Daily all weather surface soil moisture data set with 1 km resolution in China (2003-2019)

Surface soil moisture (SSM) is a crucial parameter for understanding the hydrological process of our earth surface. Passive microwave (PM) technique has long been the primary choice for estimating SSM at satellite remote sensing scales, while on the other hand, 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 public that meet the qualification of 1-km resolution and daily revisit cycles under all-weather conditions. In this study, therefore, we have developed one such SSM product in China with all these characteristics. The product was generated through downscaling of AMSR-E and AMSR-2 based SSM at 36-km, covering all on-orbit time of the two radiometers during 2003-2019. MODIS optical reflectance data and daily thermal infrared land surface temperature (LST) that have been gap-filled for cloudy conditions were the primary data inputs of the downscaling model, in order to achieve the “all-weather” quality for the SSM downscaling outcome. Daily images from this developed SSM product have achieved 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. We evaluated the product against in situ soil moisture measurements from over 2000 professional meteorological and soil moisture observation stations, and found the accuracy of the product is stable for all weathers from clear sky to cloudy conditions, with station averages of the unbiased RMSE ranging from 0.053 vol to 0.056 vol. Moreover, the evaluation results also show that the developed product distinctly outperforms the widely known SMAP-Sentinel (Active-Passive microwave) combined SSM product at 1-km resolution. This indicates potential important benefits that can be brought by our developed product, on improvement of futural investigations related to hydrological processes, agricultural industry, water resource and environment management.

地表土壤水分（SSM）是理解地球表面水文过程的关键参数。被动微波（PM）技术在卫星遥感尺度上估算SSM方面一直占据主导地位，然而，PM观测的粗分辨率（通常大于10公里）限制了其在更小尺度上的应用。尽管已有定量研究提出对基于卫星PM的SSM进行降尺度，但符合1公里分辨率和全天候每日重访周期的SSM产品极为罕见。因此，本研究在中国开发了一款具备上述所有特性的SSM产品。该产品通过将基于AMSR-E和AMSR-2的36公里分辨率SSM进行降尺度生成，覆盖了2003年至2019年两个辐射计在轨运行的全部时间。降尺度模型的主要数据输入为MODIS光学反照率数据和已填充云层缺失的每日热红外地表温度（LST），旨在实现SSM降尺度结果的“全天候”质量。该SSM产品在四月至九月期间实现了全国范围内的近乎全覆盖。对于其他月份，开发产品的国家覆盖率相较于原始每日PM观测也得到了显著提升。我们通过超过2000个专业气象和土壤水分观测站的实地土壤水分测量数据对该产品进行了评估，发现其在晴朗至多云等各种天气条件下均表现出稳定的准确性，无偏RMSE的平均站均值为0.053至0.056体积比。此外，评估结果还显示，该开发产品在1公里分辨率上明显优于广为人知的SMAP-Sentinel（主动-被动微波）组合SSM产品。这表明，我们的开发产品在改进未来与水文过程、农业产业、水资源和环境管理相关的研究方面具有潜在的重要益处。

搜集汇总

背景与挑战

背景概述

该数据集是中国2003-2019年每日全天候地表土壤湿度产品，分辨率为1公里，通过降尺度被动微波观测数据生成，结合MODIS光学和热红外数据以实现全天候覆盖。数据集在4月至9月期间全国覆盖率接近完整，其他月份覆盖率也显著提升，评估显示其精度稳定且优于同类产品，适用于水文、农业和环境管理研究。