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Since the 1970s, remote sensing has been used to monitor the environment, address national security concerns, and manage Earth resources within a market framework. However, environmental organizations can also utilize remote sensing data infrastructure to support oppositional narratives, legal processes, and direct action. We present a framework for the socio-technical practice of remote sensing in alliance with those communities and organizations that are struggling for environmental justice on the global commodity frontiers. Positioned at the intersection of critical geography and political ecology, we examine the ways that critical remote sensing has been adopted in five major types of environmental conflict: struggles opposing fossil fuel exploitation, timber extraction, intensive food production, water management practices, and the effects of mining. We present a baseline inventory of remote sensing resources that are useful to the five conflict types and are freely accessible online. A global perspective on the planetary environmental crisis is essential, and we suggest that remote sensing practitioners can, through workshops or online tutorials, help environmental justice organizations towards independent use of remote sensing data. The local communities should then determine whether remote sensing products can contribute to their struggles. [ABSTRACT FROM AUTHOR]

An acceleration of the genetic advances of durum wheat, as a major crop for the Mediterranean region, is required, but phenotyping still represents a bottleneck for breeding. This study aims to define durum wheat ideotypes under Mediterranean conditions by selecting the most suitable phenotypic remote sensing traits among different ones informing on characteristics related with leaf pigments/photosynthetic status, crop water status, and crop growth/green biomass. A set of 24 post-green revolution durum wheat cultivars were assessed in a wide set of 19 environments, accounted as the specific combinations of a range of latitudes in Spain, under different management conditions (water regimes and planting dates), through 3 consecutive years. Thus, red-green-blue and multispectral derived vegetation indices and canopy temperature were evaluated at anthesis and grain filling. The potential of the assessed remote sensing parameters alone and all combined as grain yield (GY) predictors was evaluated through random forest regression models performed for each environment and phenological stage. Biomass and plot greenness indicators consistently proved to be reliable GY predictors in all of the environments tested for both phenological stages. For the lowest-yielding environment, the contribution of water status measurements was higher during anthesis, whereas, for the highest-yielding environments, better predictions were reported during grain filling. Remote sensing traits measured during the grain filling and informing on pigment content and photosynthetic capacity were highlighted under the environments with warmer conditions, as the late-planting treatments. Overall, canopy greenness indicators were reported as the highest correlated traits for most of the environments and regardless of the phenological moment assessed. The addition of carbon isotope composition of mature kernels was attempted to increase the accuracies, but only a few were slightly benefited, as differences in water status among cultivars were already accounted by the measurement of canopy temperature. [ABSTRACT FROM AUTHOR]

The systematic use of plant protection products is now being called into question with the growing awareness of the risks they can represent for the environment and human health. The application of precision agriculture technologies helps to improve agricultural production but also to rationalize input costs and improve ecological footprints. Here we present a study on fungicide application efficiency and its impact on the grass quality of a golf course green using the free open-source image analysis software FIJI (Image J) to analyze ground RGB (high-resolution digital cameras) and multispectral aerial imagery in combination with experimental data of spray pressure and hydraulic slot nozzle size of a boom sprayer machine. The multivariate regression model best explained variance in the normalized green-red difference index (NGRDI) as a relevant indicator of healthy turfgrass fields from the aerial, ground, and machine data set. [ABSTRACT FROM AUTHOR]

In 2013, the protection status of the Nevado de Toluca Volcano was changed from National Park to a less restrictive category. Much controversy has arisen surrounding this decision as the new category allows forest harvesting practices in most of the natural protected area. We assessed the forest cover loss in the Nevado de Toluca protected area and a 20 km buffer zone around it, before and after the change in protection status, and we established a map of forest types in view of assessing biodiversity vulnerability to the protection change in the forest harvesting areas. The forest type classification was carried out applying a Random Forest algorithm to Sentinel-2 imagery. The forest cover loss was assessed using the Landsat-based Global Forest Watch product during the period 2001–2019. Forest cover loss increased after the change of the Nevado de Toluca protection status; 39.7 hectares/year (± 16.7 ha/year) between 2001 and 2013 versus 106.0 hectares/year (± 40.2 ha/year) between 2014 and 2019. Clear-cut deforestation occurred in 49.6 hectares during year 2018. According to our forest map, 7171 ha (54.8%) of the Abies religiosa forest and 6511 ha (32.5%) of the pine forests of the Nevado de Toluca protected area are inside the forest harvesting zonation. Also, forest cover loss around the natural protected area has increased, especially in 2018, indicating that allowing forest harvesting practices in the natural protected area has not reduced the pressure in the surrounding forests either. We ponder the consequences of the category change and give some suggestions for the future of the forests in the Nevado de Toluca protected area. [ABSTRACT FROM AUTHOR]

High‐throughput crop phenotyping, particularly under field conditions, is nowadays perceived as a key factor limiting crop genetic advance. Phenotyping not only facilitates conventional breeding, but it is necessary to fully exploit the capabilities of molecular breeding, and it can be exploited to predict breeding targets for the years ahead at the regional level through more advanced simulation models and decision support systems. In terms of phenotyping, it is necessary to determined which selection traits are relevant in each situation, and which phenotyping tools/methods are available to assess such traits. Remote sensing methodologies are currently the most popular approaches, even when lab‐based analyses are still relevant in many circumstances. On top of that, data processing and automation, together with machine learning/deep learning are contributing to the wide range of applications for phenotyping. This review addresses spectral and red–green–blue sensing as the most popular remote sensing approaches, alongside stable isotope composition as an example of a lab‐based tool, and root phenotyping, which represents one of the frontiers for field phenotyping. Further, we consider the two most promising forms of aerial platforms (unmanned aerial vehicle and satellites) and some of the emerging data‐processing techniques. The review includes three Boxes that examine specific case studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]