Search

Normalized Difference Vegetation Index (NDVI) time series data are used by agricultural agencies for many essential operational crop monitoring programmes. But optical sensors often miss key growth...

Remote sensing has been recognized as a cost-effective way to detect the spatial and temporal variability of crop growth and productivity. In this study, multispectral RapidEye images were used to delineate homogeneous zones of soil and crop development in two fields in Ontario, Canada, one planted with canola (Brassica napus L.) and the other with spring wheat (Triticum aestivum L.). The two fields received different levels of nitrogen (N) treatments during the pre-planting land preparation phase. Soil textures, mineral nitrogen content and crop yield were used to interpret the results of zone delineation. The analysis of variance (ANOVA) tests revealed that the high-resolution RapidEye data, particularly the imagery acquired at peak crop growth stages (i.e. when leaf area index (LAI) is high), provided valuable information for delineating within-field variability of crop growth and yield. Further analysis showed that for both crops, the spatial patterns of crop growth condition varied throughout the growth cycle, revealing different impacts of soil properties and N fertilization on the crops. In particular, during peak growth stage, the within-field variability was most strongly affected by the pre-planting N application and had the strongest correlation with crop yield. These results suggest that high-resolution satellite data (e.g., RapidEye) could assist in making decisions on optimal N fertilization for enhanced crop productivity.

This study explores the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the visible reflectance based (VIS-based) VIs. Seven VIs were derived from multi-temporal RapidEye images to correlate with LAI of two crop species having contrasting leaf structures and canopy architectures: spring wheat (a monocot) and canola (a dicot) in northern Ontario, Canada. The relationship between LAI and the selected VIs (LAI-VI) was characterized using a semi-empirical model. The Markov Chain Monte Carlo (MCMC) sampling method was used to estimate the model parameters, including the extinction coefficient (KVI) and VI value for dense green canopy (VI∞). Results showed that crop-specific regression models were much closer to a generic regression model using the RE-based VIs than using the VIS-based VIs. Furthermore, the joint posterior probability distribution of the KVI and VI∞ of the RE-based VIs tended to converge for the two crops. This suggests that the RE-based VIs are not as sensitive to canopy structure, e.g., the average leaf angle (ALA), as the VIS-based VIs. This is also demonstrated by the sensitivity analyses using both PROSAIL simulations and field measurements. Hence, the RE-based VIs can be used to develop a more generic LAI estimation algorithm for different crops. Further studies are required to assess the impact of soil reflectance and other factors, such as illumination-target-viewing geometries and atmospheric conditions, on LAI retrieval.

Agriculture and Agri-Food Canada (AAFC) delivers annual maps of crops grown across Canada, operationally, using Synthetic Aperture Radar (SAR) and optical satellite data. This study applies the AAFC methodology to sites in Latin America to test performance and adaptability to these cropping systems, using TerraSAR-X and RADARSAT SAR data. Overall classification results are promising (79.4% to 86.0%), but improvements will occur with better matching of SAR collection dates to local growing seasons, and by acquiring more robust field observations. These improvements will be the subject of additional research by AAFC and partner organizations, in this region.

Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter response from vegetation canopies is wavelength dependent. This study evaluates the utility of C-band Sentinel-1B (Sentinel-1) and L-band ALOS-2 (PALSAR) data, collected during the 2019 growing season, for generating accurate active crop extent (crop vs. non-crop) classifications over an agricultural region in western Canada. Evaluations were performed against the Agriculture and Agri-Food Canada satellite-based Annual Cropland Inventory (ACI), an open data product that maps land cover across the extent of Canada’s agricultural land. Classifications were performed using the temporal coefficient of variation (CV) approach, where an optimal crop/non-crop delineating CV threshold (CVthr) is selected according to Youden’s J-statistic. Results show that crop area mapping agreed better with the ACI when using Sentinel-1 data (83.5%) compared to PALSAR (73.2%). Analysis of performance by crop reveals that PALSAR’s poorer performance can be attributed to soybean, urban, grassland, and pasture ACI classes. This study also compared CV values to in situ wet biomass data for canola and soybeans, showing that crops with lower biomass (soybean) had correspondingly lower CV values.

