A Method for Objectively Integrating Soil Moisture Satellite Observations and Model Simulations Toward a Blended Drought Index

Abstract

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.

Multicomponent Satellite Assessment of Drought Severity in the Contiguous United States From 2002 to 2017 Using AMSR‐E and AMSR2

“…Water Resources Research observations of surface soil moisture and air humidity (Yin et al, 2018; have strong potential utility for early drought detection (AghaKouchak et al, 2015;Otkin et al, 2018). The quantification of drought impacts on surface and ground water storage is also essential for effective water resource management (Castle et al, 2014).…”

“…The effective use of global satellite observations can also greatly enhance drought monitoring capabilities by incorporating near‐real time, large‐scale measurements of multiple contributing hydrological variables (Heim, ). In particular, consistent long‐term satellite observations of surface soil moisture and air humidity (Yin et al, ; Du, Kimball, Reichle et al, ) have strong potential utility for early drought detection (AghaKouchak et al, ; Otkin et al, ). The quantification of drought impacts on surface and ground water storage is also essential for effective water resource management (Castle et al, ).…”

Near‐real‐time one‐kilometre Soil Moisture Active Passive soil moisture data product

“…After the radar stopped operation, the SMAP SM data product had been continuously generated with the radiometer (Yin et al, 2018). The SMAP is expected to archive accurate SM with the expected performance that ubRMSE is less than 0.04 m 3 /m 3 (Chan et al, 2016; Colliander et al, 2017).…”

“…It controls the SM‐precipitation feedback at continental scale and runoff‐precipitation response at watershed scale. As a result, SM observations are widely used in meteorology, hydrology and climatology (Peng, Loew, Merlin, & Verhoest, 2017; Yin, Hain, Zhan, Dong, & Ek, 2019; Yin, Zhan, Hain, Liu, & Anderson, 2018). The development of ground‐based SM measurement techniques provides an opportunity to obtain SM estimates at different soil depths (Robinson et al, 2008, Dobriyal et al, 2012, Vereecken et al, 2014) with the in situ observations commonly considered as the “truth” to validate satellite and model SM simulations against.…”

NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

“…The second advantage is from near real time updating soil moisture datasets with short latency. The soil moisture status is an important indicator to monitor agricultural drought [16]. The near real time long-term SMOPS will allow better understanding of drought development through assessing soil moisture deficits over the historical records [41].…”