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Fertilizer overuse is a common phenomenon in global agroforestry production, and this overuse causes ecological destruction. The ability to accurately estimate the nutrient content of plant leaves in real-time would be a wonderful solution to reduce the degree of environmental damage. In recent years, remote sensing technology has been widely used in the diagnosis of crop nutrition in many countries. Most studies focus on optimal band selection or create new vegetation indices, but these studies have ignored the random impact of natural environmental factors on the estimated results. This paper proposed an estimation model of total nitrogen content (TNC) in sandalwood leaves that takes sampling season and site conditions as the dummy variable and random effect, respectively. Three forestry farms with different locations and site conditions were selected as study areas to enhance the universality of this model. Multispectral images of leaves were obtained using a low-cost five-band camera (RedEdge3, MicaSense, USA), and the experimental results indicate the following: (1) the growth of the tree height, crown width and stem effectively increased under the medium gradient level (N2), whereas a high gradient level (N3) significantly promoted all aspects except tree height; (2) the mean and variance of some image texture features of the G, RE and NIR band were significantly correlated with TNC at the 0.05 and 0.01 levels, and the texture mean value index (TMVI) proposed in this paper can improve the correlation with TNC; and (3) the results obtained using the nonlinear mixed-effects model with dummy variables improved the fitting degree and estimation accuracy compared with results of SVR and BPNN. This study demonstrates the advantages of using the nonlinear mixed-effects model with dummy variables to obtain a more reliable estimation model for the nutritional diagnosis of rare tree species. [ABSTRACT FROM AUTHOR]

This paper investigates the angular effects causing spectral distortions in multi-angle remote sensing imagery. We study two WorldView-2 multispectral in-track sequences acquired over the cities of Atlanta, USA, and Rio de Janeiro, Brazil, consisting of 13 and 20 co-located images, respectively. The sequences possess off-nadir acquisition angles up to 47.5° and bear markedly different sun-satellite configurations with respect to each other. Both scenes comprise classic urban structures such as buildings of different size, road networks, and parks. First, we quantify the degree of distortion affecting the sequences by means of a non-linear measure of distance between probability distributions, the Maximum Mean Discrepancy. Second, we assess the ability of a classification model trained on an image acquired at a certain view angle to predict the land-cover of all the other images in the sequence. The portability across the sequence is investigated for supervised classifiers of different nature by analyzing the evolution of the classification accuracy with respect to the off-nadir look angle. For both datasets, the effectiveness of physically- and statistically-based normalization methods in obtaining angle-invariant data spaces is compared and synergies are discussed. The empirical results indicate that, after a suitable normalization (histogram matching, atmospheric compensation), the loss in classification accuracy when using a model trained on the near-nadir image to classify the most off-nadir acquisitions can be reduced to as little as 0.06 (Atlanta) or 0.03 (Rio de Janeiro) Kappa points when using a SVM classifier. [ABSTRACT FROM AUTHOR]