Water availability is endangering the production, quality, and economic viability of growing wine grapes worldwide. Climate change projections reveal warming and drying trends for the upcoming decades, constraining the sustainability of viticulture. In this context, a great research effort over the last years has been devoted to understanding the effects of water stress on grapevine performance. Moreover, irrigation scheduling and other management practices have been tested in order to alleviate the deleterious effects of water stress on wine production. The current manuscript provides a comprehensive overview of the advances in the research on optimizing water management in vineyards, including the use of novel technologies (modeling, remote sensing). In addition, methods for assessing vine water status are summarized. Moreover, the manuscript will focus on the interactions between grapevine water status and biotic stressors. Finally, future perspectives for research are provided. These include the performance of multifactorial studies accounting for the interrelations between water availability and other stressors, the development of a cost-effective and easy-to-use tool for assessing vine water status, and the study of less-known cultivars under different soil and climate conditions.

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Optimization of Vineyard Water Management: Challenges, Strategies, and Perspectives

Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

Simulating the Leaf Area Index of Rice from Multispectral Images

In this study, we tried to address the applicability of using dynamic remotely sensed data into a static crop model to capture the yield spatiotemporal variability at the field scale. Taking the example of the crop environment resource synthesis for wheat (CERES-wheat), the model was calibrated, improved, and validated using three years of winter wheat field measurement data (growing seasons of 2017–2019). We assimilated the Landsat-based leaf area index (LAI) into the model using the particle filter approach. Four vegetation indices, including NDVI, SAVI, EVI, and EVI-2, were evaluated to identify winter wheat LAI’s best estimator. A linear regression of Landsat-EVI-2 was found to be the most accurate representation of LAI (LAI = 10.08 × EVI-2 − 0.53) with R2 = 0.87, and mean bias error =  − 2.04. The higher LAI accuracy from EVI-2 was attributed to the soil and canopy background noise reduction and accounting for certain atmospheric conditions. Assimilating the LAI based on Landsat-EVI-2 into the CERES model improved the model’s overall performance, particularly for grain yield and biomass simulations. The default model predicted LAImax, grain yield, and biomass at 5.1 cm2 cm−2, 8.3 Mg ha−1, and 14.9 Mg ha−1 with RMSE of 1.44, 0.91 Mg ha−1, and 1.2 Mg ha−1, respectively, while the modified model (using the Landsat-EVI-2 data) predicated these values at 6.6 cm2 cm−2, 9.9 Mg ha−1, and 16.6 Mg ha−1 with RMSE of 0.81, 0.54 Mg ha−1, and 0.62 Mg ha−1, respectively.

Improving CERES-Wheat Yield Forecasts by Assimilating Dynamic Landsat-Based Leaf Area Index: A Case Study in Iran

Water use and soil water balance of Mediterranean tree crops assessed with the SIMDualKc model in orchards of southern Portugal

ABSTRACT The leaf area index (LAI) is an important agroecological physiological parameter affecting vegetation growth. To apply the genetic algorithms neural network model (GANNM) to the remote sensing inversion of winter wheat LAI throughout the growth cycle and based on GaoFen-3 Synthetic aperture radar (GF-3 SAR) images and GaoFen-1 Wide Field of View (GF-1 WFV) images, the Xiangfu District in the east of Kaifeng City, Henan Province, was selected as the testing region. Winter wheat LAI data from five growth stages were combined, and optical and microwave polarization decomposition vegetation index models were used. The backscattering coefficient was extracted by modified water cloud model (MWCM), and the LAI was obtained by MWCM inversion as input factors to construct GANNM to invert LAI. The root mean square error (RMSE) and determination coefficient (R 2) were used as evaluation indicators of the model. The fitting accuracy of winter wheat LAI in five growth stages by GANNM inversion was better than that of the BP neural network model; the R 2 was higher than 0.8, and RMSE was lower than 0.3, indicating that the model could accurately invert the growth status of winter wheat in five growth stages .

https://doi.org/10.1080/17538947.2022.2028913

Ali Keshavarz

Papers

Estimation of evapotranspiration and crop coefficient of drip-irrigated orange trees under a semi-arid climate

Improving CERES-Wheat Yield Forecasts by Assimilating Dynamic Landsat-Based Leaf Area Index: A Case Study in Iran