Data assimilation is the application of Bayes' theorem to condition the states of a dynamical systems model on observations. Any real‐world application of Bayes' theorem is approximate, and therefore, we cannot expect that data assimilation will preserve all of the information available from models and observations. We outline a framework for measuring information in models, observations, and evaluation data in a way that allows us to quantify information loss during (necessarily imperfect) data assimilation. This facilitates quantitative analysis of trade‐offs between improving (usually expensive) remote sensing observing systems versus improving data assimilation design and implementation. We demonstrate this methodology on a previously published application of the ensemble Kalman filter used to assimilate remote sensing soil moisture retrievals from Advanced Microwave Scattering Radiometer for Earth (AMSR‐E) into the Noah land surface model.

With satellite soil moisture (SM) retrievals becoming widely and continuously available, we aim to develop a method to objectively integrate the drought indices into one that is more accurate and consistently reliable. The data sets used in this paper include the Noah land surface model‐based SM estimations, Atmosphere‐Land‐Exchange‐Inverse model‐based Evaporative Stress Index, and the satellite SM products from the Advanced Scatterometer, WindSat, Soil Moisture and Ocean Salinity, and Soil Moisture Operational Product System. Using the Triple Collocation Error Model (TCEM) to quantify the uncertainties of these data, we developed an optically blended drought index (BDI_b) that objectively integrates drought estimations with the lowest TCEM‐derived root‐mean‐square‐errors in this paper. With respect to the reported drought records and the drought monitoring benchmarks including the U.S. Drought Monitor, the Palmer Drought Severity Index and the standardized precipitation evapotranspiration index products, the BDI_b was compared with the sample average blending drought index (BDI_s) and the RMSE‐weighted average blending drought indices (BDI_w). Relative to the BDI_s and the BDI_w, the BDI_b performs more consistently with the drought monitoring benchmarks. With respect to the official drought records, the developed BDI_b shows the best performance on tracking drought development in terms of time evolution and spatial patterns of 2010‐Russia, 2011‐USA, 2013‐New Zealand droughts and other reported agricultural drought occurrences over the 2009–2014 period. These results suggest that model simulations and remotely sensed observations of SM can be objectively translated into useful information for drought monitoring and early warning, in turn can reduce drought risk and impacts.

Soil moisture (SM) data from Soil Moisture Operational Product System (SMOPS) have been available and used by users since 2013, and the latest version (3.0) has been operationally released since 2017. The version 3.0 provides a combination of currently all available daily global microwave SM retrievals including observations of ASCATA, ASCATB, SMAP, SMOS, and AMSR2 from 1 April 2015 to present. This study intercompares Noah model skills with benefits of assimilating the SMOPS blended (hereafter, SMOPS) and the five individual satellite SM retrievals. Results show that SMOPS SM product presents a significant advantage in data availability in comparison with the individual SM retrievals. Significant differences in data availability, climatology, and dynamic range of SM values between the bias‐corrected SMOPS and individual SM data lead to remarkable distinctions in Noah model SM simulations. Significant improvements of assimilating individual and blended satellite SM retrievals on model SM simulations versus the open loop in both surface and root zone soil layers are evident with reducing the Soil Climate Analysis Network measurements‐based root mean square errors and raising the correlations with respect to the enhanced vegetation index. Compared to the individual SM assimilations, model SM estimations with benefits of assimilating the SMOPS data provide the more remarkable improvements in surface soil layer.

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

Evapotranspiration (ET) is one of the main components of the hydrological or water cycle. The latent heat from evapotranspiration is also one of the most important components of the energy cycle because it is the largest energy source for the atmosphere and thus is significant for weather and climate formation. Local scale evapotranspiration is mostly observed with ground instruments such as a lysimeter, Bowen ratio, or eddy covariance tower. Regional scale evapotranspiration is estimated from surface water balance or atmospheric water balance. Remote sensing approaches have been developed to retrieve regional or global scale evapotranspiration in recent decades. As the lack or reduction of evapotranspiration indicates drought, remote sensing of evapotranspiration has been applied to monitoring regional or global droughts in recent years. In this chapter we briefly review ET remote sensing studies, starting with a historical sketch before introducing the Geostationary Operational Environmental Satellites’ (GOES) ET and Drought (GET-D) product system that is operational at the National Environmental Satellite, Data, and Information Service (NESDIS). The GET-D system implements the Atmosphere–Land Exchange Inversion (ALEXI) model for estimating regional daily ET from observations of the NESDIS Geostationary Operational Environmental Satellites. The Evaporative Stress Index (ESI) based on ALEXI ET is used for monitoring drought currently for North America. An approach to merging the ESI data into microwave soil moisture observations and land-surface model soil-moisture simulations for a blended drought index is presented. The feasibility of using the ALEXI ET estimates from global satellite observations for drought monitoring is discussed.