El uso de la teledetección de alta resolución como herramienta para realizar un manejo eficiente del riego en viñedos

The use of plant-based indicators for irrigation management has been widely studied. However, the high number of measurements necessary to identify spatial variability in orchards makes this system difficult to be carried out in large commercial areas. The alternative may be the use of remote sensing. Development of high resolution airborne sensors during the last years brings about new possibilities for detecting plant water status remotely in large areas, and therefore to conduct a more efficient irrigation management for water use. The aim of this PhD thesis is the development of a tool for vineyard spatial variability management, by using high resolution remote sensing imagery. To achieve it, two methodologies to re-design irrigation sectors were firstly compared, with the goal of reducing yield variability. Methods were based on using structural vegetative indices such as Plant Cell Density (PCD) obtained from multispectral images, and leaf water potential measurements (ΨL). It is also presented the development of Crop Water Stress Index (CWSI) for the four grapevine varieties Pinot-noir, Chardonnay, Syrah and Tempranillo, as a tool for quantify vine water status with remote sensing thermal imagery. CWSI was empirically developed with infrared temperature sensors to subsequently generate CWSI maps by acquiring high resolution thermal images. CWSI was developed and validated with ΨL measurements at different phenological stages. Effectiveness aspects to consider such as the optimal moment of the day to detect vine water status with aerial thermal images, the minimum spatial resolution required, or the most appropriated aerial platform, were also studied in this PhD thesis. The implementation of this technology in viticulture will permit to make a more efficient irrigation management taking into account vineyard spatial variability.El uso de indicadores del estado hídrico de los cultivos para la optimización del riego en cultivos leñosos ha sido ampliamente estudiado. Sin embargo, el elevado número de puntos de medidas necesarios para caracterizar la variabilidad espacial de una parcela, hace que sea un sistema de difícil aplicación en grandes extensiones comerciales. La alternativa se basa en el uso de la teledetección. Con el desarrollo en los últimos años de sensores aerotransportados de alta resolución, se abren nuevas posibilidades para detectar el estado hídrico de los cultivos en grandes extensiones y poder realizar un manejo del riego más eficiente. Esta tesis doctoral tiene como principal objetivo desarrollar una herramienta que permita manejar la variabilidad espacial de los viñedos, mediante la utilización de la teledetección de alta resolución. Para tal fin, en primer lugar se han comparado dos metodologías para re-definir los sectores de riego, con el objetivo de disminuir la variabilidad productiva. Los métodos se basaron en el uso de índices estructurales de vegetación, tales como el Plant Cell Density (PCD) obtenidos a partir de imágenes multiespectrales, y con medidas del potencial hídrico de hoja (ΨL). Se presenta también el desarrollo del Crop Water Stress Index (CWSI) en las cuatro variedades de viña Pinot-noir, Chardonnay, Syrah y Tempranillo, como herramienta para cuantificar el estado hídrico mediante la teledetección térmica. El CWSI se desarrolló empíricamente con sensores de temperatura infrarrojo para posteriormente poder generar mapas de CWSI mediante la adquisición de imágenes aéreas térmicas de alta resolución. El CWSI se desarrolló y validó con medidas de ΨL en las distintas fases fenológicas. Aspectos de operatividad, tales como el momento idóneo del día para detectar el estado hídrico mediante imágenes aéreas térmicas, la resolución espacial mínima requerida, o la plataforma aérea más adecuada, también han sido estudiados en esta tesis. La implementación de esta tecnología en la viticultura permitirá realizar un manejo del riego más eficiente teniendo en cuenta la variabilidad espacial del estado hídrico en un viñedo

Performance of the Two-Source Energy Balance (TSEB) Model as a Tool for Monitoring the Response of Durum Wheat to Drought by High-Throughput Field Phenotyping

