The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.

Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is complex and labor-intensive. Here we describe the development of a streamlined approach using publicly accessible agricultural data, field-level yield, and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield. We validated our method on wheat croplands in Navarre in northern Spain, which features heterogeneous topography and rainfall. First, this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices, which were measured at various phenological stages in order to determine the optimal timings. Second, the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images. We used a supervised pixel-based random forest classification and the estimates were compared to government-published post-harvest yield statistics. When tested, the model achieved an R² of 0.83 in predicting grain yield at field level. The wheat parcels were mapped with an accuracy close to 86% for both overall accuracy and compared to official statistics. Third, the validated model was used to explore potential relationships of the mapped per-parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions. Topographic features and rainfall together accounted for an average for 11 to 20% of the observed spatial variation in grain yield in Navarre. These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale.

Although consumers appreciate the genetic diversity among tomato landraces, traditional varieties have been displaced from commercial agriculture. Their cultivation through organic farming in natural parks can contribute to their resurgence. With this aim, we developed a participatory plant breeding (PPB) program in Collserola Natural Park (Barcelona, Spain) to promote the conservation of the Mando landrace and to obtain new varieties adapted to local organic farms. Taking advantage of the natural genetic variation from the variety’s high cross-pollination rate, farmers developed five experimental inbreeds that were tested in a multi-locality trial in 2018. As a result of the PPB program, cultivation of the original landrace increased from 80 plants in 2011 to more than 2000 plants in 2018, which protected the variety from genetic erosion. Locality was the factor that contributed mostly to agronomic traits (e.g., yield (66% of the phenotypic variance)), while the genotype contributed more to the quality traits (except for soluble solids (37%) and dry matter (38%)). Farmer evaluations were highly correlated with the phenotypic traits recorded by researchers (Pearson coefficient ranging from 0.63–0.83), and led to the same final varietal selection. The superior inbred selected (3.9) is now being cultivated in the area of study. This paper discusses the efficiency of PPB in guiding the evolution of landraces.

Durum wheat production is concentrated in Mediterranean climate regions, making it essential to develop cultivars that adapt to its changing conditions, including water and heat stress. In this regard, photosynthetic capacity estimates may help improve the selection of the most adapted cultivars. However, the cost and inherent low throughput of the usual methodological approaches makes, in many cases, phenotyping unfeasible, particularly under field conditions. This study uses leaf photosynthetic measurements taken with a low-cost handheld chlorophyll sensor (MultispeQ Photosynq) and a biomass sensitive sensor (GreenSeeker) measuring the normalized difference vegetation index (NDVI) to assess the performance of six modern durum wheat cultivars. The sensors were employed at anthesis and grain filling under two different types of management (rainfed and support irrigation) for two growing seasons. Compared to irrigated plants, rainfed trials had significantly lower photosynthetic performance during the two phenological stages evaluated. Significant genotype differences in steady-state fluorescence yield (F_s) and maximum fluorescence yield (F_m′) across treatments and crop seasons were found. This study shows that leaf chlorophyll fluorescence parameters can be used to select modern wheat cultivars with an open-source, low-cost, handheld sensor (Photosynq).

The productivity of wheat in the Mediterranean region is under threat due to climate-change-related environmental factors, including fungal diseases that can negatively impact wheat yield and quality. Wheat phenotyping tools utilizing affordable, high-throughput plant phenotyping (HTPP) techniques, such as aerial and ground RGB images and quick canopy and leaf sensors, can aid in assessing crop status and selecting tolerant wheat varieties. This study focused on the impact of fungal diseases on wheat productivity in the Mediterranean region, considering the need for a precise selection of tolerant wheat varieties. This research examined the use of affordable HTPP methods, including imaging and active multispectral sensors, to aid in crop management for improved wheat health and to support commercial field phenotyping programs. This study evaluated 40 advanced lines of bread wheat (Triticum aestivum L.) at five locations across northern Spain, comparing fungicide-treated and untreated blocks under fungal disease pressure (Septoria, brown rust, and stripe rust observed). Measurements of leaf-level pigments and canopy vegetation indexes were taken using portable sensors, field cameras, and imaging sensors mounted on unmanned aerial vehicles (UAVs). Significant differences were observed in Dualex flavonoids and the nitrogen balance index (NBI) between treatments in some locations (p < 0.001 between Elorz and Ejea). Measurements of canopy vigor and color at the plot level showed significant differences between treatments at all sites, highlighting indexes such as the green area (GA), crop senescence index (CSI), and triangular greenness index (TGI) in assessing the effects of fungicide treatments on different wheat cultivars. RGB vegetation indexes from the ground and UAV were highly correlated (r = 0.817 and r = 0.810 for TGI and NGRDI). However, the Greenseeker NDVI sensor was found to be more effective in estimating grain yield and protein content (R² = 0.61–0.7 and R² = 0.45–0.55, respectively) compared to the aerial AgroCam GEO NDVI (R² = 0.25–0.35 and R² = 0.12–0.21, respectively). We suggest as a practical consideration the use of the GreenSeeker NDVI as more user-friendly and less affected by external environmental factors. This study emphasized the throughput benefits of RGB UAV HTPPs with the high similarity between ground and aerial results and highlighted the potential for HTPPs in supporting the selection of fungal-disease-resistant bread wheat varieties.