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Synthetic Aperture Radar (SAR) data have been incorporated in operational crop inventories with many advantages, particularly the availability of data under cloudy conditions and the structural information that is gained for agricultural vegetation. The Joint Experiment for Crop Assessment and Monitoring (JECAM) is the research and development branch of GEOGLAM (Group on Earth Observations Global Agricultural Monitoring), a G20 initiative to improve the global monitoring of agriculture through the use of Earth Observation (EO) data and remote sensing. The JECAM SAR Inter-Comparison Experiment, led by Agriculture and Agri-Food Canada (AAFC), is a multi-year, multi-partner project that aims to compare global methods for SAR crop monitoring and inventory. AAFC's operational SAR/optical crop inventory decision tree-based methodology has been successfully used to classify the agriculture extent of Canada since 2011. Data sets of RADARSAT-2 and Sentinel-1 SAR imagery, and optical imagery including Landsat8 and Sentinel-2 were prepared for this experiment using a suite of best practices to ensure consistency across 13 participating sites. The results from applying AAFC's operational crop inventory methodology to the other JECAM partners' sites will be discussed for optimized data sets of: one SAR and one optical image per month of the growing season; a SAR only data set utilizing all available C-band data for a growing season; and for a “kitchen sink” data set where all available SAR and optical data for a growing season were included in the classifier. Preliminary results from other JECAM Partners' crop classification methodologies will be discussed. Finally, there will be a brief discussion on the next two components of the JECAM SAR Inter-Comparison Experiment, including the use of compact polarimetry parameters for crop identification and monitoring, and the use of multi-frequency SAR for crop classification.

Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constellations of satellites – including the RADARSAT Constellation Mission (RCM) and Sentinel-1A/B, both available under free and open data policies – can be used to create stacks of dense seasonal C-band Synthetic Aperture Radar (SAR) data. Yet to date, the contribution of SAR imagery to operational crop mapping is often limited to that of a gap-filler, compensating for optical data obscured by clouds. The Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR Inter-Comparison Experiment is a multi-year, multi-partner project focused on evaluating methods for SAR-based crop classification. Stacks of dense time-series SAR imagery, from RADARSAT-2 and Sentinel-1 satellites, were acquired for 10 sites located in six countries. Decision Tree (DT) and Random Forest (RF) classification methodologies were applied to these SAR data-stacks, as well as to data-stacks of optical only, and optimized SAR/optical data combinations. For the dense time-series SAR stacks, overall classification accuracies above 85% and 80% were obtained for 6 of 10 and 8 of 10 sites, respectively. For maize, the SAR-only data delivered user’s and producer’s accuracies greater than 90% for half the sites. For soya bean, accuracies greater than 80% were reported for 5 of 9 sites and classification accuracies were greater than 80% for wheat on half the sites. Classification results were influenced by the mix and number of agriculture classes present at each site, the available SAR imagery, as well as the training and validation data sets for individual crop types. These results have important operational implications for regions of the world dominated by cloudy conditions and the lack of adequate amounts of optical imagery to support satellite-based crop monitoring.

Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constellations of satellites – including the RADARSAT Constellation Mission (RCM) and Sentinel-1A/B, both available under free and open data policies – can be used to create stacks of dense seasonal C-band Synthetic Aperture Radar (SAR) data. Yet to date, the contribution of SAR imagery to operational crop mapping is often limited to that of a gap-filler, compensating for optical data obscured by clouds. The Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR Inter-Comparison Experiment is a multi-year, multi-partner project focused on evaluating methods for SAR-based crop classification. Stacks of dense time-series SAR imagery, from RADARSAT-2 and Sentinel-1 satellites, were acquired for 10 sites located in six countries. Decision Tree (DT) and Random Forest (RF) classification methodologies were applied to these SAR data-stacks, as well as to data-stacks of optical only, and optimized SAR/optical data combinations. For the dense time-series SAR stacks, overall classification accuracies above 85% and 80% were obtained for 6 of 10 and 8 of 10 sites, respectively. For maize, the SAR-only data delivered user’s and producer’s accuracies greater than 90% for half the sites. For soya bean, accuracies greater than 80% were reported for 5 of 9 sites and classification accuracies were greater than 80% for wheat on half the sites. Classification results were influenced by the mix and number of agriculture classes present at each site, the available SAR imagery, as well as the training and validation data sets for individual crop types. These results have important operational implications for regions of the world dominated by cloudy conditions and the lack of adequate amounts of optical imagery to support satellite-based crop monitoring.

Activation of the cAMP pathway is one of the common mechanisms underlying long-term potentiation (LTP). In the Drosophila mushroom body, simultaneous activation of odor-coding Kenyon cells (KCs) and reinforcement-coding dopaminergic neurons activates adenylyl cyclase in KC presynaptic terminals, which is believed to trigger synaptic plasticity underlying olfactory associative learning. However, learning induces long-term depression (LTD) at these synapses, contradicting the universal role of cAMP as a facilitator of transmission. Here, we develop a system to electrophysiologically monitor both short-term and long-term synaptic plasticity at KC output synapses and demonstrate that they are indeed an exception where activation of the cAMP/protein kinase A pathway induces LTD. Contrary to the prevailing model, our cAMP imaging finds no evidence for synergistic action of dopamine and KC activity on cAMP synthesis. Furthermore, we find that forskolin-induced cAMP increase alone is insufficient for plasticity induction; it additionally requires simultaneous KC activation to replicate the presynaptic LTD induced by pairing with dopamine. On the other hand, activation of the cGMP pathway paired with KC activation induces slowly developing LTP, proving antagonistic actions of the two second-messenger pathways predicted by behavioral study. Finally, KC subtype-specific interrogation of synapses reveals that different KC subtypes exhibit distinct plasticity duration even among synapses on the same postsynaptic neuron. Thus, our work not only revises the role of cAMP in synaptic plasticity by uncovering the unexpected convergence point of the cAMP pathway and neuronal activity, but also establishes the methods to address physiological mechanisms of synaptic plasticity in this important model., Competing Interests: Competing interests The authors report no conflicts of interests.