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Nitrogen is an essential nutrient for citrus growth. Thus, the chemical analysis of leaf tissues is needed to determine nitrogen in the traditional agronomic method, which is time consuming, labor intensive, and costly. Satellite remote sensing (RS) can quickly acquire multispectral images of large-scale orchards and thus can support low-cost and periodic monitoring of nitrogen content in orchards. RS data have been widely used for the monitoring of nitrogen content in various crops and performed quite well in related researches. However, few studies have been conducted to evaluate the leaf nitrogen content (LNC) of citrus on the basis of the data acquired by satellite RS. In this study, Landsat 8 RS image data are used to estimate the distribution of LNC in an orchard, and the effectiveness of different estimation methods for monitoring LNC value is studied. Linear regression, partial least square regression (PLSR), support vector regression (SVR), random forest regression (RF), and deep neural network (DNN) models are constructed and compared. Experimental results demonstrate the feasibility of using satellite RS data in determining LNC in sugar citrus. In terms of evaluating LNC, the PLSR algorithm outperforms other algorithms in testing data, reaching a determination coefficient of 0.864, a root mean squared error of 1.217, and a mean relation error of 3.5%. An accurate spatial distribution of nitrogen content in an orchard can be obtained by our model, which can be used to provide powerful support for the practical management and operation of the orchard.
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