Using NDVI, climate data and machine learning to estimate yield in the Douro wine region

Barriguinha, A., Jardim, B., De Castro Neto, M., & Gil, A. (2022). Using NDVI, climate data and machine learning to estimate yield in the Douro wine region. International Journal of Applied Earth Observation and Geoinformation, 114(November), 1-14. [103069]. https://doi.org/10.1016/j.jag.2022.103069 -- Funding: The authors gratefully acknowledge: IVDP - Instituto dos Vinhos do Douro e do Porto, IP (Institute of Douro and Port Wines) (https://www.ivdp.pt/en), for providing historical data related to wine grape production for the entire DDR at the parish level; IPMA - Instituto Portuguˆes do Mar e da Atmosfera, IP (Portuguese Institute for Sea and Atmosphere)Estimating vineyard yield in advance is essential for planning and regulatory purposes at the regional level, with growing importance in a long-term scenario of perceived climate change. With few tools available, the current study aimed to develop a yield estimation model based on remote sensing and climate data with a machine-learning approach. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The study was conducted in the Douro Demarcated Region in Portugal over the period 2016–2021 using yield data from 169 administrative areas that cover 250,000 ha, in which 43,000 ha of the vineyard are in production. The optimal combination of input features, with an Mean Absolute Error (MAE) of 672.55 kg/ha and an Mean Squared Error (MSE) of 81.30 kg/ha, included the NDVI, Temperature, Relative Humidity, Precipitation, and Wind Intensity. The model was tested for each year, using it as the test set, while all other years were used as input to train the model. Two different moments in time, corresponding to FLO (flowering) and VER (veraison), were considered to estimate in advance wine grape yield. The best prediction was made for 2020 at VER, with the model overestimating the yield per hectare by 8 %, with the average absolute error for the entire period being 17 %. The results show that with this approach, it is possible to estimate wine grape yield accurately in advance at different scales.publishersversionpublishe

Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards

Irrigation in the Central Valley of California is essential for successful wine grape production. With reductions in water availability in much of California due to drought and competing water-use interests, it is important to optimize irrigation management strategies. In the current study, we investigate the utility of satellite-derived maps of evapotranspiration (ET) and the ratio of actual-to-reference ET (fRET) based on remotely sensed land-surface temperature (LST) imagery for monitoring crop water use and stress in vineyards. The Disaggregated Atmosphere Land EXchange Inverse (ALEXI/DisALEXI) surface-energy balance model, a multi-scale ET remote-sensing framework with operational capabilities, is evaluated over two Pinot noir vineyard sites in central California that are being monitored as part of the Grape Remote-Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). A data fusion approach is employed to combine ET time-series retrievals from multiple satellite platforms to generate estimates at both the high spatial (30 m) and temporal (daily) resolution required for field-scale irrigation management. Comparisons with micrometeorological data indicate reasonable model performance, with mean absolute errors of 0.6 mm day−1 in ET at the daily time step and minimal bias. Values of fRET agree well with tower observations and reflect known irrigation. Spatiotemporal analyses illustrate the ability of ALEXI/DisALEXI/data fusion package to characterize heterogeneity in ET and fRET both within a vineyard and over the surrounding landscape. These findings will inform the development of strategies for integrating ET mapping time series into operational irrigation management framework, providing actionable information regarding vineyard water use and crop stress at the field and regional scale and at daily to multi-annual time scales.info:eu-repo/semantics/acceptedVersio

Determining a robust indirect measurement of leaf area index in California vineyards for validating remote sensing-based retrievals

