F., Evans, J., and Van Dijk, A.: Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sens. Environ., 123, 280–297, 2012. a
Llamas, R. M., Guevara, M., Rorabaugh, D., Taufer, M., and Vargas, R.: Spatial gap-filling of ESA CCI satellite-derived soil moisture based on geostatistical techniques and multiple regression, Remote Sens., 12, 665, https://doi.org/10.3390/rs12040665, 2020. a
Loveland, T. R., Zhu, Z., Ohlen, D. O., Brown, J. F., Reed, B. C., and Yang, L.: An analysis of the IGBP global land-cover characterization process, Photogramm. Eng. Rem. S., 65, 1021–1032, 1999. a
Mialon, A., Rodríguez-Fernández, N. J., Santoro, M., Saatchi, S., Mermoz, S., Bousquet, E., and Kerr, Y. H.: Evaluation of the sensitivity of SMOS L-VOD to forest above-ground biomass at global scale, Remote Sens., 12, 1450, https://doi.org/10.3390/rs12091450, 2020. a
Mo, T., Choudhury, B., Schmugge, T., Wang, J. R., and Jackson, T.: A model for microwave emission from vegetation-covered fields, J. Geophys. Res.-Oceans, 87, 11229–11237, 1982. a
Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA), Earth Syst. Sci. Data, 12, 177–196, https://doi.org/10.5194/essd-12-177-2020, 2020. a
Moesinger, L., Zotta, R.-M., van der Schalie, R., Scanlon, T., de Jeu, R., and Dorigo, W.: Monitoring vegetation condition using microwave remote sensing: the standardized vegetation optical depth index (SVODI), Biogeosciences, 19, 5107–5123, https://doi.org/10.5194/bg-19-5107-2022, 2022. a
Myneni, R., Knyazikhin, Y., and Park, T.: MOD15A2H MODIS/Terra leaf area Index/FPAR 8-Day L4 global 500m SIN grid V006, NASA EOSDIS Land Processes Distributed Active Archive Center, https://doi.org/10.5067/modis/mod15a2h.006, 2015. a
Njoku, E. G., Jackson, T. J., Lakshmi, V., Chan, T. K., and Nghiem, S. V.: Soil moisture retrieval from AMSR-E, IEEE T. Geosci. Remote Sens., 41, 215–229, 2003. a
Olivares-Cabello, C., Chaparro, D., Vall-llossera, M., Camps, A., and López-Martínez, C.: Global unsupervised assessment of multifrequency vegetation optical depth sensitivity to vegetation cover, IEEE J. Sel. Top. Appl. Earth Obs., 16, 538–552, 2022. a
O'Neill, P., Bindlish, R., Chan, S., Njoku, E., and Jackson, T.: Algorithm theoretical basis document. Level 2 & 3 soil moisture (passive) data products, Jet Propulsion Laboratory, California Institute of Technology, https://nsidc.org/sites/default/files/l2_sm_p_atbd_rev_d_jun2018_auto_toc.pdf (last access: 20 June 2025), 2018. a
Su, X., Deledalle, C.-A., Tupin, F., and Sun, H.: Two steps multi-temporal non-local means for SAR images, in: 2012 IEEE International Geoscience and Remote Sensing Symposium, 2008–2011, 22–27 July 2012, Munich, Germany, IEEE, https://doi.org/10.1109/IGARSS.2012.6351106, 2012. a
Unterholzner, T.: VODCA2AGB-A novel approach for the estimation of global AGB stocks based on vegetation optical depth data and random forest regression, PhD thesis, Technische Universität Wien, https://doi.org/10.34726/hss.2023.64368, 2023. a
Vaglio Laurin, G., Vittucci, C., Tramontana, G., Ferrazzoli, P., Guerriero, L., and Papale, D.: Monitoring tropical forests under a functional perspective with satellite-based vegetation optical depth, Glob. Change Biol., 26, 3402–3416, 2020. a
Van Dijk, A. I., Beck, H. E., Crosbie, R. S., De Jeu, R. A., Liu, Y. Y., Podger, G. M., Timbal, B., and Viney, N. R.: The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society, Water Resour. Res., 49, 1040–1057, 2013. a
Vreugdenhil, M., Greimeister-Pfeil, I., Preimesberger, W., Camici, S., Dorigo, W., Enenkel, M., van der Schalie, R., Steele-Dunne, S., and Wagner, W.: Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges, Front. Water, 4, 1045451, https://doi.org/10.3389/frwa.2022.1045451, 2022. a
Wang, G., Garcia, D., Liu, Y., De Jeu, R., and Dolman, A. J.: A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations, Environ. Modell. Softw., 30, 139–142, 2012. a
Wang, Y., Yuan, Q., Li, T., Yang, Y., Zhou, S., and Zhang, L.: Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method, Earth Syst. Sci. Data, 15, 3597–3622, https://doi.org/10.5194/essd-15-3597-2023, 2023. a
