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SWIR-based spectral indices for assessing nitrogen content in potato fields
Authors:I. Herrmann  D. J. Bonfil  Y. Cohen  V. Alchanatis
Affiliation:1. The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev , Sede Boker Campus , 84990 , Israel;2. Field Crops and Natural Resources Department , Agricultural Research Organization, Gilat Research Centre , Israel;3. Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Centre , Bet Dagan , Israel
Abstract:Nitrogen (N) is an essential element in plant growth and productivity, and N fertilizer is therefore of prime importance in cultivated crops. The amount and timing of N application has economic and environmental implications and is consequently considered to be an important issue in precision agriculture. Spectral indices derived from handheld, airborne and spaceborne spectrometers are used for assessing N content. The majority of these indices are based on indirect indicators, mostly chlorophyll content, which is proven to be physiologically linked to N content. The current research aimed to explore the performance of new N spectral indices dependent upon the shortwave infrared (SWIR) region (1200–2500 nm), and particularly the 1510 nm band because it is related directly to N content. Traditional nitrogen indices (NIs) and four proposed new SWIR-based indices were tested with canopy-level spectral data obtained during two growing seasons in potato experimental plots in the northwest Negev, Israel. Above-ground biomass samples were collected at the same location of the spectral sampling to provide in-situ N content data. The performance of all indices was evaluated by three methods: (1) correlations between the existing and proposed indices and N as well as correlations among the indices themselves; (2) the root mean square error prediction (RMSEP) of the N content; and (3) the indices relative sensitivity (S r) to the N content. The results reveal a firm advantage for the proposed SWIR-based indices in their ability to predict, and in their sensitivity to, N content. The best index is one that combines information from the 1510 and 660 nm bands but no significant differences were found among the new SWIR-based indices.
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