Adjusting nitrogen (N) fertilization and accounting for the legacy of past N fertilizer application (i.e., residual N) based on remote sensing estimation of crop nutritional status may increase resource efficiency and promote sustainable management of cropping systems. Our main goal was to evaluate the potential of hyperspectral airborne imagers and ground-level sensors for identifying N fertilizer rates and the residual N effect from the previous crop fertilization in a maize/wheat rotation. A two-season field trial that provided various combinations of N rates and residual N response was established in central Spain. Ground-level sensors and aerial hyperspectral images were used to calculate vegetation indices (VIs). In addition, the solar-induced chlorophyll fluorescence (SIF ₇₆₀ ) was estimated by the Fraunhofer line-depth method using high-resolution hyperspectral imagery, and together with biophysical modeling, biochemical and biophysical constituents at canopy scales were retrieved. N uptake, N output, grain N concentration, and proximal sensors discriminated between different N fertilizer rates and identified the residual effect when it was relevant. Structural, photosynthetic pigments and short-wave infrared region (SWIR)-based VIs, together with SIF ₇₆₀ and the chlorophyll $a + b$ ( $C_{{\mathrm {ab}}}$ ), biomass, and the leaf area index (LAI), performed similarly on N rate detection. However, the residual effect of nitrification inhibitors was only detected by the structural (NDVI and OSAVI), chlorophyll (CCCI and NDRE), blue/green, NIR-SWIR ( $\text{N}_{850,1510}$ ) indices, SIF ₇₆₀ , $C_{{\mathrm {ab}}}$ , biomass, and the LAI. This study confirmed the ability of remote sensing to identify N rates at early growth stages and highlighted its potential to detect residual N in crop rotation.

Remote sensing allows fast assessment of crop monitoring over large areas; however, questions regarding uncertainty in crop parameter prediction and application to nitrogen (N) fertilization remain open. The objective of this study was to optimize of remote sensing spectral information for its application to grain yield (GY), biomass, grain N concentration (GNC), and N output assessment, and decision making on spring wheat fertilization. Spring wheat (Triticum turgidum L.) field experiments testing two tillage treatments, two irrigation levels and six N treatments were conducted in Northwest Mexico over four consecutive years. Hyperspectral images were acquired through 27 airborne flight campaigns. At harvest, GY, biomass, GNC and N output were determined. Spectral exploratory analysis was used to identify the best wavelength combinations, the most suitable vegetation indices (VIs) and the best growth stages to assess the agronomic variables. The relationship between the spectral information and the agronomic measurements was evaluated by the coefficient of determination (R²) and the root mean square error (RMSE). The ability of the indices to guide fertilizer recommendation was assessed through an error analysis based on the N sufficiency index. GY was better assessed from the end of flowering to the early milk stage by VIs based on the combination of bands from near infrared radiation/visible and from near infrared radiation/red-edge regions (R² > 0.6; RMSE < 700 kg ha⁻¹). N output was efficiently assessed by a combination of bands from near infrared radiation/red-edge at booting (R² > 0.7; RMSE < 9 kg N ha⁻¹). The GNC was better estimated by VIs combining bands in near infrared radiation/red-edge at early milk, but with great variability among the years studied. Some VIs were promising for guiding fertilizer recommendation for increasing GNC, but there was not a single index providing reliable recommendations every year. This study highlights the potential of remote sensing imagery to assess GY and N output in spring wheat, but the identification of GNC responsive sites needs to be improved.

The hydraulic traits of plants, or the efficiency of water transport throughout the plant hydraulic system, could help to anticipate the impact of climate change and improve crop productivity. However, the mechanisms explaining the role of hydraulic traits on plant photosynthesis and thus, plant growth and yield, are just beginning to emerge. We conducted an experiment to identify differences in growth patterns at leaf, root and whole plant level among four wild olive genotypes and to determine whether hydraulic traits may help to explain such differences through their effect on photosynthesis. We estimated the relative growth rate (RGR), and its components, leaf gas exchange and hydraulic traits both at the leaf and whole-plant level in the olive genotypes over a full year. Photosynthetic capacity parameters were also measured. We observed different responses to water stress in the RGRs of the genotypes studied being best explained by changes in the net CO₂ assimilation rate (NAR). Further, net photosynthesis, closely related to NAR, was mainly determined by hydraulic traits, both at leaf and whole-plant levels. This was mediated through the effects of hydraulic traits on stomatal conductance. We observed a decrease in leaf area: sapwood area and leaf area: root area ratios in water-stressed plants, which was more evident in the olive genotype Olea europaea subsp. guanchica (GUA8), whose RGR was less affected by water deficit than the other olive genotypes. In addition, at the leaf level, GUA8 water-stressed plants presented a better photosynthetic capacity due to a higher mesophyll conductance to CO₂ and a higher foliar N. We conclude that hydraulic allometry adjustments of whole plant and leaf physiological response were well coordinated, buffering the water stress experienced by GUA8 plants. In turn, this explained their higher relative growth rates compared to the rest of the genotypes under water-stress conditions.

