The HyPlant imaging spectrometer is a high-performance airborne instrument consisting of two sensor modules. The DUAL module records hyperspectral data in the spectral range from 400–2500 nm, which is useful to derive biochemical and structural plant properties. In parallel, the FLUO module acquires data in the red and near infrared range (670–780 nm), with a distinctly higher spectral sampling interval and finer spectral resolution. The technical specifications of HyPlant FLUO allow for the retrieval of sun-induced chlorophyll fluorescence (SIF), a small signal emitted by plants, which is directly linked to their photosynthetic efficiency. The combined use of both HyPlant modules opens up new opportunities in plant science. The processing of HyPlant image data, however, is a rather complex procedure, and, especially for the FLUO module, a precise characterization and calibration of the sensor is of utmost importance. The presented study gives an overview of this unique high-performance imaging spectrometer, introduces an automatized processing chain, and gives an overview of the different processing steps that must be executed to generate the final products, namely top of canopy (TOC) radiance, TOC reflectance, reflectance indices and SIF maps.

Chlorophyll fluorescence (ChlF) information offers a deep insight into the plant physiological status by reason of the close relationship it has with the photosynthetic activity. The unmanned aerial systems (UAS)-based assessment of solar induced ChlF (SIF) using non-imaging spectrometers and radiance-based retrieval methods, has the potential to provide spatio-temporal photosynthetic performance information at field scale. The objective of this manuscript is to report the main advances in the development of UAS-based methods for SIF retrieval with non-imaging spectrometers through the latest scientific contributions, some of which are being developed within the frame of the Training on Remote Sensing for Ecosystem Modelling (TRuStEE) program. Investigations from the Universities of Edinburgh (School of Geosciences) and Tasmania (School of Technology, Environments and Design) are first presented, both sharing the principle of the spectroradiometer optical path bifurcation throughout, the so called ‘Piccolo-Doppio’ and ‘AirSIF’ systems, respectively. Furthermore, JB Hyperspectral Devices’ ongoing investigations towards the closest possible characterization of the atmospheric interference suffered by orbital platforms are outlined. The latest approach focuses on the observation of one single ground point across a multiple-kilometer atmosphere vertical column using the high altitude UAS named as AirFloX, mounted on a specifically designed and manufactured fixed wing platform: ‘FloXPlane’. We present technical details and preliminary results obtained from each instrument, a summary of their main characteristics, and finally the remaining challenges and open research questions are addressed. On the basis of the presented findings, the consensus is that SIF can be retrieved from low altitude spectroscopy. However, the UAS-based methods for SIF retrieval still present uncertainties associated with the current sensor characteristics and the spatio-temporal mismatching between aerial and ground measurements, which complicate robust validations. Complementary studies regarding the standardization of calibration methods and the characterization of spectroradiometers and data processing workflows are also required. Moreover, other open research questions such as those related to the implementation of atmospheric correction, bidirectional reflectance distribution function (BRDF) correction, and accurate surface elevation models remain to be addressed.

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 ≈ 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.

Energy is required in all agricultural activities. Diagramming material flows needed by crop production systems supports the proper analysis of energy flows interactions within a system’s boundaries. The latter complemented with an economic analysis gives a clear view of how beneficial a new practice within a crop cycle is—in this case, the variable slope (VS) land leveling (LL) operation. VS is a global navigation satellite system (GNSS, with real time kinematics—RTK—accuracy) LL technique used to create a smooth continuous surface with a constant slope, by cutting and filling topsoil layers only in those points presenting “anomalies” of micro-relief which make the movement of water difficult. This operation is important for paddy production since: (i) it enables to crop during dry seasons by harnessing the water of rivers and wells, and (ii) improves the production during rainy seasons, by allowing the farmer to manage the drainage timely and homogeneously. The present study aims to analyze, from the energy perspective, the effects of the VS leveling implementation in a paddy field (located in the Costa Rican Pacific), throughout input (labor, gas oil, etc.) and output (yield and price) data of five consecutive years (2011–2015). A material flow diagram was created representing two scenarios: before and after leveling the land. The materials were converted into energy (MJ ha⁻¹) data, used for the estimation of EROI (energy return on investment), EP (energy productivity) and EB (energy balance) indices, while looking for a clearer understanding of the LL impact on the use of energy within the agroecosystem. Moreover, in order to complement the energy perspective, an economic point of view was considered as well through a profitability analysis where the total gain obtained over the years with LL was compared with that obtained without LL. Results showed that the increase in energy consumed by incorporating VS leveling is compensated by the gradual increase of energy embodied in yield, increasing energy balance (EB) from 26,192 MJ ha⁻¹ up to 91,166 MJ ha⁻¹. Similarly, EROI and EP were duplicated with LL. Economic total gain after incorporating the VS increased from less than 500 USD ha⁻¹ up to 1800 USD ha⁻¹ in the third year after leveling. Yield is more affected under adverse weather conditions with irregular water supply, either limited or excessive; and effects are less pronounced when the yield limiting factor is associated with biotic stress unrelated to irrigation and drainage facts. An environmental positive impact should also be noted, since VS allowed the production benefits of having highly-efficient irrigation and drainage systems, while avoiding major damage to topsoil layers.

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 (<10 m) in forests. Spatial dependencies of small-scale agricultural scenes were in a middle position with approximately 100 m. Moreover, the spatial dependencies were found to be significantly (p = 0.023) linked to the average object size of the ecosystems. This demonstrates the importance of flexible downscaling methods, e.g. in a fractals-based direction (Quiros et al., in press).