Mapping Surface Heat Fluxes by Assimilating SMAP Soil Moisture and GOES Land Surface Temperature Data

Abstract

Surface heat fluxes play a crucial role in the surface energy and water balance. In situ measurements are costly and difficult, and large‐scale flux mapping is hindered by surface heterogeneity. Previous studies have demonstrated that surface heat fluxes can be estimated by assimilating land surface temperature (LST) and soil moisture to determine two key parameters: a neutral bulk heat transfer coefficient (CHN) and an evaporative fraction (EF). Here a methodology is proposed to estimate surface heat fluxes by assimilating Soil Moisture Active Passive (SMAP) soil moisture data and Geostationary Operational Environmental Satellite (GOES) LST data into a dual‐source (DS) model using a hybrid particle assimilation strategy. SMAP soil moisture data are assimilated using a particle filter (PF), and GOES LST data are assimilated using an adaptive particle batch smoother (APBS) to account for the large gap in the spatial and temporal resolution. The methodology is implemented in an area in the U.S. Southern Great Plains. Assessment against in situ observations suggests that soil moisture and LST estimates are in better agreement with observations after assimilation. The RMSD for 30 min (daytime) flux estimates is reduced by 6.3% (8.7%) and 31.6% (37%) for H and LE on average. Comparison against a LST‐only and a soil moisture‐only assimilation case suggests that despite the coarse resolution, assimilating SMAP soil moisture data is not only beneficial but also crucial for successful and robust flux estimation, particularly when the uncertainties in the model estimates are large.

The CR of Evaporation: A Calibration‐Free Diagnostic and Benchmarking Tool for Large‐Scale Terrestrial Evapotranspiration Modeling

“…While ET can be accurately monitored by a wide variety of ground measurements (e.g., lysimeters, energy balance Bowen ratio, eddy covariance [EC], and scintillometry; Allen et al, ), estimation of large‐scale, long‐term ET remains a difficult task since these in situ techniques typically cover short periods (mainly less than a decade) with limited spatial extent. As a result, a number of approaches employing land surface models (LSM; Cai et al, ; Ma et al, ; Xia et al, ), remote sensing (RS) algorithms (Fisher et al, ; Miralles et al, ; Mu et al, ), data assimilation systems (Lu et al, ; Xu et al, ), and/or geostatistical upscaling of ground ET measurements (Jung et al, ), all with inputs from meteorological as well as surface property data, have been devised to quantify ET over larger areas and longer periods. However, most methods mentioned above require a significant number of soil‐ and vegetation‐related parameters as inputs, which are typically interpolated from limited point‐scale measurements/surveys and/or are retrieved from satellite/airborne observations (Masson et al, ), thus leading to additional uncertainties.…”

LIDA: A Land Integrated Data Assimilation Framework for Mapping Land Surface Heat and Evaporative Fluxes by Assimilating Space‐Borne Soil Moisture and Land Surface Temperature

“…Following previous studies (Bateni, Entekhabi, & Castelli, 2013; Bateni et al, 2014; Lu et al, 2017), the LST data are directly assimilated without bias correction. Our investigation through a set of synthetic experiments (results are not shown here) showed that assimilating biased SM (as long as the bias is within the reported range for SM SMAP over SGP) only slightly degrades the performance of LIDA on the estimation of heat fluxes.…”

“…Data assimilation (DA) techniques, namely, the variational DA (VDA) technique, have also been used to quantify the heat and evaporative fluxes, specifically through the assimilation of the diurnal cycle of land surface temperature (LST) into a model of the dynamic surface energy balance (SEB) (Abdolghafoorian et al, 2017; Bateni, Entekhabi, & Castelli, 2013; Caparrini et al, 2004a; Lu et al, 2017; Sini et al, 2008; Xu et al, 2019). The rationale for these VDA‐based schemes is based on the recognition that the dynamic of LST contains information on the partitioning of available energy into sensible and latent heat fluxes, since different components of the SEB vary on how they dissipate available energy at the land surface (Bateni & Entekhabi, 2012).…”

Mapping Regional Turbulent Heat Fluxes via Assimilation of MODIS Land Surface Temperature Data into an Ensemble Kalman Smoother Framework

“…Hence, a number of methods have been developed to estimates turbulent heat fluxes from remotely sensed land surface temperature (LST) data. Broadly speaking, these approaches fall into two main categories: retrieval-based (e.g., Anderson et al, 1997;Bastiaanssen, Menenti, et al, 1998;Bastiaanssen, Pelgrum, et al, 1998;Carlson, 2007;Jia et al, 2009;Jiang & Islam, 2003;Kustas et al, 2012;Liang et al, 2010;Liu et al, 2007;Ma et al, 2018;Mallick et al, 2013Mallick et al, , 2014Moran et al, 1994;Norman et al, 1995;Song et al, 2018;Su 2002;Sun et al, 2013;Tang et al, 2010;Wang et al, 2006;Yao et al, 2013;Zhu et al, 2017), and data assimilation approaches (e.g., Abdolghafoorian et al, 2017;Bateni, Entekhabi, & Jeng, 2013;Bateni, Entekhabi, & Castelli, 2013;Bateni et al, 2014;Bateni & Liang, 2012;Boni et al, 2001;Castelli et al, 1999;Caparrini et al, 2003Caparrini et al, , 2004aCaparrini et al, , 2004bCarrera et al, 2015;Farhadi et al, 2014Farhadi et al, , 2016He et al, 2018;Lu et al, 2016Lu et al, , 2017Xu, et al, 2014;Xu, Bateni, et al, 2015;…”