Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

Abstract

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product

“…The time series standard deviation of the O-F residuals is a measure of the typical misfit between the model forecast Tb and the (rescaled) SMAP observations. Similarly, the time series standard deviation of the increments is a measure of the typical adjustment of the model forecast soil moisture at any given (Balsamo et al, 2018, their Figure 8a; Figure 6), whereas at other sites (e.g., Little Washita, Oklahoma), the model changes had less impact on the skill of the soil moisture estimates (Reichle, Liu, et al, 2018, their Figure 5). For reference, supporting information Tables S2-S5 provide a complete listing of the performance metrics at all sites.…”

Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

“…The use of SRS data in water resources monitoring is promising, and it has led to an increasing number of studies on a variety of topics in hydrology, including precipitation, evaporation, and soil moisture estimation (Cazenave et al, 2016;Chen & Wang, 2018;Cui et al, 2019;National Academies of Sciences, Engineering, and Medicine, 2019;Schultz & Engman, 2012). SRS data complement in situ hydrometeorological data (Balsamo et al, 2018), which are typically scarce and whose unavailability hinders the understanding of environmental systems (Tang et al, 2009). This aspect is particularly relevant for developing countries where research for development initiatives have been increasing in the recent years (Montanari et al, 2015).…”

Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations

“…Biases are a common issue of snowpack remote sensing (Veyssière et al, 2019;Balsamo et al, 2018) and require a proper estimation or correction before assimilation. Many methods exist in the NWP community to correct for the bias or dynamically estimate it in a data assimilation system (Draper et al, 2015;Auligné et al, 2007).…”

“…(3) extensive, representative, and continuous in-situ observations of snowpack variables to constrain satellite reflectance biases (4) additional data from other satellite sources (Balsamo et al, 2018). All of those suffer from limitations owing to the specificities of snowpack modelling and monitoring in a complex terrain, respectively : (1) snowpack reflectance modelling probably suffers from some biases (Tuzet et al, 2017) (2) absence of any operational network measuring in-situ snowpack reflectance (3) sparse in-situ snowpack measurements in general (4) lack of reliable reflectance retrieval from other satellite sources (as shown here for S2).…”