Prolonged periods of wet soil conditions, when present during critical crop development stages, can significantly elevate the risk of some crop diseases. Wet soils in fields of flowering canola are a concern with respect to the development of sclerotinia as this pathogen feeds on the petals of the canola flower. As such, determining if canola is in bloom during periods of high moisture is important in deciding whether to take action to mitigate this disease. In this paper, RADARSAT-2 quad polarization and TerraSAR-X dual polarization Synthetic Aperture Radar (SAR) data were used with a novel dynamic filtering framework to estimate canola growth stages. In this process, a new crop growth stage indicator was developed and SAR polarimetric parameters sensitive to changes in phenology were identified. Model development used multi-year SAR satellite and field data for one site in Manitoba, Canada. The crop growth estimator was then tested on unseen data from three sites, one in each of Canada's Prairie provinces. This independent validation established that the growth estimator was able to accurately determine canola growth stage and date of flowering with high accuracy. Correlation coefficients (r-values) between observed and estimated phenology ranged from 0.91 to 0.96. Given that this method performed well on test data from other sites and years, this approach could be widely adopted for monitoring the development of canola over extended regions.

Information on crop phenological development stages such as emergence, flowering, fruiting, maturing and senescence is essential for crop production surveillance and yield prediction. It has long been related to optical spectral signatures such as the Normalized Difference Vegetation Index (NDVI) or spectral shifts in the red-edge range. In recent years, more efforts have been made to explore the sensitivity of Synthetic Aperture Radar (SAR), particularly polarimetric SAR signatures, to crop biophysical parameters or phenological stages. In this study, phenological metrics of canola (Brassica napus) and spring wheat (Triticum spp.) are related with temporal evolution of polarimetric SAR parameters derived from the C-band RADARSAT-2 full polarimetric SAR data. Both crops are very common in north eastern Ontario, Canada, but have very anatomically different development processes. From multi-temporal RADARSAT-2 data acquired in three consecutive years (2012–2014), significant correlations were observed between a number of SAR polarimetric parameters and the growth parameters of both crops. Strong correlation was observed between plant height and the Alpha angle of the Cloude-Pottier decomposition, with the R2 of 0.91 and 0.66 for canola and wheat, respectively. The R2 increased when the polarimetric parameters were smoothed in the time domain (R2 of 0.98 for canola and 0.88 for wheat). Strong correlation was also observed for the two crops between the effective leaf area index (LAIe) and the Beta angle, and between days-after-seeding (DAS) and a combination of the Alpha and the Beta angles. These findings show that multi-temporal C-band polarimetric SAR parameters could be used for tracking crop phenological development stages.

With increasing demands for renewable energy and dietary vegetable oils, the production of canola has become widespread in recent years. Modeling canola growth and yield is a helpful approach to predict canola responses to various environments, especially under climate change. However, few studies have been performed for predicting growth and yield of canola in Canada. In this study, we evaluated the CSM-CROPGRO-Canola model in Decision Support System for Agrotechnology Transfer v4.6 for simulating spring canola at West Nipissing in Eastern Canada. The model was evaluated using plant and soil data collected from field experiments over three growing seasons (2012–2014). The model could predict the observed crop development and successfully mimic the characteristics of canola regarding light absorption and utilization using combinations of leaves and pods. The accumulations of aboveground biomass were satisfactorily simulated in the life cycle under different nitrogen (N) fertilizer application rates, with a normalized RMSE of 19%. The seed yields were successfully predicted with different N application rates except for an underestimation under zero N application. The underestimation of yield under low N rates was possibly related to the deficiency in the simulated N mineralization that could also be associated with inaccurate input soil data. A better simulation of seed yields under low N application was achieved when the soil organic matter module based on the CENTURY model was used in DSSAT v4.6. The calibrated model simulated soil moisture and inorganic N contents satisfactorily, showing a good performance of the CSM-CROPGRO-Canola model for the study region.

