Abstract
A series of satellite-based passive and active microwave instruments provide soil moisture retrievals spanning
altogether more than three decades. This offers the opportunity to generate a combined product that incorporates
the advantages of both microwave techniques and spans the observation period starting 1979. However,
there are several challenges in developing such a dataset, e.g., differences in instrument specifications result in
different absolute soil moisture values, the global passive and active microwave retrieval methods produce
conceptually different quantities, and products vary in their relative performances depending on vegetation density.
This paper presents an approach for combining four passive microwave products from the VU University
Amsterdam/National Aeronautics and Space Administration and two active microwave products from the
Vienna University of Technology. First, passive microwave soilmoisture retrievals fromthe ScanningMultichannel
Microwave Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I), and the Tropical Rainfall
Measuring Mission microwave imager (TMI) instruments were scaled to the climatology of the Advanced
Microwave Scanning Radiometer — Earth Observing System (AMSR-E) derived product and then all four were
combined into a single merged passive microwave product. Second, active microwave soil moisture estimates
from the European Remote Sensing (ERS) Scatterometer instrument were scaled to the climatology of the
Advanced Scatterometer (ASCAT) derived estimates. Both were combined into a merged active microwave
product. Finally, the two merged products were rescaled to a common globally available reference soilmoisture
dataset provided by a land surfacemodel (GLDAS-1-Noah) and then blended into a single passive/active product.
Blending of the active and passive data setswas based on their respective sensitivity to vegetation density.While
this three step approach imposes the absolute values of the land surface model dataset to the final product, it
preserves the relative dynamics (e.g., seasonality and inter-annual variations) of the original satellite derived
retrievals.More importantly, the long term changes evident in the original soilmoisture productswere also preserved.
The method presented in this paper allows the long term product to be extended with data from other
current and future operational satellites. The multi-decadal blended dataset is expected to enhance our basic understanding of soil moisture in the water, energy and carbon cycles
