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Conventional methods for crop lodging assessments need accurate ground observations and tend to be laborious. Lodging assessment methods and accuracy can thus be improved using remote sensing data from small unmanned aerial systems (UASs) and low orbiting satellites (LOSs). With such aim, imagery to assess spearmint crop lodging was acquired using a small UAS at two ground sample distances (GSDs) of 0.01 and 0.03 m. Crop surface model (CSM) and six image color features were extracted from small UAS-based data. These features were then classified into not lodged (NL), partially lodged (PL), and lodged (L) groups. Mean and majority feature classes were obtained for 50 regions of interest (ROI) of size 1 m2 each. Features were compared with visual crop lodging ratings using Pearson correlation ( ${r}$ ) and Cohen’s kappa (CK) coefficients. CSM showed higher assessment accuracy with ${r}~\approx $ 0.85 and CK > 0.60. Mean percentage red (%R) was observed to have the strongest correlation with visual ratings ( $r= 0.75$ and CK = 0.40–0.59) followed by mean percentage blue (%B), both at 0.01-m GSD. The percentage of lodging calculated from %R and %B maps was also contrasted with similar estimates from LOS-based imagery at 3.00-m GSD with no statistical differences found at 5% level.

The assessment of large-scale vegetation functioning is essential to improve cropland productivity and monitor natural ecosystem health. The development of remote sensing (RS) technologies over decades made such assessments possible from field- to global-scale. Nevertheless, commonly used reflectance-based RS methods are often not sensitive enough to timely inform preventive or corrective actions. Recent advances on the RS of solar-induced chlorophyll fluorescence (SIF) have opened opportunities for novel approaches of earlier stress detection since SIF was found to be closely linked to photosynthesis. The forthcoming FLuorescence EXplorer (FLEX) satellite mission of the European Space Agency (ESA, to be launched) will offer timely non-aggregated global-scale SIF data at 300 m spatial resolution. Such pixel size, even though unique and accurate enough to monitor processes at biome level, may not be suitable to assess field scale processes. Therefore, the development of methodologies to downscale satellite-SIF information is currently of utmost interest since allowing to increase the spatial resolution of origin observations. A first step to comprehend the characteristics that possible approaches must meet is to understand the magnitude of the spatial variability within a coarse pixel footprint across representative vegetation types. Our study consequently aims to understand the spatial variability within the footprint of a FLEX pixel. We particularly analyze the spatial dynamics of SIF via the near infrared reflectance of vegetation (NIRv) data derived from Sentinel 2, World View- and Geo Eye- (10.0 m, 0.30, 0.40 m pixel-1, respectively) that was suggested as proxy for SIF in absence of environmental stress. With Sentinel 2 based NIRv we focus on four ecosystems, including small and large scale agriculture, pampa and savannah, with World View- and Geo Eye based NIRv, we investigate rain and coniferous forests. The very high resolution of World View- and Geo Eye was required to compute the variograms of forests since they were affected by a nugget effect when using Sentinel-2 images. Investigated ecosystems represent the most abundant vegetation types that the FLEX mission will cover. We also assessed the relation between the spatial dependencies (approximated by the lag of calculated semi-variograms) and the average object size in all the ecosystems. We found largest spatial dependencies (400-600 m) in large-scale agriculture, pampa and savannah and contrasting lower (

Solar-induced fluorescence (SIF) has become a promising remote sensing parameter to quantify actual photosynthesis beyond the ‘greenness' measurements. Despite the great advances in instrumentation to measure canopy SIF, we are still at the beginning of having concepts to quantitatively relate SIF to actual rates of photosynthesis. In this article, we discuss the three elements that are crucial to scale canopy SIF measurements to leaf function, namely (i) canopy structure and its bio-chemical composition determining light absorption, (ii) the functional status of photosynthetic light conversion and fluorescence emission under non-steady state conditions, and (iii) the re-absorption and scattering of the fluorescence signal within the canopy.

The impact of elevated $[\text{CO}_{2}](\mathrm{e}[\text{CO}_{2}])$ in on yield, biomass (BM) and chlorophyll fluorescence (ChlF) was analyzed in three genotypes of common beans (Phaseolus vulgaris L.), a key food-security crop. Active- and passive-sensed ChlF traits acquired by the Light-Induced-Fluorescence-Transient (LIFT), Moni-Pulse-Amplitude-Modulation (MoniPAM), and Fluorescence Box $(\text{FloX})$ instruments were compared. Total biomass increased for all genotypes under $\mathrm{e}[\text{CO}_{2}]$ , but their biomass partitioning significantly differed. The highest yielding genotype under $\mathrm{e}[\text{CO}_{2}]$ also showed the highest photosynthetic activity according to different active-sensed ChlF methods. Furthermore, $\mathrm{e}[\text{CO}_{2}]$ resulted in earlier senescence, which was detected by either satellite- or FloXderived Normalized Difference Vegetation Index (NDVI). Moreover, we observed a significant agreement between MoniPAM- and LIFT-measured ChlF data $(\mathrm{R}^{2}=0.89, p= 0.02)$ , as well as between SIF and FloX measurements $(\mathrm{R}^{2}= 0.62,p=0.03)$ .