Information on spatial and temporal variability of crop growth status is important for understanding the interaction between plants and environmental conditions. In particular, information at the sub-field level is critical for detecting and combating within-field yield-limiting factors for implementation of precision agriculture. High-resolution optical satellite sensors are useful tools to acquire the necessary information in this regard. This study used multi-temporal RapidEye data to estimate the green effective plant area index (PAI) and leaf chlorophyll content of two field crops, spring wheat ( Triticum aestivum L.) and canola ( Brassica napus L.). The objective was to evaluate the capability of RapidEye imagery for detecting within-field variability of crop growth conditions. Field experiments were carried out in northern Ontario over two consecutive years, 2012 and 2013. Several vegetation indices were selected and derived from the RapidEye data to correlate with crop PAI measured using digital hemispherical photography and leaf chlorophyll content index (CCI) measured using a CCM-200 chlorophyll meter. Results showed that the effective PAI and CCI of the two crops were best correlated with different indices. For effective PAI, the modified triangular vegetation index 2 (MTVI2) was the best for spring wheat (R 2 = 0.74, RMSE = 0.72 and n = 200) whereas the red-edge normalized difference vegetation index (NDVI RE ) was the best for canola (R 2 = 0.83, RMSE = 0.49 and n = 147). For CCI, the chlorophyll vegetation index (CVI) was the best for spring wheat (R 2 = 0.78, RMSE = 4.1 and n = 128) whereas the red-edge chlorophyll index (CI RE ) was the best for canola (R 2 = 0.33, RMSE = 6.4 and n = 88). The study also confirmed that the combined transformed chlorophyll absorption reflectance index/optimized soil adjusted vegetation index (TCARI/OSAVI) was satisfactory for spring wheat leaf chlorophyll content estimation when the crop canopy is not sparse. Using selected vegetation indices, RapidEye data can be used to map the within-field variability of crop growth conditions, which is useful for applications in precision agriculture. The reflectance in the red-edge or the green bands is useful in mapping crop leaf chlorophyll variability.

Accurate crop growth stage estimation is important in precision agriculture as it facilitates improved crop management, pest and disease mitigation and resource planning. Earth observation imagery, specifically Synthetic Aperture Radar (SAR) data, can provide field level growth estimates while covering regional scales. In this paper, RADARSAT-2 quad polarization and TerraSAR-X dual polarization SAR data and ground truth growth stage data are used to model the influence of canola growth stages on SAR imagery extracted parameters. The details of the growth stage modeling work are provided, including a) the development of a new crop growth stage indicator that is continuous and suitable as the state variable in the dynamic estimation procedure; b) a selection procedure for SAR polarimetric parameters that is sensitive to both linear and nonlinear dependency between variables; and c) procedures for compensation of SAR polarimetric parameters for different beam modes. The data was collected over three crop growth seasons in Manitoba, Canada, and the growth model provides the foundation of a novel dynamic filtering framework for real-time estimation of canola growth stages using the multi-sensor and multi-mode SAR data. A description of the dynamic filtering framework that uses particle filter as the estimator is also provided in this paper.

Understanding the spatial variability of soil mineral nitrogen (SMN) and crop growth is an important step for implementing precision nitrogen (N) management technologies for canola production. A 3 ...

The aim of this paper is to assess the accuracy of an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarimetric (FQ) images of an agricultural area in North-eastern Ontario, Canada. Polarimetric images and field data were acquired during the 2011 and 2012 growing seasons. The classification and field data collection focused on the main crop types grown in the region, which include: wheat, oat, soybean, canola and forage. The polarimetric parameters were extracted with PolSAR analysis using both the Cloude–Pottier and Freeman–Durden decompositions. The object-oriented classification, with a single date of PolSAR data, was able to classify all five crop types with an accuracy of 95% and Kappa of 0.93; a 6% improvement in comparison with linear-polarization only classification. However, the time of acquisition is crucial. The larger biomass crops of canola and soybean were most accurately mapped, whereas the identification of oat and wheat were more variable. The multi-temporal data using the Cloude–Pottier decomposition parameters provided the best classification accuracy compared to the linear polarizations and the Freeman–Durden decomposition parameters. In general, the object-oriented classifications were able to accurately map crop types by reducing the noise inherent in the SAR data. Furthermore, using the crop classification maps we were able to monitor crop growth stage based on a trend analysis of the radar response. Based on field data from canola crops, there was a strong relationship between the phenological growth stage based on the BBCH scale, and the HV backscatter and entropy.

