Remote sensing based indices for streamflow estimation

Derivatives of RS data have been used as indicators of vegetation, water, soil, atmosphere and clouds. The use of RS based indices as indicators of biophysical properties of vegetation stemmed from the application of satellite data to civilian purposes (Jensen, 2000). Jackson (1983) emphasized several qualities that the vegetation index should have :“the index should be particularly sensitive to vegetative covers, insensitive to soil brightness, insensitive to soil color, little affected by atmospheric effects, environmental effects and solar illumination geometry and sensor viewing conditions”. Based on that, the reflectance value of visible, near infrared and mid infrared bands have been used as inputs to vegetation indices (Jensen, 2000).

Pearson and Miller (1972) developed the first two vegetation indices, which are the ‘Ratio Vegetation Index’ (RVI) and the ‘Vegetation Index Number’ (VIN). They are the band ratios of red and near infrared bands. They can be written as;

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= (2.4)

where R is the mean reflectance of the red band and NIR is the mean reflectance of the near infrared band. These indices were used to enhance contrast between land and vegetation, but they are very sensitive to the atmospherical effects.

Later, the Normalized Difference Vegetation Index (NDVI) was introduced by Tucker (1979), and quickly become the most commonly used vegetation index within the RS community (Kite and Pietroniro, 1996). It represents vegetation density, vigor, vegetation stage and seasonality (Jackson et al., 2004; Thenkabail et al., 2004). Furthermore, it has been used to estimate the Leaf Area Index (LAI). LAI is identified as the single most important variable for quantifying energy and mass exchange by plant canopies over landscapes (Running et al., 1986). Therefore, LAI has become a vital variable in hydrological process modelling. LAI has been widely used to generate ET information that are few into the catchment process models (Kite and Pietroniro, 1996; Andersen et al., 2002) for streamflow estimation. Since, the traditional method for calculating LAI is laborious (Kite and Pietroniro, 1996), many authors used NDVI to calculate this vital information. As a representative index of vegetation, NDVI has been used in various other applications such as LULC classification (Gamage et al., 2007), drought monitoring (Thenkabail et al., 2004), spatial downscaling of TRMM data (Immerzeel et al., 2009), soil moisture estimation (Wang et al., 2007; Schnur et al., 2010) and for understanding the seasonal dynamics of the canopy cover (de Silveira et al., 2007).

The inherent nonlinearity of ratio based indices (i.e. the index is not directly proportionate to the input) is the main disadvantage of the NDVI. In addition, this index is sensitive to additive noise effects such as atmospheric path radiances, and it exhibits scaling problems and saturated signals over high biomass conditions. Furthermore, it is very sensitive to canopy background variations with NDVI degradation being particularly strong in case of high canopy background brightness (Huete, 1988). To address these disadvantages, Huete et al. (2002) introduced the Enhanced Vegetation Index (EVI) which has improved sensitivity in high biomass regions and improved vegetation monitoring through de- coupling of the canopy background signal and a reduction in atmosphere influences.

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EVI has been widely used in vegetation applications. Huete et al. (2006) used EVI to examine the vegetation growth of the Amazon forest during its dry season. EVI has also been used in LAI calculations as a substitute to NDVI in ET estimations (Ahmad et al., 2005). Guerschman et al. (2009) used monthly EVI and interpolated climate data as input variables to derive monthly actual ET estimates. The main advantage of this methodology which was facilitated by EVI, is that it required a single set of parameters. The EVI has been widely applied in many disciplines including in the calculation of Net Primary Production (Wu et al., 2011).

Both EVI and NDVI are good indices that represent vegetation greenness well, but they perform poorly in respect to vegetation water content (Jackson et al., 2004), which is a surrogate of soil moisture content. Therefore, Jackson et al. (2004) used the Normalized Difference Water Index (NDWI) (Gao, 1996) to map the vegetation water content over agricultural crops. They were able to successfully map the vegetation water content using NDWI over the period of 1 month which was their study period. This index was further examined by Weissling and Xie (2009) who used NDWI and other indices to estimate the 8-day mean streamflow in a Texas (USA) catchment. They tested 32 variables which are based on visible and thermal bands. Out of those 32 variables, they found that the deseasoned land surface moisture stress index, NEXRAD precipitation and the MODIS daytime land surface temperature are significantly related to streamflow. In this study, they also found a fair level of agreement between observed and estimated streamflow, and concluded that the estimation performances could have been improved by improving NEXRAD precipitation.

Other than vegetation indices, indices based on thermal bands were also used in vegetation and hydroclimatological applications. Visible, near infrared and mid infrared bands’ reflectance values were directly used as inputs for the calculation of vegetation indices. However, the radiance values of thermal bands were converted into brightness temperature before they were used as inputs to thermal indices.