In precision agriculture, remote sensing imagery plays a vital role in assessing the spatio-temporal variability of crop physiological components, including chlorophyll content (Cab). Determining such components allows evaluating crop nitrogen (N) status to provide fertilizer recommendations to ensure food security. This study investigates the efficacy of Sentinel-2 in estimating Cab and leaf area index (LAI) within a two-year winter wheat (Triticum aestivum L.) field experiment carried out in Aranjuez, Spain, encompassing various N and water regimes. We employed a hybrid artificial neural network (ANN) coupling the PROSAIL model with Sentinel-2 images to retrieve Cab and LAI. A multiple linear regression model was constructed with these plant traits to estimate crop N status and grain N output (kg N ha-1). The accuracy of the plant trait retrievals was validated using field data collected with various sensors and destructive samples across different growth stages (GSs). Furthermore, we assessed the predictions of Cab and LAI using Sentinel-2 time series. The accuracy of the proposed hybrid approach, and its relevance for precision agriculture were evident in its accurate estimation of key parameters: Cab (RMSE = 7.6 μg cm-2), LAI (RMSE = 0.97 m2 m-2), nitrogen nutrition index (RMSE = 0.13) and grain N output (RMSE = 17.34 kg N ha-1). Furthermore, it correctly tracked temporal fluctuations in Cab and LAI using Sentinel-2 images, meaning it can be used to optimize N fertilization rates. This method allows for the timely identification of N-sufficient and N-stressed wheat plots, particularly evident starting 100 days after the sowing date.

La sequía es un fenómeno recurrente en los pastizales de Borena, Etiopía, con impactos severos sobre la producción ganadera y las comunidades agro-pastoriles. Los datos de Observación de la Tierra (EO) ofrecen un gran potencial para mejorar la detección de sequías mediante información espacio-temporal oportuna; sin embargo, muchos indicadores tradicionales se centran únicamente en la sequía meteorológica o no son adecuados para ecosistemas semiáridos. La evapotranspiración real (ET) es un indicador clave, ya que integra condiciones atmosféricas (i.e. sequía meteorológica), disponibilidad de agua (i.e. sequía hidrológica) y el estado fisiológico de la vegetación (i.e. sequía agrícola/ecológica). Este estudio evalúa estimaciones de ET obtenidas con el modelo de balance de energía de tres fuentes (3SEB), forzado con datos globales de Copernicus (Sentinel-3 y ERA5), para caracterizar la respuesta de la vegetación a la sequía en ecosistemas semiáridos. El modelo se validó en tres sitios de eddy-covariance (EC), dos dehesas en España y una sábana en Sudáfrica que presentan condiciones similares (i.e., clima, estructura, fenología) a las que encontramos en Borena. Además, se generaron mapas de ET para Borena (2019–2022) y se compararon con el índice de vegetación de diferencia normalizada (NDVI), comúnmente usado en indicadores de sequía para caracterizar la respuesta de la vegetación. Los resultados de ET preliminares muestran buena concordancia con las mediciones EC (RMSE ~1 mm/día) y una respuesta más rápida frente al NDVI, lo que resalta su potencial para la detección temprana del estrés hídrico. El trabajo futuro se centrará en comparar indicadores basados en ET con índices tradicionales de sequía mediante un análisis espacio-temporal, así como con mediciones de servicios ecosistémicos.

Martín Escorza, Carlos, 2010, Geología, aguas, romanos y románico de Tierra de Campos (Palencia), Madrid, Sociedad de Amigos del Museo Nacional de Ciencias Naturales, CSIC