Estimation of Nitrogen Content in Soybean Using Remote Sensing

Introduction
Chemical concentration of plants is indicator of their biologic status. Among the many foliar chemicals in plants, nitrogen (N) is an important indicator of photosynthetic rate and overall nutritional status. Plants usually take up nitrogen in the nitrate form (NO₃^-) and one major source of nitrate leaching is fertilizer applied to the crops. Supplying inadequate N may decrease crop yields and increase the N fertilizer (more than the needs of plants). In addition to economic loss, nitrate ions may move into surface and ground water and contribute to eutrophication of lakes and streams and raise health problem (Liaghat and Balasundram 2010). Thus estimation of nitrogen content is important in many agricultural studies.
Traditionally leaf nitrogen content is measured in the lab using different chemical methods. Nitrogen analysis either by the Kjeldahl or Dumas method is expensive and requires specialized equipments. An alternative method for N determination is the digestion of potassium persulfate (K₂S₂O₈). Persulfate digestion requires only a modest initial investment and has few environmental risks. The common problems of all above mentioned approaches are the facts that they are time consuming, expensive and destructive approaches. The advent of remote sensing has proved its usefulness as an alternative measure to these traditional approaches.
The aim of this study is to estimate canopy nitrogen content in vast area in northern part of Iran, Gorgan, using remote sensing vegetation indices. Later it was used in calibration of different vegetation indices and for estimation of CNC of a vast area in Gorgan, Iran.
 
Methodology
LANDSAT TM imagery simultaneous to the field campaign was acquired. The field campaign was conducted in the latter half of August 2009 in northern part of Iran, Gorgan (36° 54' N, 54° 53' E). Fifty sample plots of 30 m× 30m were randomly chosen. In each sample plot, 4 to 7 subplots were selected and in each subplot 30 leaves form different parts of Soybean crops were cut and transferred to lab. Then using persulfate digestion, nitrogen content of the leaves was determined. In the field, canopy percentage was measured and multiplied by the leaf nitrogen content to calculate the canopy nitrogen content (CNC). The regression line between different vegetation indices (NDVI, GI, SAVI2, GRI) and CNC was calculated and the results validated using cross validation approach.
 
Results and Discussion
Our study showed that the Persulfate digestion is an accurate method for determination of total N in soybean plant when measured in lab. Persulfate digestion does not produce a large quantities of toxic waste associated with Kjeldahl digestion. Additionally, persulfate digestion requires a minimum of specialized equipment: large screw topped culture tubes, an autoclave or a large pressure cooker and test tube racks. The method facilitates the determination of a large number of samples with the use of simple equipments.
The relation of measured nitrogen at leaf and canopy level against indices is shown in figure 2. Clearly the relation at canopy level shows a better behavior than at leaf level. Results showed that GI has close relationship with CNC and can be used to retrieve crop vegetation nitrogen. This index uses green band of the electromagnetic spectrum which is appropriate for chlorophyll estimation and has a direct relationship with nitrogen. The most commonly used vegetation index is NDVI. The NDVI has been used for many years to measure and monitor plant growth, biomass production and vegetation cover from multispectral satellite data. Although in our study NDVI was not chosen as the best index, this index is generally considered a good indicator of the amount of vegetation and, hence, is useful in distinguishing vegetation from soil ( Svotwa et al. 2012).
 
Conclusion
Our study showed that persulfate digestion does not produce the large quantities of toxic waste associated with Kjeldahl digestion and it requires a minimum of specialized equipments. In comparison to the used indices in this study (NDVI, GI, SAVI2, GRI), the GI index demonstrated a better correlation with canopy nitrogen content. This good relationship is not surprising as GI has been developed for chlorophyll estimation which has a direct relationship with nitrogen. Although, the amount of N is only 26% of leaf dry weight but surprisingly it has strong effect on reflected radiation.