Accurate measurement of leaf chlorophyll concentration (LChl) in the field using a portable chlorophyll meter (PCM) is crucial to support methodology development for mapping the spatiotemporal variability of crop nitrogen status using remote sensing. Several PCMs have been developed to measure LChl instantaneously and non-destructively in the field, however, their readings are relative quantities that need to be converted into actual LChl values using conversion functions. The aim of this study was to investigate the relationship between actual LChl and PCM readings obtained by three PCMs: SPAD-502, CCM-200, and Dualex-4. Field experiments were conducted in 2016 on four crops: corn (Zea mays L.), soybean (Glycine max L. Merr.), spring wheat (Triticum aestivum L.), and canola (Brassica napus L.), at the Central Experimental Farm of Agriculture and Agri-Food Canada in Ottawa, Ontario, Canada. To evaluate the impact of other factors (leaf internal structure, leaf pigments other than chlorophyll, and the heterogeneity of LChl distribution) on the conversion function, a global sensitivity analysis was conducted using the PROSPECT-D model to simulate PCM readings under different conditions. Results showed that Dualex-4 had a better performance for actual LChl measurement than SPAD-502 and CCM-200, using a general conversion function for all four crops tested. For SPAD-502 and CCM-200, the error in the readings increases with increasing LChl. The sensitivity analysis reveals that deviations from the calibration functions are more induced by non-uniform LChl distribution than leaf architectures. The readings of Dualex-4 can have a better ability to restrict these influences than those of the other two PCMs.

Synthetic aperture radar (SAR) is more sensitive to the dielectric properties and structure of the targets and less affected by weather conditions than optical sensors, making it more capable of detecting changes induced by management practices in agricultural fields. In this study, the capability of C-band SAR data for detecting crop seeding and harvest events was explored. The study was conducted for the 2019 growing season in Temiskaming Shores, an agricultural area in Northern Ontario, Canada. Time-series SAR data acquired by Sentinel-1 constellation with the interferometric wide (IW) mode with dual polarizations in VV (vertical transmit and vertical receive) and VH (vertical transmit and horizontal receive) were obtained. interferometric SAR (InSAR) processing was conducted to derive coherence between each pair of SAR images acquired consecutively in time throughout the year. Crop seeding and harvest dates were determined by analyzing the time-series InSAR coherence and SAR backscattering. Variation of SAR backscattering coefficients, particularly the VH polarization, revealed seasonal crop growth patterns. The change in InSAR coherence can be linked to change of surface structure induced by seeding or harvest operations. Using a set of physically based rules, a simple algorithm was developed to determine crop seeding and harvest dates, with an accuracy of 85% (n = 67) for seeding-date identification and 56% (n = 77) for harvest-date identification. The extra challenge in harvest detection could be attributed to the impacts of weather conditions, such as rain and its effects on soil moisture and crop dielectric properties during the harvest season. Other factors such as post-harvest residue removal and field ploughing could also complicate the identification of harvest event. Overall, given its mechanism to acquire images with InSAR capability at 12-day revisiting cycle with a single satellite for most part of the Earth, the Sentinel-1 constellation provides a great data source for detecting crop field management activities through coherent or incoherent change detection techniques. It is anticipated that this method could perform even better at a shorter six-day revisiting cycle with both satellites for Sentinel-1. With the successful launch (2019) of the Canadian RADARSAT Constellation Mission (RCM) with its tri-satellite system and four polarizations, we are likely to see improved system reliability and monitoring efficiency.

Information on crop types derived from remotely sensed images provides valuable input for many applications such as crop growth modeling and yield forecasting. In this paper, a random forest (RF) classifier was used for crop classification using multispectral RapidEye imagery over two study sites, one in north-eastern China and one in eastern Ontario, Canada. Both vegetation indices (VIs) and textural features were derived from the RapidEye imagery and used for classification. A total of 20 VIs, categorized into two groups with and without the red edge (RE) band in an index, were calculated. A total of eight types of textural features were derived using four different window sizes from both the RE and the near-infrared bands. To reduce redundancies among the VIs and textural features, feature selection using the principal component analysis, correlation analysis, and stepwise discriminant analysis was performed. Results showed that the overall classification accuracy was improved by ~7% when the RE indices were combined with the five spectral bands in classification, as compared with that using the five bands alone. When textural information was included, the overall classification accuracy increased by ~6% compared with that using the band reflectance alone. Furthermore, when all the features (band reflectance, VIs, and texture) were used, the overall classification accuracy increased by ~12% compared with that using only the band reflectance. The RF importance measures showed that the RE reflectance was important for classification, as indicated by the high importance for the triangular vegetation index, transformed chlorophyll absorption in reflectance index, and green-rededge normalized difference vegetation index. The gray-level co-occurrence matrix mean is the most useful for classification among the textural features. The study provides a means to feature extraction and selection for crop classification from remote sensing imagery.