Within the last few decades mangrove forests worldwide have been experiencing high annual rates of loss and many of those that remain have undergone considerable degradation. To understand the condition of these forests, various optical remote sensing platforms have been used to map and monitor these wetlands, including the use of these data for biophysical parameter mapping. For many mangrove forests a reliable source of optical imagery is not possible given their location in quasi-permanent cloud cover or smoke covered regions. In such cases it is recommended that Synthetic Aperture Radar (SAR) be considered. The purpose of this investigation was to examine the relationships between various ALOS-PALSAR modes, acquired from eight images, and mangrove biophysical parameter data collected from a black mangrove ( Avicennia germinans ) dominated forest that has experienced considerable degradation. In total, structural data were collected from 61 plots representing the four common stand types found in this degraded forest of the Mexican Pacific: tall healthy mangrove ( n = 17), dwarf healthy mangrove ( n = 15), poor condition mangrove ( n = 13), and predominantly dead mangrove ( n = 16). Based on backscatter coefficients, significant negative correlation coefficients were observed between filtered single polarization ALOS PALSAR (6.25 m) HH backscatter and Leaf Area Index (LAI). When the dead stands were excluded ( n = 45) the strength of these relationships increased. Moreover, significant negative correlation coefficients were observed with stand height, Basal Area (BA) and to a lesser degree with stem density and mean DBH. With the coarser spatial resolution dual-polarization and quad polarization data (12.5 m) only a few, and weaker, correlation coefficients were calculated between the mangrove parameters and the filtered HH backscatter. However, significant negative values were once again calculated for the HH when the 16 dead mangrove stands were removed from the sample. Conversely, strong positive significant correlation coefficients were calculated between the cross-polarization HV backscatter and LAI when the dead mangrove stands were considered. Although fewer in comparison to the HH correlations, a number of VV backscatter based relationships with mangrove parameters were observed from the quad polarization mode and, to a lesser extent, with the one single VV polarization data. In addition to backscatter coefficients, stepwise multiple regression models of the mangrove biophysical parameter data were developed based on texture parameters derived from the grey level co-occurrence matrix (GLCM) of the ALOS data. A similar pattern to the backscatter relationships was observed for models based on the single polarization unfiltered data, with fairly strong coefficients of determination calculated for LAI and stem height when the dead stands were excluded. In contrast, similar coefficients of determination with biophysical parameters were observed for the dual and quad polarization multiple regression models when the dead stands were both included and excluded from the analyses. An estimated mangrove LAI map of the study area, derived from a multiple regression model of the quad polarization texture parameters, showed comparable spatial patterns of degradation to a map derived from higher spatial resolution optical satellite data.

When dead stands are included, the strongest overall relationships between the ultra-fine backscatter incidence angle of ∼32° and the various structural parameters were found using the horizontal-horizontal HH polarization/horizontal-vertical HV polarization ratio. However, if the dead stands are not included, then significant relationships with the ultra-fine data were only calculated with the HH data. Similar results were observed using the corresponding incidence angle ∼33° of the fine beam data. When a shallower incidence angle was considered ∼39°, fewer and weaker relationships were calculated. Moreover, no significant relationships were observed if the dead stands were excluded from the sample at this incidence angle. The highest correlation coefficients using the steepest incidence ∼27° were found with the co-polarized HH, vertical-vertical VV polarization modes. Several polarimetric parameters entropy, pedestal height, surface roughness, alpha angle based on the decomposition of the scattering matrix of the fine beam mode at this incidence angle were also found to be significantly correlated to mangrove structural data. The highest correlation R = 0.71 was recorded for entropy and LAI. When the dead stands were excluded, volume scattering was found to be the most significant polarimetric parameter. Finally, multiple regression models, based on texture measures derived from both the grey level co-occurrence matrix GLCM and the sum and difference histogram SADH of the ultra-fine data, were developed to estimate mangrove parameters. The results indicate that only models derived from the HH data are significant and that several of these were strong predictors of all but stem density.

The potential of a random forest (RF) classifier for radar-only crop classifications was evaluated for an eastern and western Canadian site. Overall classification accuracies were improved by approximately 4%–5% over traditional boosted decision trees with gains of up to 7% in the accuracies of specific classes. Accuracies above 85% were obtained for key crops including canola, soybeans, corn, and wheat. Variable importance measures generated by the RF classifier showed that the most important acquisitions occurred in late August to early September at peak biomass and after wheat harvest. The least important images were acquired in May and mid-July. The HV and VV polarizations had the most significant contributions, while the HH polarization contributed little throughout the season, except in late September when the HH response was largely driven by soil conditions. The sensitivity of three RF parameters (number of training pixels, number of trees, and number of variables to select from at each split) was...