José Fernandez Ortega

La disponibilidad de recursos hídricos para la agricultura es cada vez más restringida, por lo que al mismo tiempo cobra cada vez mayor importancia el llamado “riego de precisión”, cuyo objetivo es aplicar la dosis necesaria de riego al cultivo en el momento adecuado, siendo tendencia desde hace años los estudios sobre aplicaciones de riego deficitario. Si bien el riego de precisión se basa en las nuevas tecnologías disponibles para la monitorización y gestión, el diseño clásico del sistema de riego implica la aplicación uniforme de agua en todo el sistema, sin tener en cuenta la variabilidad existente en la parcela. Además, el diseño hidráulico de sistemas riego procede a una simplificación de la forma y topografía de la subunidad real a una subunidad con forma y topografía totalmente regular. En esta comunicación se presenta el resultado del diseño hidráulico y su posterior validación de un sistema de riego de precisión en una parcela comercial de vid (Vitis vinífera L.) en espaldera, teniendo en cuenta la variabilidad del cultivo. Dicha variabilidad se determinó mediante vuelos con vehículo aéreo no tripulado (VANT) y posterior zonificación en función del vigor del cultivo. De esta manera, se zonificó un sector de riego con 3 zonas de diferente vigor: alto, medio y bajo. El diseño hidráulico del sector tuvo en cuenta dicha zonificación, aplicando diferente dosis de agua según la zona. Para el diseño del sistema de riego se recurrió a herramientas informáticas especializadas, las cuales permiten un diseño totalmente irregular en forma y topografía, y que permiten además evaluar el funcionamiento del sistema a nivel de gotero. Tras el montaje del nuevo sistema de riego se procedió a su evaluación y posterior calibración, tomando datos de presión y caudal en la nueva instalación. Los resultados obtenidos confirmaron la aplicación diferenciada de agua en función del diseño propuesto. La utilización de herramientas específicas para diseño hidráulico de sistemas de riego irregulares en forma y topografía permiten un riego adaptado a la variabilidad la parcela. Este estudio se enmarca dentro del proyecto Preciriego (sistema experto para el diseño y control de un sistema de riego a presión, personalizado y de precisión), continuando los estudios de diseño de sistemas de riego de precisión en cultivos herbáceos.

The application of geomatics to the agroforestry field is acquiring greater prominence in recent times in a world that is increasingly digital and aware of sustainability and food security. The use of geomatics tools for 3D documentation and visualisation is becoming essential in so-called precision agriculture. This article describes the methodology used to obtain the wood volume of a set of vines in the town of Tarazona de la Mancha (Albacete, Spain).

The development of unmanned aerial vehicles (UAVs) and light sensors has required new approaches for high-resolution remote sensing applications. High spatial and temporal resolution spectral data acquired by multispectral and conventional cameras (or red, green, blue (RGB) sensors) onboard UAVs can be useful for plant water status determination and, as a consequence, for irrigation management. A study in a vineyard located in south-eastern Spain was carried out during the 2018, 2019, and 2020 seasons to assess the potential uses of these techniques. Different water qualities and irrigation application start throughout the growth cycle were imposed. Flights with RGB and multispectral cameras mounted on a UAV were performed throughout the growth cycle, and orthoimages were generated. These orthoimages were segmented to include only vegetation and calculate the green canopy cover (GCC). The stem water potential was measured, and the water stress integral (S_ψ) was obtained during each irrigation season. Multiple linear regression techniques and artificial neural networks (ANNs) models with multispectral and RGB bands, as well as GCC, as inputs, were trained and tested to simulate the S_ψ. The results showed that the information in the visible domain was highly related to the S_ψ in the 2018 season. For all the other years and combinations of years, multispectral ANNs performed slightly better. Differences in the spatial resolution and radiometric quality of the RGB and multispectral geomatic products explain the good model performances with each type of data. Additionally, RGB cameras cost less and are easier to use than multispectral cameras, and RGB images are simpler to process than multispectral images. Therefore, RGB sensors are a good option for use in predicting entire vineyard water status. In any case, field punctual measurements are still required to generate a general model to estimate the water status in any season and vineyard.

High-resolution remote sensing imagery by unmanned aerial vehicles (UAVs) has been used as a tool for the environmental management of natural resources. Monitoring programmes that evaluate the ecological status of water bodies according to the Water Framework Directive (WFD) involve significant costs and sampling efforts that can be reduced by using UAVs. UAV imagery was used to measure some metrics of the “macrophytes and phytobenthos” biological quality element (BQE), which is required to assess the ecological status of European lakes; e.g. the percentage cover of hydrophytes and helophytes. Eight UAV flights took place during an annual cycle (July 2016 to July 2017) in a small karstic lake located in southeast Spain. Limnolog¬ical surveys of physicochemical (temperature, conductivity, dissolved oxygen, pH) and biological (pigments) parameters were simultaneously performed to correctly interpret the UAV images. For each flight performance, an orthomosaic of georeferenced RGB images was obtained, and the different features of interest were monitored and quantified by an automated identification and classification system (the LAIC software). The UAV images allowed us to not only evaluate the lake’s ecological status by measuring macrophyte metrics, but to also detect relevant ecological events for environmental management. A gradual burial process of charophyte meadows by the proliferation of periphytic cyanobacterial was detected in an early state by UAV images. Stratification processes, such as hypolimnetic sulphur bacteria blooms or metalimnetic white colloidal layers, were also ob¬served by UAV imaging. We conclude that UAV imagery is a useful tool for environmental lake management.

The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on assessing, comparing, and discussing the performances and limitations of satellite and UAV-based imagery in terms of canopy development, i.e., the leaf area index (LAI), and yield, i.e., the dry aboveground biomass (DAGB), for maize. Three commercial maize fields were studied over four seasons to obtain the LAI and DAGB. The normalized difference vegetation index (NDVI) and visible atmospherically resistant index (VARI) from satellite platforms (Landsat 5TM, 7 ETM+, 8OLI, and Sentinel 2A MSI) and the VARI and green canopy cover (GCC) from UAV imagery were compared. The remote sensing predictors in addition to the growing degree days (GDD) were assessed to estimate the LAI and DAGB using multilinear regression models (MRMs). For LAI estimation, better adjustments were obtained when predictors from the UAV platform were considered. The DAGB estimation revealed similar adjustments for both platforms, although the Landsat imagery offered slightly better adjustments. The results obtained in this study demonstrate the advantage of remote sensing platforms as a useful tool to estimate essential agronomic features.

The launch of Sentinel-2A and B satellites has boosted the development of many applications that could benefit from the fine resolution of the supplied information, both in time and in space. Crop classification is a necessary task for efficient land management. We evaluated the benefits of combining Landsat-8 and Sentinel-2A information for irrigated crop classification. We also assessed the robustness and efficiency of 22 nonparametric classification algorithms for classifying irrigated crops in a semiarid region in the southeast of Spain. A parcel-based approach was proposed calculating the mean normalized difference vegetation index (NDVI) of each plot and the standard deviation to generate a calibration-testing set of data. More than 2000 visited plots for 12 different crops along the study site were utilized as ground truth. Ensemble classifiers were the most robust algorithms but not the most efficient because of their low prediction rate. Nearest neighbor methods and support vector machines have the best balance between robustness and efficiency as methods for classification. Although the F1 score is close to 90%, some misclassifications were found for spring crops (e.g., barley, wheat and peas). However, crops with quite similar cycles could be differentiated, such as purple garlic and white garlic, showing the powerfulness of the developed tool.