Accurate ground-based measurements of leaf area index (LAI) are needed for validation of remote sensing-based retrievals used in models estimating plant water use, stress, carbon assimilation and other land surface processes. Several methods for indirect LAI estimation with the Plant Canopy Analyzer (PCA, LAI-2200C, LI-COR, Lincoln, NE, USA) were evaluated using destructive (direct) leaf area measurements in three split-canopy vineyards and one double-vertical vineyard in California, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). A method with the sensor facing the canopy, and four readings occurring evenly across the interrow space, had a coefficient of determination (R2) of 0.87 and relative root mean square error (RRMSE) of 16%, when compared to direct LAI measurements via destructive sampling. A previously used method, with the sensor facing down-row, showed lower correlation to direct LAI (R2 = 0.75, RRMSE = 33%) and underestimation which was mitigated by removing the outer sensor rings from analysis. A PCA method is recommended for rapid and accurate LAI estimation in split-canopy vineyards, though local calibration may be required. The method was tested within small units of ground surface area, which compliments high-resolution datasets such as those acquired by small unmanned aerial vehicles. The utility of ground-based LAI measurements to validate remote sensing products is discussed.info:eu-repo/semantics/acceptedVersio

Optimizing precision irrigation of a vineyard to improve water use efficiency and profitability by using a decision-oriented vine water consumption model

While the agronomic and economic benefits of regulated deficit irrigation (RDI) strategies have long been established in red wine grape varieties, spatial variability in water requirements across a vineyard limits their practical application. This study aims to evaluate the performance of an integrated methodology—based on a vine water consumption model and remote sensing data—to optimize the precision irrigation (PI) of a 100-ha commercial vineyard during two consecutive growing seasons. In addition, a cost-benefit analysis (CBA) was conducted of the tested strategy. Using an NDVI generated map, a vineyard with 52 irrigation sectors and the varieties Tempranillo, Cabernet and Syrah was classified in three categories (Low, Medium and High). The proposed methodology allowed viticulturists to adopt a precise RDI strategy, and, despite differences in water requirement between irrigation sectors, pre-defined stem water potential thresholds were not exceeded. In both years, the difference between maximum and minimum water applied in the different irrigation sectors varied by as much as 25.6%. Annual transpiration simulations showed ranges of 240.1–340.8 mm for 2016 and 298.6–366.9 mm for 2017. According to the CBA, total savings of 7090.00 € (2016) and 9960.00 € (2017) were obtained in the 100-ha vineyard with the PI strategy compared to not PI. After factoring in PI technology and labor costs of 5090 €, the net benefit was 20.0 € ha−1 in 2016 and 48.7 € ha−1 in 2017. The water consumption model adopted here to optimize PI is shown to enhance vineyard profitability, water use efficiency and yield.info:eu-repo/semantics/publishedVersio

Assessing the Feasibility of Using Sentinel-2 Imagery to Quantify the Impact of Heatwaves on Irrigated Vineyards

Heatwaves are common in many viticultural regions of Australia. We evaluated the potential of satellite-based remote sensing to detect the effects of high temperatures on grapevines in a South Australian vineyard over the 2016-2017 and 2017-2018 seasons. The study involved: (i) comparing the normalized difference vegetation index (NDVI) from medium- and high-resolution satellite images; (ii) determining correlations between environmental conditions and vegetation indices (Vis); and (iii) identifying VIs that best indicate heatwave effects. Pearson's correlation and Bland-Altman testing showed a significant agreement between the NDVI of high- and medium-resolution imagery (R = 0.74, estimated difference ??0.093). The band and the VI most sensitive to changes in environmental conditions were 705 nm and enhanced vegetation index (EVI), both of which correlated with relative humidity (R = 0.65 and R = 0.62, respectively). Conversely, SWIR (short wave infrared, 1610 nm) exhibited a negative correlation with growing degree days (R = -0.64). The analysis of heat stress showed that green and red edge bands-the chlorophyll absorption ratio index (CARI) and transformed chlorophyll absorption ratio index (TCARI)-were negatively correlated with thermal environmental parameters such as air and soil temperature and growing degree days (GDDs). The red and red edge bands-the soil-adjusted vegetation index (SAVI) and CARI2-were correlated with relative humidity. To the best of our knowledge, this is the first study demonstrating the effectiveness of using medium-resolution imagery for the detection of heat stress on grapevines in irrigated vineyards.</p