Wigneron, J.-P., Kerr, Y. H., Waldteufel, P., Saleh, K., Escorihuela, M.-J., Richaume, P., Ferrazzoli, P., de Rosnay, P., Gurney, R., Calvet, J.-C., Grant, J.-P., Guglielmetti, M., Hornbuckle, B., Mätzler, C., Pellarin, T., and Schwank, M.: L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields, Remote Sens. Environ., 107, 639–655, 2007. a
Wigneron, J.-P., Jackson, T. J., O'Neill, P., De Lannoy, G., de Rosnay, P., Walker, J. P., Ferrazzoli, P., Mironov, V., Bircher, S., Grant, J. P., Kurum, M., Schwank, M., Munoz-Sabater, J., Das, N., Royer, A., Al-Yaari, A., Al Bitar, A., Fernandez-Moran, R., Lawrence, H., Mialon, A., Parrens, M., Richaume, P., Delwart, S., and Kerr, Y.: Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms, Remote Sens. Environ., 192, 238–262, 2017. a, b
Wigneron, J.-P., Fan, L., Ciais, P., Bastos, A., Brandt, M., Chave, J., Saatchi, S., Baccini, A., and Fensholt, R.: Tropical forests did not recover from the strong 2015–2016 El Niño event, Sci. Adv., 6, eaay4603, https://doi.org/10.1126/sciadv.aay4603, 2020. a
Wigneron, J.-P., Li, X., Frappart, F., Fan, L., Al-Yaari, A., De Lannoy, G., Liu, X., Wang, M., Le Masson, E., and Moisy, C.: SMOS-IC data record of soil moisture and L-VOD: historical development, applications and perspectives, Remote Sens. Environ., 254, 112238, https://doi.org/10.1016/j.rse.2020.112238, 2021. a, b, c
Wild, B., Teubner, I., Moesinger, L., Zotta, R.-M., Forkel, M., van der Schalie, R., Sitch, S., and Dorigo, W.: VODCA2GPP – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing, Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, 2022. a
Yang, H. and Wang, Q.: Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021, J. Hydrol., 621, 129579, https://doi.org/10.1016/j.jhydrol.2023.129579, 2023. a
Yao, Y., Humphrey, V., Konings, A. G., Wang, Y., Yin, Y., Holtzman, N., Wood, J. D., Bar-On, Y., and Frankenberg, C.: Investigating diurnal and seasonal cycles of Vegetation Optical Depth retrieved from GNSS signals in a broadleaf forest, Geophys. Res. Lett., 51, e2023GL107121, https://doi.org/10.1029/2023GL107121, 2024. a
Zhang, H., Hagan, D. F. T., Dalagnol, R., and Liu, Y.: Forest canopy changes in the southern Amazon during the 2019 fire season based on passive microwave and optical satellite observations, Remote Sensing, 13, 2238, 2021a. a
Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021b. a
Zhang, Q., Yuan, Q., Jin, T., Song, M., and Sun, F.: SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022, Earth Syst. Sci. Data, 14, 4473–4488, https://doi.org/10.5194/essd-14-4473-2022, 2022. a
Zhao, T., Shi, J., Entekhabi, D., Jackson, T. J., Hu, L., Peng, Z., Yao, P., Li, S., and Kang, C. S.: Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm, Remote Sens. Environ., 257, 112321, https://doi.org/10.1016/j.rse.2021.112321, 2021. a
Zhong, S., Fan, L., De Lannoy, G., Frappart, F., Zeng, J., Vreugdenhil, M., Peng, J., Liu, X., Xing, Z., Wang, M., Li, X., Wang, H., and Wigneron, J.-P.: Quantitative assessment of various proxies for downscaling coarse-resolution VOD products over the contiguous United States, Int. J. Appl. Earth Obs., 130, 103910, https://doi.org/10.1016/j.jag.2024.103910, 2024. a
Zhu, X., Cai, F., Tian, J., and Williams, T. K.-A.: Spatiotemporal fusion of multisource remote sensing data: Literature survey, taxonomy, principles, applications, and future directions, Remote Sens., 10, 527, https://doi.org/10.3390/rs10040527, 2018. a
Zotta, R.-M., Moesinger, L., van der Schalie, R., Vreugdenhil, M., Preimesberger, W., Frederikse, T., de Jeu, R., and Dorigo, W.: VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring, Earth Syst. Sci. Data, 16, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024, 2024. a
Short summary
Existing L-band vegetation optical depth (L-VOD) products suffer from data gaps and coarse resolution of historical data. Therefore, it is necessary to integrate multi-temporal and multisource L-VOD products. Our study begins with the reconstruction of missing data and then develops a spatiotemporal fusion model to generate global daily seamless 9 km L-VOD products from 2010 to 2021, which are crucial for understanding the global carbon cycle.
Existing L-band vegetation optical depth (L-VOD) products suffer from data gaps and coarse...
Altmetrics
Final-revised paper
Preprint