Contents lists available at ScienceDirect Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet An improved surface soil moisture downscaling approach over cloudy areas based on geographically weighted regression Peilin Song a,b , Jingfeng Huang a,b, ⁎ , Lamin R. Mansaray c a Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou, 310058, PR China b Key Laboratory of Agricultural Remote Sensing and Information Systems, Zhejiang University, Zhejiang Province, Hangzhou, 310058, PR China c Laboratoty of Agro-Meteorology and Geo-Informatics, Magbosi Land, Water and Environment Research Centre (MLWERC), Sierra Leone Agricultural Research Institute (SLARI), Tower Hill, PMB 1313, Freetown, Sierra Leone ARTICLE INFO Keywords: Surface soil moisture (SSM) Land surface temperature (LST) interpolation Passive microwave Downscaling Geographically Weighted Regression (GWR) ABSTRACT This study proposed a methodological framework for downscaling AMSR-2 surface soil moisture (SSM) products over cloudy areas using MODIS LST/NDVI datasets. The experiment was conducted in a relatively large area of 430,000 km 2 in the middle and lower reaches of the Yangtze and Huaihe rivers in China, which is characterized by humid climate and frequent cloudy weather conditions. As MODIS LSTs suffer from serious pixel loss due to cloud interference in this area, an effective LST interpolation method was preliminarily applied to achieve daily LST datasets with quasi-full covers. And rather small RMSEs in the range 1.5 K–3.5 K were obtained when the interpolated LST datasets were validated against a reference LST dataset built from observed relationships be- tween LST and ground-based near-surface air temperatures on clear sky days. A regression equation was then established between AMSR-2 SSM and spatially resampled MODIS datasets using “Geographically Weighted Regression (GWR)” to implement the SSM downscaling process. SSM estimates downscaled by the GWR-based method showed a better performance over those downscaled by the traditional “universal triangle feature (UTF)” based method in view of their “non-biased RMSEs (ubRMSEs)”, correlation coefficients, and mean biases with respect to ground-based soil moisture validation data. Comparisons between SSM estimates from MODIS LST inputs and those from interpolated LST inputs were conducted, and they showed that the SSM estimates downscaled by interpolated LST inputs performed only slightly poorer (with an ubRMSE difference no larger than 0.02 cm 3 /cm 3 ) than those by MODIS data. Time series analysis further showed that the GWR-based downscaled SSM estimates with reconstructed LST data inputs are in phase with the variation in ground-based soil moisture with the exception of areas of extremely high vegetation cover or low temperatures. The frame- work proposed in this study thus proved feasible for the derivation of reliable downscaled high spatial resolution SSM estimates, an essential application in mitigating pixel loss under cloudy weather conditions. 1. Introduction Surface soil moisture (SSM) is an important variable in terrestrial hydrological cycles and global energy exchanges. SSM interacts with vegetation covers and plays an important role in ecosystem functioning, water resource cycles, and in climate/weather monitoring and predic- tion systems. With good penetration capability through the vegetation canopy and atmospheric layers coupled with the advantage of all- weather observations, passive microwave remote sensing techniques based on datasets observed from space-borne sensors such as the Tropical Rainfall Measuring Mission Microwave Imager (TMI) (Gao et al., 2006), the “Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E)” (Njoku et al., 2003), AMSR-2 (Parinussa et al., 2015), Soil Moisture and Ocean Salinity Mission (SMOS) (Berger et al., 2002) and Soil Moisture Active Passive Mission (SMAP) (Entekhabi et al., 2010), have played a crucial role in the estimation of near surface soil moisture, especially for global-scale applications. However, the poor spatial resolution of passive microwave sensors (usually several tens of kilometers) as well as the practical constraints on antenna size and low altitude earth orbits, are usually considered not sufficient enough for regional and local scale studies, conditions under which SSM datasets with higher spatial resolution are required. Downscaling passive-microwave-derived SSM products by the sy- nergistic coupling of optical/thermal-infrared datasets is currently one of the most widely applied techniques to obtain SSM representations at high spatio-temporal resolution, with its advantages of easier https://doi.org/10.1016/j.agrformet.2019.05.022 Received 28 March 2018; Received in revised form 13 May 2019; Accepted 22 May 2019 ⁎ Corresponding author at: Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou, 310058, PR China. E-mail addresses: foreverspl@zju.edu.cn (P. Song), hjf@zju.edu.cn (J. Huang), l.mansaray@slari.gov.sl (L.R. Mansaray). Agricultural and Forest Meteorology 275 (2019) 146–158 0168-1923/ © 2019 Elsevier B.V. All rights reserved. T