The Grape Remote Sensing Atmospheric Profile and Evapotranspiration Experiment

Particularly in light of California’s recent multiyear drought, there is a critical need for accurate and timely evapotranspiration (ET) and crop stress information to ensure long-term sustainability of high-value crops. Providing this information requires the development of tools applicable across the continuum from subfield scales to improve water management within individual fields up to watershed and regional scales to assess water resources at county and state levels. High-value perennial crops (vineyards and orchards) are major water users, and growers will need better tools to improve water-use efficiency to remain economically viable and sustainable during periods of prolonged drought. To develop these tools, government, university, and industry partners are evaluating a multiscale remote sensing–based modeling system for application over vineyards. During the 2013–17 growing seasons, the Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data within adjacent pinot noir vineyards in the Central Valley of California. Additionally, each year ground, airborne, and satellite remote sensing data were collected during intensive observation periods (IOPs) representing different vine phenological stages. An overview of the measurements and some initial results regarding the impact of vine canopy architecture on modeling ET and plant stress are presented here. Refinements to the ET modeling system based on GRAPEX are being implemented initially at the field scale for validation and then will be integrated into the regional modeling toolkit for large area assessment

GeoAI approach to Vineyard Yield Estimation

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsKnowing in advance vineyard yield is a key issue for growers, winemakers, policy makers, and regulators being fundamental to achieve the best balance between vegetative and reproductive growth, and to allow more informed decisions like thinning, irrigation and nutrient management, schedule harvest, optimize winemaking operations, program crop insurance, fraud detection and grape picking workforce demand. In a long-term scenario of perceived climate change, it is also essential for planning and regulatory purposes at the regional level. Estimating yield is complex and requires knowing driving factors related to climate, plant, and crop management that directly influence the number of clusters per vine, berries per cluster, and berry weight. These three yield components explain 60%, 30%, and 10% of the yield. The traditional methods are destructive, labor-demanding, and time-consuming, with low accuracy primarily due to operator errors and sparse sampling (compared to the inherent spatial variability in a production vineyard). Those are supported by manual sampling, where yield is estimated by sampling clusters weight and the number of clusters per vine, historical data, and extrapolation considering the number of vines in a plot. As the extensive research in the area clearly shows, improved applied methodologies are needed at different spatial scales. The methodological approaches for yield estimation based on indirect methods are primarily applicable at small scale and can provide better estimates than the traditional manual sampling. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Despite surpassing the limitations assigned to traditional manual sampling methods with the same or better results on accuracy, they still lack a fundamental key aspect: the real application in commercial vineyards. Another gap is the lack of solutions for estimating yield at broader scales (e.g., regional level). The perception is that decisions are more likely to take place on a smaller scale, which in some cases is inaccurate. It might be the case in regulated areas and areas where support for small viticulturists is needed and made by institutions with proper resources and a large area of influence. This is corroborated by the fact that data-driven models based on Trellis Tension and Pollen traps are being used for yield estimation at regional scales in real environments in different regions of the world. The current dissertation consists of the first study to identify through a systematic literature review the research approaches for predicting yield in vineyards for wine production that can serve as an alternative to traditional estimation methods, to characterize the different new approaches identifying and comparing their applicability under field conditions, scalability concerning the objective, accuracy, advantages, and shortcomings. In the second study following the identified research gap, a yield estimation model based on Geospatial Artificial Intelligence (GeoAI) with remote sensing and climate data and a machine-learning approach was developed. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The results show that this approach makes it possible to estimate wine grape yield accurately in advance at different scales

The grape remote sensing atmospheric profile and evapotranspiration experiment

Particularly in light of California’s recent multiyear drought, there is a critical need for accurate and timely evapotranspiration (ET) and crop stress information to ensure long-term sustainability of high-value crops. Providing this information requires the development of tools applicable across the continuum from subfield scales to improve water management within individual fields up to watershed and regional scales to assess water resources at county and state levels. High-value perennial crops (vineyards and orchards) are major water users, and growers will need better tools to improve water-use efficiency to remain economically viable and sustainable during periods of prolonged drought. To develop these tools, government, university, and industry partners are evaluating a multiscale remote sensing–based modeling system for application over vineyards. During the 2013–17 growing seasons, the Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data within adjacent pinot noir vineyards in the Central Valley of California. Additionally, each year ground, airborne, and satellite remote sensing data were collected during intensive observation periods (IOPs) representing different vine phenological stages. An overview of the measurements and some initial results regarding the impact of vine canopy architecture on modeling ET and plant stress are presented here. Refinements to the ET modeling system based on GRAPEX are being implemented initially at the field scale for validation and then will be integrated into the regional modeling toolkit for large area assessment.info:eu-repo/semantics/publishedVersio