The current lack of efficient methods for high throughput field phenotyping is a constraint on the goal of increasing durum wheat yields. This study illustrates a comprehensive methodology for phenotyping this crop's water use through the use of the two-source energy balance (TSEB) model employing very high resolution imagery. An unmanned aerial vehicle (UAV) equipped with multispectral and thermal cameras was used to phenotype 19 durum wheat cultivars grown under three contrasting irrigation treatments matching crop evapotranspiration levels (ETc): 100%ETc treatment meeting all crop water requirements (450 mm), 50%ETc treatment meeting half of them (285 mm), and a rainfed treatment (122 mm). Yield reductions of 18.3 and 48.0% were recorded in the 50%ETc and rainfed treatments, respectively, in comparison with the 100%ETc treatment. UAV flights were carried out during jointing (April 4th), anthesis (April 30th), and grain-filling (May 22nd). Remotely-sensed data were used to estimate: (1) plant height from a digital surface model (H, R2 = 0.95, RMSE = 0.18m), (2) leaf area index from multispectral vegetation indices (LAI, R2 = 0.78, RMSE = 0.63), and (3) actual evapotranspiration (ETa) and transpiration (T) through the TSEB model (R2 = 0.50, RMSE = 0.24 mm/h). Compared with ground measurements, the four traits estimated at grain-filling provided a good prediction of days from sowing to heading (DH, r = 0.58–0.86), to anthesis (DA, r = 0.59–0.85) and to maturity (r = 0.67–0.95), grain-filling duration (GFD, r = 0.54–0.74), plant height (r = 0.62–0.69), number of grains per spike (NGS, r = 0.41–0.64), and thousand kernel weight (TKW, r = 0.37–0.42). The best trait to estimate yield, DH, DA, and GFD was ETa at anthesis or during grain filling. Better forecasts for yield-related traits were recorded in the irrigated treatments than in the rainfed one. These results show a promising perspective in the use of energy balance models for the phenotyping of large numbers of durum wheat genotypes under Mediterranean conditions.info:eu-repo/semantics/publishedVersio

A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation

This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions.This study is supported by the INVITE project (agreement No. 817970), funded by the Horizon 2020 Framework Program of the European Union. This study received support from a Consolidated Research Group grant awarded to the Institut de Recerca i Tecnologia Agroalimentàries (IRTA) under grant number 2021 SGR 01429 (Technologies and crop solutions for drought mitigation—AGRI DROUGHT HUB).info:eu-repo/semantics/publishedVersio

Feasibility of Using the Two-Source Energy Balance Model (TSEB) with Sentinel-2 and Sentinel-3 Images to Analyze the Spatio-Temporal Variability of Vine Water Status in a Vineyard

In viticulture, detailed spatial information about actual evapotranspiration (ETa) and vine water status within a vineyard may be of particular utility when applying site-specific, precision irrigation management. Over recent decades, extensive research has been carried out in the use of remote sensing energy balance models to estimate and monitor ETa at the field level. However, one of the major limitations remains the coarse spatial resolution in the thermal infrared (TIR) domain. In this context, the recent advent of the Sentinel missions of the European Space Agency (ESA) has greatly improved the possibility of monitoring crop parameters and estimating ETa at higher temporal and spatial resolutions. In order to bridge the gap between the coarse-resolution Sentinel-3 thermal and the fine-resolution Sentinel-2 shortwave data, sharpening techniques have been used to downscale the Sentinel-3 land surface temperature (LST) from 1 km to 20 m. However, the accurate estimates of high-resolution LST through sharpening techniques are still unclear, particularly when intended to be used for detecting crop water stress. The goal of this study was to assess the feasibility of the two-source energy balance model (TSEB) using sharpened LST images from Sentinel-2 and Sentinel-3 (TSEB-PTS2+3) to estimate the spatio-temporal variability of actual transpiration (T) and water stress in a vineyard. T and crop water stress index (CWSI) estimates were evaluated against a vine water consumption model and regressed with in situ stem water potential (Ψstem). Two different TSEB approaches, using very high-resolution airborne thermal imagery, were also included in the analysis as benchmarks for TSEB-PTS2+3. One of them uses aggregated TIR data at the vine+inter-row level (TSEB-PTairb), while the other is based on a contextual method that directly, although separately, retrieves soil and canopy temperatures (TSEB-2T). The results obtained demonstrated that when comparing airborne Trad and sharpened S2+3 LST, the latter tend to be underestimated. This complicates the use of TSEB-PTS2+3 to detect crop water stress. TSEB-2T appeared to outperform all the other methods. This was shown by a higher R2 and slightly lower RMSD when compared with modelled T. In addition, regressions between T and CWSI-2T with Ψstem also produced the highest R2.info:eu-repo/semantics/publishedVersio

Post-Harvest Regulated Deficit Irrigation in Chardonnay Did Not Reduce Yield but at Long-Term, It Could Affect Berry Composition

