Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment
Introduction
In the drylands of the Sahelian zone in West Africa, management of vegetation resources is of key importance. Since the 1970s, this zone has been characterized by erratic climatic changes/fluctuations having profound impacts on the natural ecosystems and the agricultural production. Proper management of natural resources in this zone requires regular information on the seasonal development and variation in a range of biophysical parameters but because of limited availability of monitoring network in these areas it is a difficult task. Considering the varying spatial and temporal range of these biophysical parameters, it is evident that the use of repetitive accurate Earth Observation (EO) data is the only realistic means of acquiring much of this information. During the last decades, much focus has been placed on the modelling of biophysical variables from EO surface reflectance data aiming at monitoring the productivity of terrestrial ecosystems (net primary production, NPP) and concurrently with the rapid development of satellite sensor design the accuracy of the derivation of biophysical variables have also improved considerably. New satellite sensors like MODIS sensor, flying aboard NASA's TERRA satellite and the MERIS carried by the European ENVISAT have been substantially improved on the subject of spectral and spatial resolution compared to the widely used NOAA AVHRR.
When modelling net primary production (NPP) in a semiarid environment like the Sahelian zone canopy, water stress is a key variable. NPP has traditionally been assessed using the light use efficiency approach (LUE) but without information on canopy water stress the LUE approach provides an estimate of potential rather than actual production. Canopy water stress has proved to be difficult to derive from conventional EO data. However, the spectral configuration on the MODIS sensor open up the possibilities of the derivation of a shortwave infrared water stress index (SIWSI) on a daily basis using the information from the shortwave infrared channel 5 (1230–1250 nm) or 6 (1628–1652 nm), which are wavelength areas at which leaf water content influence the radiometric response. In this paper, two different configurations of the SIWSI are derived from the MODIS near- and shortwave infrared data. The indices are compared to in situ top layer soil moisture measurements from the semiarid Senegal 2001 and 2002, serving as an indicator of canopy water content. Soil moisture is furthermore modelled from daily rainfall data to be able to test the SIWSIs against soil moisture for a larger number of locations than the fieldwork sites.
Section snippets
Background
A summary of the approaches on the subject of plant water stress monitoring is provided starting with a brief review on the development of the NPP assessment to underline the importance of a water stress estimate within the scientific domain of modelling NPP in a semiarid environment.
The net primary production provides a measure of the production activity or growth of terrestrial vegetation. Estimation of NPP is often based on the LUE concept originally proposed by Monteith (1972) who
Method
The method used in this study is a continuation of the approach using information from the near- and shortwave infrared wavelengths. The new MODIS satellite instrument has the advantage of two narrow discrete channels in the SWIR with a signal to noise ratio above 100 (Guenther, Xiong, Salomonson, Barnes, & Young, 2002) which both could be useful for monitoring of leaf water content (Gao, 1996). In Fig. 1, the MODIS bands 1–6 spectral configurations combined with information of the atmospheric
Study area
The climate of Senegal is characterized by a rainy season lasting from approximately May to October in the southern part of the country and from July to October in the northern part. Rainfall in the northern part of the country is very sparse with average values of 200–400 mm/year. The southern part of the country normally receives from 800 to 1200 mm/year. The strong North–South rainfall gradient is a characteristic feature of the entire Sahelian region and moving towards the north the
Fieldwork
Fieldwork has been performed at one location (Dahra, Fig. 4) in 2001 and at four different locations in 2002 (two sites near Dahra 10 km apart and two sites near Tessekre 10 km apart). From June to November both years, NDVI, rainfall and volumetric soil moisture content in a depth of 10 cm are measured (Table 1). Soil moisture is measured by Theta probe soil moisture sensors, type ML2x responding to changes in the apparent dielectric constant and rainfall is measured by Rain Collector standard
Comparing SIWSI with in situ soil moisture measurements
Canopy water stress measured from satellite (the SIWSI(6,2) configuration) is tested against in situ measurements of soil moisture in Fig. 9. The 2001 rainfall in the central and northern part of Senegal in general reflects the average values of 300–500 mm. For the area of fieldwork, 2001 was slightly drier with an annual total of 288 mm. Volumetric soil moisture content measured in 10 cm is reflecting the water availability for the plant photosynthetic activity and a decrease in soil moisture
Conclusions and perspectives
A shortwave infrared water stress index derived from MODIS near- and shortwave infrared channels has been evaluated against in situ measurements of soil moisture, rainfall and NDVI in the semiarid Sahelian zone in West Africa. Daily values of two different configurations of the SIWSI denoted SIWSI(6,2) and SIWSI(5,2) have been compared to soil moisture measured in the 10 cm. The year 2001 rainfall in the region was slightly below average and the results show a strong correlation between SIWSI
Acknowledgements
The study is funded by the Danish Research Councils, ESA related research, Grant No. 9902490. Scientific equipment for fieldwork is funded by the Danish Research Agency, Danish Agricultural and Veterinary Research Council. The authors would like to thank the MODIS Land Discipline Group for creating and sharing the MODIS LAND data. The Centre de Suivi Ecologique in Dakar is kindly acknowledged for providing logistic support. Thanks also to the staff at Institut Sénégalais de Recherhes
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