No abstract

Hailstorms and strong winds represent a threat to crops, causing defoliation, lodging and in turn yield losses. Crop damages are nowadays assessed by field inspectors, which implies time demanding assessment and difficulties in deriving estimates over large areas. Hailstones and strong wind damage plants through stem breaking, defoliation and lodging, thus leaf area index (LAI) can be a viable tool to detect and quantify the damage level. Here, hailstorm and strong wind damage was artificially caused in a maize field and compared with NDVI-derived LAI from proximal and remote sensing techniques. Estimated LAI was obtained by a NDVI-derived fractional vegetation cover and calibrated light extinction coefficient. Results showed that estimated LAI from remote sensing was able to identify crop damage, with a clear differentiation between leaf damage levels immediately after the event. Following surveys showed a strong recovering capability of maize leading LAI values of damaged treatments to align to those of the control after about 20 days. Remote sensing techniques, coupled with ground measurements, can become a reference tool to assess site-specific hailstorm and strong wind damage over large areas.

Extreme events such as hailstorms are a cause for concern in agriculture, leading to both economic and food supply losses. Traditional damage estimation techniques have recently been called into question since damages have rarely been quantified at the large-field scale. Damage-estimation methods used by field inspectors are complex and sometimes subjective and hardly account for damage spatial variability. In this work, a normalized difference vegetation index (NDVI)-based parametric method was applied using both unmanned aerial vehicles (UAV) and Sentinel-2 sensors to estimate the leaf area index (LAI) of maize (Zea mays L.) resulting from simulated hail damage. These methods were then compared to the LAI values generated from the Sentinel-2 Biophysical Processor. A two-year experiment (2020–2021) was conducted during the maize cropping season, with hail events simulated during a range of maize developmental stages (the 8th-leaf, flowering, milky and dough stages) using a 0–40% defoliation gradient of damage intensities performed with the aid of specifically designed prototype machines. The results showed that both sensors were able to accurately estimate LAI in a nonstandard damaged canopy while requiring only the calibration of the extinction coefficient $$k(\vartheta )$$ k ( ϑ ) in the case of parametric estimations. In this case, the calibration was performed using 2020 data, providing $$k(\vartheta )$$ k ( ϑ ) values of 0.59 for Sentinel-2 and 0.37 for the UAV sensor. The validation was performed on 2021 data, and showed that the UAV sensor had the best accuracy (R2 of 0.86, root-mean-square error (RMSE) of 0.71). The $$k(\vartheta )$$ k ( ϑ ) value proved to be sensor-specific, accounting for the NDVI retrieval differences likely caused by the different spatial operational scales of the two sensors. NDVI proved effective in parametrically estimating maize LAI under damaged canopy conditions at different defoliation degrees. The parametric method matched the Sentinel-2 biophysical process-generated LAI well, leading to less underestimations and more accurate LAI retrieval.

<p>Variable rate irrigation is usually based on prescription maps delineated according to a static approach. Irrigation rate and timing are optimised by sensor and/or modelling-based methods applied within homogenous zones whose spatial distribution is kept constant during the crop season. The objective of this study was to develop a procedure based on the combination of the crop-energy-water balance model FEST-EWB-SAFY with remote sensing data of vegetation variables and land surface temperature to generate dynamic irrigation prescription maps. The crop-energy-water balance FEST-EWB-SAFY model couples the distributed energy-water balance FEST-EWB, which allows computing continuously in time and distributed in space both soil moisture and evapotranspiration fluxes, and the SAFY (simple model for yield prediction and plant development).</p><p>The model was tested in a 30-ha field cultivated with soybean in 2022 at Ceregnano, in the lower zone of the Po Valley (Italy). Irrigation was provided by 270m long lateral move irrigation machine, equipped with a precision irrigation system with a lateral resolution of 34 m. The model was pixelwise calibrated with remotely sensed land surface temperature (LST, RMSE 1.3 °C) and leaf area index (RMSE 0.45) as well as local measured soil moisture at 10cm and 50cm depth (RMSE 0.04). Four dynamic prescription maps were generated during the season, calculating the pixel-by-pixel difference between the field retention capacity and the daily average of the 50-cm soil moisture profile. Dynamic variable rate irrigation was compared with a conventional irrigation system according to an experimental block design with three replicates and evaluated in terms of crop yield, irrigation volumes and water use efficiency.</p><p>FEST-EWB-SAFY allowed the creation of dynamic maps that captured the crop water requirement variability originated by the interaction of ET, soil properties and field management. Compared with the conventional system, there was a significant increase in water use efficiency, but not in crop yield. These results confirm that the model-based dynamic prescription maps could be used to optimize variable irrigation in highly spatio-temporal dynamic cropping systems</p>

<p>Extreme weather events such as hailstorms represent a threat to crops, causing both economic and food supply losses. Hailstorm intensity is likely to increase in the future pushing more farmers to purchase crop insurances to prevent related economic losses. Currently, insurers mostly rely on field inspectors for crop damage assessments, which can build up limitations such as: (i) partial subjectivity in damage estimations; (ii) inaccuracies in wide-area assessments; (iii) difficulties in accounting for damage spatial variability. Sensors mounted on UAVs (Unmanned Aerial Vehicles) and satellites can fulfill these requirements when coupled with advanced spectral analysis techniques, such as spectral mixture analysis (SMA). In this experiment we applied SMA on UAV hyperspectral images to quantify dead-and-alive organs during the growth of winter wheat (<em>Triticum aestivum </em>L.) and estimate yield loss due to hail damage. The experiment was conducted on a 17-ha field located in the surroundings of Venice (NE Italy). The experiment involved four simulated hail treatments (null, low, medium and high damage) at three plant growing stages (flowering, milky and over-ripe). Treatments were in triplicate for a total of 30 plots, nine sized 60x60 m and 18, 20x20 m. Damages were inflicted using a prototype specifically designed at the University of Padova, consisting of a rotating pole with whips attached and positioned on the back of a tractor. Damage intensity was adjusted with the aid of insurance field inspectors. A UAV M600 Pro (DJI, Shenzhen, China) was equipped with a nanohyperspec (400-1000 nm) camera (Headwall, Boston, USA). Pixel ground resolution was about 0.04 m. UAV surveys were performed after each damage, leaving a period of 7-10 days to the crop for developing a detectable morphologic and physiologic response (e.g., leaf drying, development of necrosis). At each flight, crop samples were collected, and pure spectral signatures of dead and alive stems, leaves and spikes were analyzed using an ASD Fieldspec 4 (Malvern Panalytical Ltd, Malvern, UK) in proximal sensing configuration. SMA algorithm was run on UAV imagery by selecting endmembers composed of intact green plant organs, bare soil and dead spikes, thus allowing for differentiation between damaged and undamaged vegetation. Results showed that increasing yield loss due to hail damage intensity was associated with an increasing number of dead spikes. Proximal-sensed hyperspectral signatures highly differentiated between undamaged and damaged vegetation, especially in the red-edge and chlorophyll absorption (~ 680 nm) regions. In this context, the SMA technique was promising for disentangling dead spikes from alive organs, aiding the area-damaged classification and allowing hyperspectral imagery for a direct estimate of yield losses.</p>