Abstract The low-resolution characteristic of passive microwave surface soil moisture (SSM) products greatly limits their application in many fields at regional or local scale. Aiming to overcome this limitation, a random forest (RF)-based downscaling approach was proposed in this study to disaggregate the Soil Moisture Active and Passive (SMAP) SSM product with the synergistic use of the Optical/Thermal infrared (TIR) observations from the Moderate-Resolution Imaging Spectro-radiometer (MODIS) onboard the Terra and Aqua satellites. The Iberian Peninsula was selected as the study area during the period from 2015 to 2016. First, the performance of the RF-based approach in building the SSM relationship model with surface variables (surface temperature, vegetation index, leaf area index, albedo, water index, solar factor, and elevation) was compared with that resulting from a widely used polynomial-based relationship model. Good agreement was achieved for the RF-based method with a correlation coefficient (R) above 0.95 and a mean root-mean-square deviation (RMSD) of 0.009 m 3 /m 3 . Next, four data combinations (AM + Terra, AM + Aqua, PM + Terra, and PM + Aqua) were generated according to the different overpass times of the SMAP and MODIS observations, and they were separately used to derive the spatially downscaled SSM with the proposed RF-based downscaling method. Validation was performed with the in situ measurements from the REMEDHUS network of the University of Salamanca in Spain. The results indicated that all combinations have similar good performances with an unbiased root-mean-square difference (ubRMSD) of 0.022 m 3 /m 3 , and the downscaled SSM at 1-km spatial resolution presented better accuracy while showing higher spatial heterogeneity and more detailed temporal pattern. Finally, the temporal changing pattern of the downscaled SSM was assessed with the precipitation time series from eight meteorological stations in the study area, and the rainfall effect on the variation of SSM was well tracked from its temporal changes. Overall, this study suggests that the proposed RF-based downscaling method is able to capture the variation of SSM well, and it should be helpful to improve the resolution of passive microwave soil moisture data and facilitate their applications at small scales.

中文翻译：

---

使用随机森林回归的 SMAP 被动地表土壤水分产品的空间降尺度方法

摘要 被动微波表面土壤水分（SSM）产品的低分辨率特性极大地限制了其在区域或局部尺度的许多领域的应用。为了克服这一限制，本研究提出了一种基于随机森林 (RF) 的降尺度方法，通过协同使用来自土壤的光学/热红外 (TIR) 观测结果来分解土壤水分主动和被动 (SMAP) SSM 产品。 Terra 和 Aqua 卫星上的中分辨率成像光谱辐射计 (MODIS)。伊比利亚半岛被选为 2015 年至 2016 年期间的研究区域。 首先，基于 RF 的方法在构建具有地表变量（地表温度、植被指数、叶面积指数、反照率、水指数，太阳因子，和高程）与广泛使用的基于多项式的关系模型产生的结果进行了比较。相关系数 (R) 高于 0.95 且平均均方根偏差 (RMSD) 为 0.009 m 3 /m 3 的基于 RF 的方法取得了良好的一致性。接下来，根据SMAP和MODIS观测的不同通过时间生成四种数据组合（AM+Terra、AM+Aqua、PM+Terra和PM+Aqua），分别用于推导空间缩小的SSM提出的基于 RF 的降尺度方法。使用来自西班牙萨拉曼卡大学 REMEDHUS 网络的原位测量进行了验证。结果表明，所有组合都具有相似的良好性能，无偏均方根差 (ubRMSD) 为 0.022 m 3 /m 3 ，在 1 公里空间分辨率下缩小的 SSM 表现出更好的精度，同时表现出更高的空间异质性和更详细的时间模式。最后，利用研究区8个气象站的降水时间序列评估了降尺度SSM的时间变化规律，并从SSM的时间变化中很好地跟踪了降雨对SSM变化的影响。总的来说，这项研究表明，所提出的基于 RF 的降尺度方法能够很好地捕捉 SSM 的变化，应该有助于提高被动微波土壤水分数据的分辨率并促进其在小尺度上的应用。利用研究区8个气象站的降水时间序列评估降尺度SSM的时间变化规律，并从其时间变化中很好地跟踪了降雨对SSM变化的影响。总的来说，这项研究表明，所提出的基于 RF 的降尺度方法能够很好地捕捉 SSM 的变化，应该有助于提高被动微波土壤水分数据的分辨率并促进其在小尺度上的应用。利用研究区8个气象站的降水时间序列评估降尺度SSM的时间变化规律，并从其时间变化中很好地跟踪了降雨对SSM变化的影响。总的来说，这项研究表明，所提出的基于 RF 的降尺度方法能够很好地捕捉 SSM 的变化，应该有助于提高被动微波土壤水分数据的分辨率并促进其在小尺度上的应用。