Future increases in temperatures are expected to advance grapevine phenology and shift ripening to warmer months, leaving a longer post-harvest period with warmer temperatures. Accumulation of carbohydrates occurs during post-harvest, and has an influence on vegetative growth and yield in the following growing season. This study addressed the possibility of adopting regulated deficit irrigation (RDI) during post-harvest in Chardonnay. Four irrigation treatments during post-harvest were applied over three consecutive seasons: (i) control (C), with full irrigation; (ii) low regulated deficit irrigation for sparkling base wine production (RDIL SP), from harvest date of sparkling base wine, irrigation when stem water potential (Ψstem) was less than −0.9 MPa; (iii) mild regulated deficit irrigation for sparkling base wine production (RDIM SP), from harvest date of sparkling base wine, irrigation when Ψstem was less than −1.25 MPa; (iv) mild regulated deficit irrigation for wine production (RDIM W), from harvest data of wine, irrigation when Ψstem was less than −1.25 MPa. Root starch concentration in full irrigation was higher than under RDI. Yield parameters did not differ between treatments, but differences in berry composition were detected. Considering that the desirable berry composition attributes of white varieties are high in titratable acidity, it would seem inappropriate to adopt RDI strategy during post-harvest. However, in a scenario of water restriction, it may be considered because there was less impact on yield and berry composition than if RDI had been adopted during pre-harvest.info:eu-repo/semantics/publishedVersio

Using Unmanned Aerial Vehicle and Ground-Based RGB Indices to Assess Agronomic Performance of Wheat Landraces and Cultivars in a Mediterranean-Type Environment

The adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are expected to increase yield instability. Remote sensing has been of growing interest in breeding programs since it is a cost-effective technology useful for assessing the canopy structure as well as the physiological traits of large genotype collections. The purpose of this study was to evaluate the use of a 4-band multispectral camera on-board an unmanned aerial vehicle (UAV) and ground-based RGB imagery to predict agronomic traits as well as quantify the best estimation of leaf area index (LAI) in rainfed conditions. A collection of 365 bread wheat genotypes, including 181 Mediterranean landraces and 184 modern cultivars, was evaluated during two consecutive growing seasons. Several vegetation indices (VI) derived from multispectral UAV and ground-based RGB images were calculated at different image acquisition dates of the crop cycle. The modified triangular vegetation index (MTVI2) proved to have a good accuracy to estimate LAI (R2 = 0.61). Although the stepwise multiple regression analysis showed that grain yield and number of grains per square meter (NGm2) were the agronomic traits most suitable to be predicted, the R2 were low due to field trials were conducted under rainfed conditions. Moreover, the prediction of agronomic traits was slightly better with ground-based RGB VI rather than with UAV multispectral VIs. NDVI and GNDVI, from multispectral images, were present in most of the prediction equations. Repeated measurements confirmed that the ability of VIs to predict yield depends on the range of phenotypic data. The current study highlights the potential use of VI and RGB images as an efficient tool for high-throughput phenotyping under rainfed Mediterranean conditions.info:eu-repo/semantics/publishedVersio

Remote Sensing Energy Balance Model for the Assessment of Crop Evapotranspiration and Water Status in an Almond Rootstock Collection

One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.info:eu-repo/semantics/publishedVersio

Evaluation of transpiration in different almond production systems with two-source energy balance models from UAV thermal and multispectral imagery

A growing number of intensive irrigated production systems of the almond crop have been established in recent years. However, there is little information regarding the crop water requirements. Remote sensing-based models such as the two-source energy balance (TSEB) have proven to be reliable ways to accurately estimate actual crop evapotranspiration. However, few efforts have been made to validate the transpiration with sap flow measurements in woody row crops with different production systems and water status. In this study, the TSEB Priestley-Taylor (TSEB-PT) and contextual approach (TSEB-2T) models were assessed to estimate canopy transpiration. In addition, the effect of applying a basic clumping index for heterogeneous randomly placed clumped canopies and a rectangular hedgerow clumping index on the TSEB transpiration estimation was assessed. The TSEB inputs were obtained from high resolution multispectral and thermal imagery using an unmanned aerial vehicle. The leaf area index (LAI), stem water potential (Ψstem) and fractional intercepted photosynthetically active radiation (fIPAR) were also measured. Significant differences were observed in transpiration between production systems and irrigation treatments. The combined use of the TSEB-2T with the C&N-R transmittance model gave the best transpiration estimations for all production systems and irrigation treatments. The use of in situ PAR transmittance in the TSEB-2T model significantly improved the root mean squared error. Thus, the better agreement observed with the TSEB when using the C&N-R model and in situ PAR transmittance highlights the importance of improving radiative transfer models for shortwave canopy transmittance, especially in woody row crops.This research was supported by the PRIMA ALTOS project (No. PCI2019-103649) funded by the Ministry of Science, Innovation and Universities of the Spanish government and by the internal IRTA's scholarship. The authors would also like to thank all the Efficient Use of Water in Agriculture program team, at the IRTA, for their technical support, as well as the Horizon 2020 Research and Innovation Program (H2020) of the European Commission, in the context of the Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE) action and ACCWA project: grant agreement No.: 823965. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.info:eu-repo/semantics/publishedVersio