Brightness temperature (BT) has been directly used to estimate rainfall given its direct relationship with rainfall (Arkin, 1979; Arking and Childs, 1985; Arkin and Meisner, 1987). Furthermore, BT has been used to separate no-rain clouds from rain clouds (Ba and Gruber, 2001a; Kuligowski, 2002). For example, Kuligowski (2002) used brightness

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temperature difference and brightness temperature gradient to conduct such separation. These indices indicate the presence of water vapor and textural information of the cloud top. These properties provide vital information needed to separate rain clouds from no-rain clouds. In addition to its cloud-related applications, BT has been widely used in ET estimation (Bastiaanssen et al., 1998b; Bastiaanssen et al., 2002), which were then used in water productivity investigations over large command areas (Gamage et al., 2009a).

The above mentioned thermal indices and BT based applications were initially restricted to the data acquired from meteorological satellites. However, such applications were made possible with sun synchronized satellites like NOAA AVHRR. Since NOAA AVHRR holds two thermal bands (band 4 and 5), it was possible to use BT difference as an index for rain/no rain cloud separation. This situation was further improved with the emergence of the MODIS satellite. MODIS introduces several other thermal bands in addition to the bands of NOAA AVHRR. Indeed, MODIS adds 14 new thermal bands in the range of 3.660 µm to 12.270 µm, which can be used in various applications such as surface and atmospheric temperature mapping, cirrus cloud identification and cloud top temperature estimation.

The literature described above has important implications for the hypothesis of this study: that remotely sensed indices (both vegetation and thermal) sufficiently represent the variation of hydrometeorological variables such as rainfall, evapotranspiration and soil moisture. However, sparse literature is available for using RS indices to estimate streamflow. This will be explored later in this study.

2.5

Summary

This chapter started by explaining the process of streamflow estimation, its history and classification of streamflow estimation models. It then proceeded to exploring the history of RS and discussing RS systems that are used to acquire data. Some satellites and sensors are also discussed. These satellites and their sensors were especially selected as they have been widely used in hydrological applications. Next, the chapter reviewed streamflow estimation with RS data. Under this section, both catchment process modelling and statistical modelling approaches were discussed in two separate sub sections. First the estimation of rainfall, evapotranspiration, and classification of LULC using RS data, which

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are the most important inputs for catchment process modelling, were discussed under the sub section labelled catchment process modelling. Second the RS based vegetation and thermal indices that are surrogates of hydrometeorological variables were reviewed under the sub section labelled statistical modelling.

Different catchment process models that aim at estimating streamflow using RS based input variables as inputs were discussed in Section 2.4.1. The review showed that RS estimated inputs have been used in different spatiotemporal scales ranging from sub-daily to annual and micro catchments to mega catchments. The literature also showed that the accuracy of the estimation is higher in lower temporal resolution such as monthly and annually in large catchments. In contrast, the literature showed that results are not satisfactory when the same data were applied to medium or small catchments on finer temporal resolution (i.e. daily).

Rainfall estimation is the oldest application of RS data which is used in meteorological satellite data. Initial rainfall estimation was based on the cloud indexing technique using thermal infrared bands data. Later, this technique was modified by introducing a brightness temperature threshold for rainy clouds. However, the accuracy of the estimates were poor since brightness temperature only gave information relating to cloud-top. More accurate rainfall estimation processes were developed with microwave and radar RS data, which have the ability to penetrate clouds. However, rainfall estimates, which used microwave and radar, are low in spatial resolution, thus failing to address the variability of rainfall in medium and small catchments.

The estimation of potential evapotranspiration (PET) is another important variable, both in streamflow estimation as well as in agricultural applications. PET data can be derived using direct measurements such as the lysimeter or can be estimated using a modelling approach, which use meteorological variables as inputs. In the absence of direct measurements and meteorological variables, RS data have been used partially used as inputs to estimate PET. SEBAL and SEBS are examples of models that utilize RS data to estimate PET. However these estimations are limited to non-cloudy days.

RS data have been widely used in classifying LULC. The unsupervised and the supervised are the two main LULC classification approaches. The unsupervised classification is

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simple but less accurate. In contrast, supervised classification is complex, but accurate and meaningful. It has three stages, and uses knowledge on the ground to classify LULC. LULC information has been applied in various hydrological models to assess the effect of LULC changes on streamflow generation, ground water discharge and climate change.

Various statistical models have also been used to estimate streamflow with meteorological variables. Recently, the Artificial Neural Networks models have gained popularity among various statistical models. They have also used meteorological variables as inputs to estimate streamflows. At the time of this writing, no literature was available on the use of RS based indices as inputs to estimate streamflow by employing ANN models.

The literature examined in this chapter shows some of the gaps in estimating rainfall, ET and LULC for streamflow estimation. In addition, it is clear that sparse literature is available on the application of RS based indices for streamflow estimation. Therefore, methodologies are proposed to overcome some of the gaps mentioned in this chapter. These methodologies will be discussed in Chapter 3.

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3

CHAPTER 3: STUDY AREA, DATA AND