Use of remote sensing‑derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub‑field level

Grapevine simulation models are mostly used to estimate plant development, growth and yield at plot scale. However, the spatial variability of pedologic and micro-climatic conditions can influence vine growth, leading to a sub-field heterogeneity in plant vigor and final yield that may be better estimated through the assimilation of high spatial resolution data in crop models. In this study, the spatial variability of grapevine intercepted radiation at fruit-set was used as input for a grapevine simulation model to estimate the variability in biomass accumulation and yield in two Tuscan vineyards (Sites A and B). In Site A, the model, forced with intercepted radiation data as derived from the leaf area index (LAI), measured at canopy level in three main vigor areas of the vineyard, provided a satisfactory simulation of the final pruning weight (r2 = 0.61; RMSE = 19.86 dry matter g m−2). In Site B, Normalized Difference Vegetation Index (NDVI) from Sentinel-2A images was firstly re-scaled to account for canopy fraction cover over the study areas and then used as a proxy for grapevine intercepted radiation for each single pixel. These data were used to drive the grapevine simulation model accounting for spatial variability of plant vigor to reproduce yield variability at pixel scale (r2 = 0.47; RMSE = 75.52 dry matter g m−2). This study represents the first step towards the realization of a decision tool supporting winegrowers in the selection of the most appropriate agronomic practices for reducing the vine vigor and yield variability at sub-field level

Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards

Evapotranspiration (ET) is a key variable for hydrology and irrigation water management,with significant importance in drought-stricken regions of the western US. This is particularly true for California, which grows much of the high-value perennial crops in the US. The advent of small Unmanned Aerial System (sUAS) with sensor technology similar to satellite platforms allows for the estimation of high-resolution ET at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate ET from sUAS products, the sensitivity of ET models to different model grid size/resolution in complex canopies, such as vineyards, is still unknown.The variability of row spacing, canopy structure, and distance between fields makes this information necessary because additional complexity processing individual fields. Therefore, processing the entire image at a fixed resolution that is potentially larger than the plant-row separation is more efficient.From a computational perspective, there would be an advantage to running models at much coarser resolutions than the very fine native pixel size from sUAS imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (TSEB2T) model, which uses remotely sensed soil/substrate and canopy temperature from sUAS imagery, was used to estimate ET and identify the impact of spatial domain scale under different vine phenological conditions. The analysis relies upon high-resolution imagery collected during multiple years and times by the Utah State University Aggie Air TM sUAS program over a commercial vineyard located near Lodi, California.This project is part of the USDA-Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Original spectral and thermal imagery data from sUAS were at 10 cm and 60 cm per pixel, respectively, and multiple spatial domain scales (3.6, 7.2,14.4, and 30 m) were evaluated and compared against eddy covariance (EC) measurements. Results indicated that the TSEB2T model is only slightly affected in the estimation of the net radiation (Rn) and the soil heat flux (G) at different spatial resolutions, while the sensible and latent heat fluxes (HandLE, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of H and underestimation of LE values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (LST) and the normalized difference vegetation index (NDVI) at coarse model resolution. Another predominant reason for LE reduction in TSEB2T was the decrease in the aerodynamic resistance (Ra), which is a function of the friction velocity (u∗)that varies with mean canopy height and roughness length. While a small increase in grid size can be implemented, this increase should be limited to less than twice the smallest row spacing present in the sUAS imagery. The results also indicated that the mean LE at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in LE values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of LE are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications