and Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer
Suite (VIIRS) for vegetation health. There are currently no dedicated missions to
directly estimate ET and agricultural drought from space. However, diverse techniques using satellite products exist in the literature to map ET and drought from
space (Moran et al. 1994; Mu et al. 2007; Allen et al. 2011; Yagci et al. 2017).
In this chapter, three distinct proven methods to estimate soil moisture, ET, and
agricultural drought using satellite and ancillary data will be introduced, and then,
some example maps and their validation results against the ground truth will be
presented.
15.2 Soil Moisture
The characteristics of agricultural drought such as spatial extent, intensity, onset, and
duration (Wilhite 2005) can be accurately traced via monitoring of soil moisture,
which has been recently shown to be a skillful predictor of vegetation conditions
(Bolten et al. 2010). Soil moisture estimates can be obtained using different platforms, like ground station-based observations, hydrological model simulations, and
satellite remote sensing. Ground station-based observations suffer from spatial
representativeness errors, while hydrological models require accurate precipitation
as well as irrigated water estimates to yield skillful soil moisture simulations over
agricultural fields (Hain et al. 2015). On the other hand, remote sensing-based
observations can accurately predict the ground conditions with sufficient spatial
representation (Parinussa et al. 2014), thereby providing reliable independent estimates of ground conditions.
Satellite-based soil moisture estimates also suffer from many different error
sources. As a result, it is often desirable to merge independent soil moisture
information to acquire a more skillful product. Such merging can be implemented
using simple least-squares merging methods (Yilmaz et al. 2012; Anderson et al.
2012) as well as data assimilation methods (Houser et al. 1998; Bolten et al. 2010;
Yilmaz et al. 2011; Maggioni and Houser 2017). Merged soil moisture estimates in
most cases improve the soil moisture product accuracy in isolation. The studies in
general emphasize the added utility of the merging system as a whole, while this
added utility is not separately analyzed for its components.
In this study, simple soil moisture merging estimation methodology is introduced
as a benchmark product for the comparison of the skill of soil moisture estimates in
merging studies. Such a product could be very helpful in distinguishing the added
utility gained via independent products and merging methodologies.
15 Mapping and Monitoring of Soil Moisture, Evapotranspiration, and Agricultural. . .
301
Suite (VIIRS) for vegetation health. There are currently no dedicated missions to
directly estimate ET and agricultural drought from space. However, diverse techniques using satellite products exist in the literature to map ET and drought from
space (Moran et al. 1994; Mu et al. 2007; Allen et al. 2011; Yagci et al. 2017).
In this chapter, three distinct proven methods to estimate soil moisture, ET, and
agricultural drought using satellite and ancillary data will be introduced, and then,
some example maps and their validation results against the ground truth will be
presented.
15.2 Soil Moisture
The characteristics of agricultural drought such as spatial extent, intensity, onset, and
duration (Wilhite 2005) can be accurately traced via monitoring of soil moisture,
which has been recently shown to be a skillful predictor of vegetation conditions
(Bolten et al. 2010). Soil moisture estimates can be obtained using different platforms, like ground station-based observations, hydrological model simulations, and
satellite remote sensing. Ground station-based observations suffer from spatial
representativeness errors, while hydrological models require accurate precipitation
as well as irrigated water estimates to yield skillful soil moisture simulations over
agricultural fields (Hain et al. 2015). On the other hand, remote sensing-based
observations can accurately predict the ground conditions with sufficient spatial
representation (Parinussa et al. 2014), thereby providing reliable independent estimates of ground conditions.
Satellite-based soil moisture estimates also suffer from many different error
sources. As a result, it is often desirable to merge independent soil moisture
information to acquire a more skillful product. Such merging can be implemented
using simple least-squares merging methods (Yilmaz et al. 2012; Anderson et al.
2012) as well as data assimilation methods (Houser et al. 1998; Bolten et al. 2010;
Yilmaz et al. 2011; Maggioni and Houser 2017). Merged soil moisture estimates in
most cases improve the soil moisture product accuracy in isolation. The studies in
general emphasize the added utility of the merging system as a whole, while this
added utility is not separately analyzed for its components.
In this study, simple soil moisture merging estimation methodology is introduced
as a benchmark product for the comparison of the skill of soil moisture estimates in
merging studies. Such a product could be very helpful in distinguishing the added
utility gained via independent products and merging methodologies.
15 Mapping and Monitoring of Soil Moisture, Evapotranspiration, and Agricultural. . .
301