IR ¼ ETc À P À ΔS
ð7:20Þ
Although ETc is a crop-specific parameter, it can be calculated from the reference
evapotranspiration (ET 0 ) that depends on meteorological parameters (Allen et al.
1998):
ETc ¼ Kc ∙ ET 0
ð7:21Þ
where Kc is a crop-specific coefficient depending on the phenological stage. ΔS is
affected by soil water capacity besides the difference between crop water consumption and the total water intake. It is also known that soil moisture is also effective on
some remote sensing indices as the normalized difference moisture index (NDMI)
and NDVI. Even though crop-specific parameters and models are not known, a data
fusion scheme can still be constructed in different ways by using the existing data
records and the crop cover map as a context (Kulaglic and Üstündağ 2014). One of
them is shown in Fig. 7.13 that is intended to nowcast root zone soil moisture
depending on precipitation, ET 0 , irrigation, and the NDVI.
Fig. 7.13 A data fusion model example for large-scale plant root zone soil moisture estimation
Fig. 7.14 Time series NDVI (sNDVI) generation model
7 Data Fusion in Agricultural Information Systems
125
ð7:20Þ
Although ETc is a crop-specific parameter, it can be calculated from the reference
evapotranspiration (ET 0 ) that depends on meteorological parameters (Allen et al.
1998):
ETc ¼ Kc ∙ ET 0
ð7:21Þ
where Kc is a crop-specific coefficient depending on the phenological stage. ΔS is
affected by soil water capacity besides the difference between crop water consumption and the total water intake. It is also known that soil moisture is also effective on
some remote sensing indices as the normalized difference moisture index (NDMI)
and NDVI. Even though crop-specific parameters and models are not known, a data
fusion scheme can still be constructed in different ways by using the existing data
records and the crop cover map as a context (Kulaglic and Üstündağ 2014). One of
them is shown in Fig. 7.13 that is intended to nowcast root zone soil moisture
depending on precipitation, ET 0 , irrigation, and the NDVI.
Fig. 7.13 A data fusion model example for large-scale plant root zone soil moisture estimation
Fig. 7.14 Time series NDVI (sNDVI) generation model
7 Data Fusion in Agricultural Information Systems
125