A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019
A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019
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DOI：
10.5194/essd-14-2613-2022
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发表时间：
2022-06
影响因子：
11.4
通讯作者：
P. Song;Yongqiang Zhang;Jianping Guo;Jiancheng Shi;T. Zhao;B. Tong
中科院分区：
文献类型：
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作者：
P. Song;Yongqiang Zhang;Jianping Guo;Jiancheng Shi;T. Zhao;B. Tong
抽象。表层土壤水分（SSM）是了解地球表面水文过程的关键。被动微波（PM）技术一直是从卫星角度估计全球SSM的主要工具，但PM观测的粗分辨率（通常> 10 km）阻碍了其在更精细尺度上的应用。虽然已经提出了对缩小基于PM的卫星SSM进行定量研究的建议，但向公众提供的满足1公里分辨率和全天候条件下每日重访周期要求的产品很少。在这项研究中，我们开发了一个这样的SSM产品在中国的所有这些特点。该产品是通过缩小36公里处基于AMSR-E/AMSR-2（地球观测系统高级微波扫描辐射计及其后继者）的SSM的尺度而生成的，涵盖了2003 - 2019年期间两个辐射计的所有在轨时间。中分辨率成像分光仪的光学反射率数据和每日热红外陆地表面温度（LST），已填补了多云条件下的差距是降尺度模型的主要数据输入，以便实现"全天候"的质量为1公里的SSM。从这个开发的SSM产品的每日图像有准完整的覆盖全国在4月至9月期间。对于其他月份，通过专门开发的子模型，在降尺度过程中填充相邻PM条带接缝之间的差距，所开发产品的全国覆盖百分比相对于原始每日PM观测也大大提高。该产品与来自2000多个气象站的原位土壤水分测量结果进行了很好的比较，由无偏均方根差（RMSD）的站平均值表示，范围从0.052到0.059 vol-1。此外，评估结果还表明，所开发的产品优于SMAP（土壤水分主动被动）和哨兵（主被动微波）组合SSM产品在1公里，与0.55的相关系数达到0.40后，后者的产品。这表明新产品具有很大的潜力，可用于水文界，农业，水资源和环境管理。新产品可在www.example.com下载（Song和Zhang，2021 b）。
Abstract. Surface soil moisture (SSM) is crucial for understanding the hydrological process of our earth surface. The passive microwave (PM) technique has long been the primary tool for estimating global SSM from the view of satellites, while the coarse resolution (usually >∼10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to the public that meet the qualification of 1 km resolution and daily revisit cycles under all-weather conditions. In this study, we developed one such SSM product in China with all these characteristics. The product was generated through downscaling the AMSR-E/AMSR-2-based (Advance Microwave Scanning Radiometer of the Earth Observing System and its successor) SSM at 36 km, covering all on-orbit times of the two radiometers during 2003–2019. MODIS optical reflectance data and daily thermal-infrared land surface temperature (LST) that had been gap-filled for cloudy conditions were the primary data inputs of the downscaling model so that the “all-weather” quality was achieved for the 1 km SSM. Daily images from this developed SSM product have quasi-complete coverage over the country during April–September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations through a specifically developed sub-model for filling the gap between seams of neighboring PM swaths during the downscaling procedure. The product compares well against in situ soil moisture measurements from 2000+ meteorological stations, indicated by station averages of the unbiased root mean square difference (RMSD) ranging from 0.052 to 0.059 vol vol−1. Moreover, the evaluation results also show that the developed product outperforms the SMAP (Soil Moisture Active Passive) and Sentinel (active–passive microwave) combined SSM product at 1 km, with a correlation coefficient of 0.55 achieved against that of 0.40 for the latter product. This indicates the new product has great potential to be used by the hydrological community, by the agricultural industry, and for water resource and environment management. The new product is available for download at https://doi.org/10.11888/Hydro.tpdc.271762 (Song and Zhang, 2021b).