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

Assimilation of Sentinel-2 Biophysical Variables into a Digital Twin for the Automated Irrigation Scheduling of a Vineyard

Decision support systems (DSS) are needed to carry out precision irrigation. Key issues in this regard include how to deal with spatial variability and the adoption of deficit irrigation strategies at the field scale. A software application originally designed for water balance-based automated irrigation scheduling locally fine-tuned through the use of sensors has been further developed with the emerging paradigm of both digital twins and the Internet of Things (IoT). The aim of this research is to demonstrate the feasibility of automatically scheduling the irrigation of a commercial vineyard when adopting regulated deficit irrigation (RDI) strategies and assimilating in near real time the fraction of absorbed photosynthetically active radiation (fAPAR) obtained from Sentinel-2 imagery. In addition, simulations of crop evapotranspiration obtained by the digital twin were compared with remote sensing estimates using surface energy balance models and Copernicus-based inputs. Results showed that regression between instantaneous fAPAR and in situ measurements of the fraction of intercepted photosynthetically active radiation (fIPAR) had a coefficient of determination (R2) ranging from 0.61 to 0.91, and a root mean square deviation (RMSD) of 0.10. The conversion of fAPAR to a daily time step was dependent on row orientation. A site-specific automated irrigation scheduling was successfully adopted and an adaptive response allowed spontaneous adjustments in order to stress vines to a certain level at specific growing stages. Simulations of the soil water balance components performed well. The regression between digital twin simulations and remote sensing-estimated actual (two-source energy balance Priestley–Taylor modeling approach, TSEB-PTS2+S3) and potential (Penman–Monteith approach) evapotranspiration showed RMSD values of 0.98 mm/day and 1.14 mm/day, respectively.This research was funded by the projects IRRINTEGRAL (No. RTI2018-099949-R-C21) and DIGISPAC (No. TED2021-131237B-C21), both from the Ministry of Science, Innovation and Universities of the Spanish government.The authors would like to thank all the Efficient Use of Water in Agriculture program team at the IRTA for their technical support, as well as Family Torres winery. This study was undertaken as part of the Horizon 2020 Programme for Research and Innovation (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.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