Washington State produces about 70% of total fresh market apples in the United States. One of the primary goals of apple breeding programs is the development of new cultivars resistant to devastating diseases such as fire blight. The overall objective of this study was to investigate high-throughput phenotyping techniques to evaluate fire blight disease symptoms in apple trees. In this regard, normalized stomatal conductance data acquired using a portable photosynthetic system, image data collected using RGB and multispectral cameras, and visible-near infrared spectral reflectance acquired using a hyperspectral sensing system, were independently evaluated to estimate the progression of fire blight infection in young apple trees. Sensors with ranging complexity – from simple RGB to multispectral imaging to hyperspectral system – were evaluated to select the most accurate technique for the assessment of fire blight disease symptoms. The proximal multispectral images and visible-near infrared spectral reflectance data were collected in two field seasons (2016, 2017); while, proximal side-view RGB images and multispectral images using unmanned aerial systems were collected in 2017. The normalized stomatal conductance data was correlated with disease severity rating (r = 0.51, P < 0.05). The features extracted from RGB images (e.g., maximum length of senesced leaves, area of senesced leaves, ratio between senesced and healthy leaf area) and multispectral images (e.g., vegetation indices) also demonstrated potential in evaluation of disease rating (|r| > 0.35, P < 0.05). The average classification accuracy achieved using visible-near infrared spectral reflectance data during the classification of susceptible from symptomless groups ranged between 71 and 93% using partial least square regression and quadratic support vector machine. In addition, fire blight disease ratings were compared with normalized difference spectral indices (NDSIs) that were generated from visible-near infrared reflectance spectra. The selected spectral bands in the range 710–2,340 nm used for computing NDSIs showed consistently higher correlation with disease severity rating than data acquired from RGB and multispectral imaging sensors across multiple seasons. In summary, these specific spectral bands can be used for evaluating fire blight disease severity in apple breeding programs and potentially as early fire blight disease detection tool to assist in production systems.

Field pea cultivars are constantly improved through breeding programs to enhance biotic and abiotic stress tolerance and increase seed yield potential. In pea breeding, the Above Ground Biomass (AGBM) is assessed due to its influence on seed yield, canopy closure, and weed suppression. It is also the primary yield component for peas used as a cover crop and/or grazing. Measuring AGBM is destructive and labor-intensive process. Sensor-based phenotyping of such traits can greatly enhance crop breeding efficiency. In this research, high resolution RGB and multispectral images acquired with unmanned aerial systems were used to assess phenotypes in spring and winter pea breeding plots. The Green Red Vegetation Index (GRVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), plot volume, canopy height, and canopy coverage were extracted from RGB and multispectral information at five imaging times (between 365 to 1948 accumulated degree days/ADD after 1 May) in four winter field pea experiments and at three imaging times (between 1231 to 1648 ADD) in one spring field pea experiment. The image features were compared to ground-truth data including AGBM, lodging, leaf type, days to 50% flowering, days to physiological maturity, number of the first reproductive node, and seed yield. In two of the winter pea experiments, a strong correlation between image features and seed yield was observed at 1268 ADD (flowering). An increase in correlation between image features with the phenological traits such as days to 50% flowering and days to physiological maturity was observed at about 1725 ADD in these winter pea experiments. In the spring pea experiment, the plot volume estimated from images was highly correlated with ground truth canopy height (r = 0.83) at 1231 ADD. In two other winter pea experiments and the spring pea experiment, the GRVI and NDVI features were significantly correlated with AGBM at flowering. When selected image features were used to develop a least absolute shrinkage and selection operator model for AGBM estimation, the correlation coefficient between the actual and predicted AGBM was 0.60 and 0.84 in the winter and spring pea experiments, respectively. A SPOT-6 satellite image (1.5 m resolution) was also evaluated for its applicability to assess biomass and seed yield. The image features extracted from satellite imagery showed significant correlation with seed yield in two winter field pea experiments, however, the trend was not consistent. In summary, the study supports the potential of using unmanned aerial system-based imaging techniques to estimate biomass and crop performance in pea breeding programs.