Evaluating global trends (1988–2010) in harmonized multi‐satellite surface soil moisture

Global trends in a new multi‐satellite surface soil moisture dataset were analyzed for the period 1988–2010. 27% of the area covered by the dataset showed significant trends (p = 0.05). Of these, 73% were negative and 27% positive. Subtle drying trends were found in the Southern US, central South America, central Eurasia, northern Africa and the Middle East, Mongolia and northeast China, northern Siberia, and Western Australia. The strongest wetting trends were found in southern Africa and the subarctic region. Intra‐annual analysis revealed that most trends are not uniform among seasons. The most prominent trend patterns in remotely sensed surface soil moisture were also found in GLDAS‐Noah and ERA Interim modeled surface soil moisture and GPCP precipitation, lending confidence to the obtained results. The relationship with trends in GIMMS‐NDVI appeared more complex. In areas of mutual disagreement more research is needed to identify potential deficiencies in models and/or remotely sensed products.

[1]  P. Sen.  Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[2]  D. Helsel,et al.  Statistical methods in water resources , 2020, Techniques and Methods.

[3]  Thomas J. Jackson,et al.  Soil moisture retrieval from AMSR-E , 2003, IEEE Trans. Geosci. Remote. Sens..

[4]  J. Janowiak,et al.  The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present) , 2003 .

[5]  Mingquan Mu,et al.  Forty‐five years of observed soil moisture in the Ukraine: No summer desiccation (yet) , 2004 .

[6]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[7]  Venkat Lakshmi,et al.  The role of satellite remote sensing in the Prediction of Ungauged Basins , 2004 .

[8]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[9]  Dennis P. Lettenmaier,et al.  Long-Term Climate and Derived Surface Hydrology and Energy Flux Data for Mexico: 1925–2004 , 2007 .

[10]  W. Wagner,et al.  Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT) , 2007 .

[11]  Michael E. Schaepman,et al.  A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[12]  E. Wood,et al.  Global Trends and Variability in Soil Moisture and Drought Characteristics, 1950–2000, from Observation-Driven Simulations of the Terrestrial Hydrologic Cycle , 2008 .

[13]  C. Albergel,et al.  From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations , 2008 .

[14]  R. Jeu,et al.  Multisensor historical climatology of satellite‐derived global land surface moisture , 2008 .

[15]  D. Verdon‐Kidd,et al.  Nature and causes of protracted droughts in southeast Australia: Comparison between the Federation, WWII, and Big Dry droughts , 2009 .

[16]  Dick Dee,et al.  Low‐frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets , 2010 .

[17]  S. Seneviratne,et al.  Recent decline in the global land evapotranspiration trend due to limited moisture supply , 2010, Nature.

[18]  Yi Y. Liu,et al.  Error characterisation of global active and passive microwave soil moisture datasets. , 2010 .

[19]  D. E. Harrison,et al.  Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 Update) , 2010 .

[20]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[21]  R. Koster,et al.  Assessment and Enhancement of MERRA Land Surface Hydrology Estimates , 2011 .

[22]  Yi Y. Liu,et al.  Global long‐term passive microwave satellite‐based retrievals of vegetation optical depth , 2011 .

[23]  Yi Y. Liu,et al.  Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals , 2011 .

[24]  A. Dai.  Drought under global warming: a review , 2011 .

[25]  Tao Zhang,et al.  Was there a basis for anticipating the 2010 Russian heat wave? , 2011 .

[26]  Robert M. Parinussa,et al.  Error Estimates for Near-Real-Time Satellite Soil Moisture as Derived From the Land Parameter Retrieval Model , 2011, IEEE Geoscience and Remote Sensing Letters.

[27]  E. Wood,et al.  Observed Land–Atmosphere Coupling from Satellite Remote Sensing and Reanalysis , 2011 .

[28]  Shilong Piao,et al.  A global analysis of soil moisture derived from satellite observations and a land surface model , 2012 .

[29]  Lars Isaksen,et al.  Soil Moisture Analyses at ECMWF: Evaluation Using Global Ground-Based In Situ Observations , 2012 .

[30]  Sidebar 2.2: building a climate record of soil moisture from historical satellite observations , 2012 .

[31]  Y. Kerr,et al.  Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations , 2012 .

[32]  C. Taylor,et al.  Afternoon rain more likely over drier soils , 2012, Nature.

[33]  Yi Y. Liu,et al.  Trend-preserving blending of passive and active microwave soil moisture retrievals , 2012 .