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The Delottococcus aberiae is a mealybug pest known as Cotonet de les Valls in the province of Castellón (Spain). This tiny insect is causing large economic losses in the Spanish agricultural sector, especially in the citrus industry. The European Copernicus program encourages the progress of Earth observation (EO) in relation to the development of agricultural monitoring tools. In this context, this work is based on the analysis of the temporal evolution of spectral surface reflectance data from Sen2Like, analyzing healthy and fields affected by the mealybug. The study area is focused on the surroundings of Vall d’Uixó (Castellón, Spain), involving an approximate area of 25 ha distributed in a total of 21 fields of citrus trees with different mealybug incidence, classified as healthy or unhealthy, during the 2020–2021 season. The relationship between the mealybug infestation level and the Normalized Difference Vegetation Index (NDVI) and other optical bands (Red, NIR, SWIR, derived from Sen2Like) were analyzed by studying the time-series evolution of each parameter across the time period 2017–2022. In this study, we also demonstrate that evergreen fruit trees such as citrus, show a seasonality across the EO-based time series, which is linked to directional effects caused by the sensor–sun geometry. This can be mitigated by using a Bidirectional Reflectance Distribution Function (BRDF) model such as the High-Resolution Adjusted BRDF Algorithm (HABA). To study the infested fields separately from healthy ones and avoid mixing fields with very different spectral responses caused by field type, separation between rows, or age, we studied the evolution of each parcel separately using monthly linear regressions, considering the 2017–2018 seasons as a reference when the pest had not developed yet. The observations indicate the feasibility of the distinction between affected and healthy plots during a year utilizing specific spectral ranges, with SWIR proving a notably effective channel, enabling separability from mid-summer to the fall. Furthermore, the anomaly inspection demonstrates an increase in the effects of the pest from 2020 to 2022 in all spectral regions and enables a first approximation for identifying healthy and affected fields based on negative anomalies in the red and SWIR channels and positive anomalies in the NIR and NDVI. This work contributes to the development of new monitoring tools for efficient and sustainable action in pest control.

The Delottococcus aberiae is a mealybug pest known as Cotonet de les Valls in the province of Castellón (Spain). This tiny insect is causing large economic losses in the Spanish agricultural sector, especially in the citrus industry. The European Copernicus program encourages the progress of Earth observation (EO) in relation to the development of agricultural monitoring tools. In this context, this work is based on the analysis of the temporal evolution of spectral surface reflectance data from Sen2Like, analyzing healthy and fields affected by the mealybug. The study area is focused on the surroundings of Vall d'Uixó (Castellón, Spain), involving an approximate area of 25 ha distributed in a total of 21 fields of citrus trees with different mealybug incidence, classified as healthy or unhealthy, during the 2020–2021 season. The relationship between the mealybug infestation level and the Normalized Difference Vegetation Index (NDVI) and other optical bands (Red, NIR, SWIR, derived from Sen2Like) were analyzed by studying the time-series evolution of each parameter across the time period 2017–2022. In this study, we also demonstrate that evergreen fruit trees such as citrus, show a seasonality across the EO-based time series, which is linked to directional effects caused by the sensor–sun geometry. This can be mitigated by using a Bidirectional Reflectance Distribution Function (BRDF) model such as the High-Resolution Adjusted BRDF Algorithm (HABA). To study the infested fields separately from healthy ones and avoid mixing fields with very different spectral responses caused by field type, separation between rows, or age, we studied the evolution of each parcel separately using monthly linear regressions, considering the 2017–2018 seasons as a reference when the pest had not developed yet. The observations indicate the feasibility of the distinction between affected and healthy plots during a year utilizing specific spectral ranges, with SWIR proving a notably effective channel, enabling separability from mid-summer to the fall. Furthermore, the anomaly inspection demonstrates an increase in the effects of the pest from 2020 to 2022 in all spectral regions and enables a first approximation for identifying healthy and affected fields based on negative anomalies in the red and SWIR channels and positive anomalies in the NIR and NDVI. This work contributes to the development of new monitoring tools for efficient and sustainable action in pest control. [ABSTRACT FROM AUTHOR]

Rice production remains highly dependent on nitrogen (N). There is no positive linear correlation between N concentration and yield in rice cultivation because an excess of N can unbalance the distribution of photo-assimilates in the plant and consequently produce a lower yield. We intended to study these imbalances. Remote sensing is a useful tool for monitoring rice crops. The purpose of this study was to evaluate the effectiveness of using remote sensing to assess the impact of N applications on rice crop behavior. An experiment with three different doses (120, 170 and 220 kg N·ha−1) was carried out over two years (2021 and 2022) in Valencia, Spain. Biomass, Leaf Area Index (LAI), plants per m2, yield, N concentration and N uptake were determined. Moreover, reflectance values in the green, red, and NIR bands of the Sentinel-2 satellite were acquired. The two data matrices were merged in a correlation study and the resulting interpretation ended in a protocol for the evaluation of the N effect during the main phenological stages. The positive effect of N on the measured parameters was observed in both years; however, in the second year, the correlations with the yield were low, being attributed to a complex interaction with climatic conditions. Yield dependence on N was optimally evaluated and monitored with Sentinel-2 data. Two separate relationships between NIR–red and NDVI–NIR were identified, suggesting that using remote sensing data can help enhance rice crop management by adjusting nitrogen input based on plant nitrogen concentration and yield estimates. This method has the potential to decrease nitrogen use and environmental pollution, promoting more sustainable rice cultivation practices.

In this paper, we investigated the monitoring and characterization of the pest Magnaporthe oryzae, known as rice blast, in the Bomba rice variety at the Albufera Natural Park, located in Valencia, Spain during the 2022 and 2023 seasons. Using reflectance data from different Sentinel-2 satellite bands, various vegetative indices were calculated for each year. Significant differences in reflectance in the visible (B4), infrared (B8), red-edge (B6 and B7), and SWIR (B11) bands were detected between healthy and unhealthy fields. Additionally, variations were observed in the vegetation indices, with RVI and IRECI standing out for their higher accuracy in identifying blast-affected plots compared to NDVI and NDRE. Early differences in band values, vegetative indices, and spectral signatures were observed between the unhealthy and healthy plots, allowing for the anticipation of control treatments, whose effectiveness relies on timely intervention.