An Unmanned Aerial System (UAS) for concurrent measurements of solar-induced chlorophyll fluorescence and hyperspectral reflectance toward improving crop monitoring

Partitioning Net Ecosystem Exchange (NEE) of CO2 Using Solar‐Induced Chlorophyll Fluorescence (SIF)

“…SIF is light emitted from excited chlorophyll molecules upon absorption of solar photons by plants. This unique mechanistic constraint, combined with growing volumes of SIF measurements from ground to satellite platforms (e.g., Chang et al., 2020a; Frankenberg et al., 2011; Guanter et al., 2012; Joiner et al., 2011; Sun et al., 2018, 2017; Yang et al., 2015), holds great promise for closing this long‐standing open problem in global ecology.…”

A Review on Applications and Utility of Remote Sensing and Geographic Information Systems in Agriculture and Natural Resource Management

“…A variety of vegetation indices, statistics, and machine learning algorithms, such as deep convolutional neural network and random forest, have been used to reduce the dimensionality of hyperspectral data and extract meaningful information on crop conditions [17,18,19]. Hyperspectral image quantification of solarinduced chlorophyll fluorescence (SIF) has recently been used to quantify photosynthesis, plant nutrients, and biotic and abiotic stressors like disease and water stress [17][18][19][20][21][22].…”

“…A variety of vegetation indices, statistics, and machine learning algorithms, such as deep convolutional neural network and random forest, have been used to reduce the dimensionality of hyperspectral data and extract meaningful information on crop conditions [17,18,19]. Hyperspectral image quantification of solarinduced chlorophyll fluorescence (SIF) has recently been used to quantify photosynthesis, plant nutrients, and biotic and abiotic stressors like disease and water stress [17][18][19][20][21][22]. The suitable spatio-temporal determination necessary for PA is determined by various aspects, including management objectives, field size, and the flexibility of farm equipment to change input application rates (irrigation, fertiliser, pesticide, etc.).…”

Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on wavelet neural network approach using spectral indices

“…Diagnosis of N nutrition in crops based on spectral data has made considerable progress [ 17 ]. The technique has been applied in several crops to obtain crop N nutrition status spectral indices [ 18 – 20 ]. Based on spectral indices, various crop N nutrition monitoring models have been established, and they have achieved a high accuracy.…”