Farming and earth observation: sentinel-2 data to estimate within-field wheat grain yield

Wheat grain yield (GY) is a crop feature of central importance affecting agricultural, environmental, and socioeconomic sustainability worldwide. Hence, the estimation of within-field variability of GY is pivotal for the agricultural management, especially in the current global change context. In this sense, Earth Observation Systems (EOS) are key technologies that use satellite data to monitor crop yield, which can guide the application of precision farming. Yet, novel research is required to improve the multiplatform integration of data, including data processing, and the application of this discipline in agricultural management. This article provides a novel methodological analysis and assessment of its applications in precision farming. It presents an integration of wheat GY, Global Positioning Systems (GPS), combine harvester data, and EOS Sentinel-2 multispectral bands. Moreover, it compares several indices and machine learning (ML) approaches to map within-field wheat GY. It also analyses the importance of multi-date remote sensing imagery and explores its potential applications in precision agriculture. The study was conducted in Spain, a major European wheat producer. Within-field GY data was obtained from a GPS combine harvester machine for 8 fields over three seasons (2017-2019) and consecutively processed to match Sentinel-2 10 m pixel size. Seven vegetation indices (NDVI, GNDVI, EVI, RVI, TGI, CVI and NGRDI) as well as the biophysical parameter LAI (leaf area index) retrieved with radiative transfer models (RTM) were calculated from Sentinel-2 bands. Sentinel-2 10 m resolution bands alone were also used as variables. Random forest, support vector machine and boosted regressions were used as modelling approaches, and multilinear regression was calculated as baseline. Different combinations of dates of measurement were tested to find the most suitable model feeding data. LAI retrieved from RTM had a slightly improved performance in estimating within-field GY in comparison with vegetation indices or Sentinel-2 bands alone. At validation, the use of multi-date Sentinel-2 data was found to be the most suitable in comparison with single date images. Thus, the model developed with random forest regression (e.g. R-2 = 0.89, and RSME = 0.74 t/ha when using LAI) outperformed support vector machine (R-2 = 0.84 and RSME = 0.92 t/ha), boosting regression (R-2 = 0.85 and RSME = 0.88 t/ha) and multilinear regression (R-2 = 0.69 and RSME = 1.29 t/ha). However, single date images at specific phenological stages (e.g. R-2 = 0.84, and RSME = 0.88 t/ha using random forest at stem elongation) also posed relatively high R-2 and low RMSE, with potential for precision farming management before harvest.A & nbsp;We acknowledge the support of the project PID2019-106650RB-C21 from the Ministerio de Ciencia e Innovacion, Spain. J.S. is a recipient of a FPI doctoral fellowship from the same institution (grant: PRE2020-091907) . J.L.A. acknowledges support from the Institucio Catalana de Recerca i Estudis Avancats (ICREA) , Generalitat de Catalunya, Spain) . S. C.K. is supported by the Ramon y Cajal RYC-2019-027818-I research fellowship from the Ministerio de Ciencia e Innovacion, Spain. We acknowledge the support of Cerealto Siro Group, together with Cristina de Diego and Javier Velasco, technical staff from the company, by providing the wheat yield data. This research was also supported by the COST Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu)

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

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.This research was funded by the Spanish projects AGL2016-76527-R and IRUEC PCIN-2017-063 from the Ministerio de Economía y Competividad (MINECO, Spain) and by the support of Catalan Institution for Research and Advanced Studies (ICREA, Generalitat de Catalunya, Spain), through the ICREA Academia Program

Women in GRSS Activities at IEEE M2GARSS 2020

In this “Women in GRSS” column, we spotlight the activities organized by the IEEE Geoscience and Remote Sensing Society (GRSS) Inspire, Develop, Empower, and Advance (IDEA) Committee during the 2020 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS) held 9–11 March at the Ramada Plaza Tunis, Tunisia

Visible ozone-like injury, defoliation, and mortality in two Pinus uncinata stands in the Catalan Pyrenees (NE Spain)

Ozone concentrations in the Pyrenees have exceeded the thresholds for forest protection since 1994. We surveyed the severity of visible O₃ injuries, crown defoliation, and tree mortality of Pinus uncinata, the dominant species in subalpine forests in this mountain range, along two altitudinal and O₃ gradients in the central Catalan Pyrenees and analysed their relationships with the local environmental conditions. The severity of visible O₃ injuries increased with increasing mean annual [O₃] when summer water availability was high (summer precipitation/potential evapotranspiration above 0.96), whereas higher [O₃] did not produce more visible injuries during drier conditions. Mean crown defoliation and tree mortality ranged between 20.4-66.4 and 0.6-29.6 %, respectively, depending on the site. Both were positively correlated with the accumulated O₃ exposure during the last 5 years and with variables associated with soil-water availability, which favours greater O₃ uptake by increasing stomatal conductance. The results indicate that O₃ contributed to the crown defoliation and tree mortality, although further research is clearly warranted to determine the contributions of the multiple stress factors to crown defoliation and mortality in P. uncinata stands in the Catalan Pyrenees

Dataset of above and below ground traits assessed in Durum wheat cultivars grown under Mediterranean environments differing in water and temperature conditions

Ideotypic characteristics of durum wheat associated with higher yield under different water and temperature regimes were studied under Mediterranean conditions. The focus of this paper is to provide raw and supplemental data from the research article entitled "Durum wheat ideotypes in Mediterranean environments differing in water and temperature conditions" [1], which aims to define specific durum wheat ideotypes according to their responses to different agronomic conditions. In this context, six modern (i.e. post green revolution) genotypes with contrasting yield performance (i.e. high vs low yield) were grown during two consecutive years under different treatments: (i) winter planting under support-irrigation conditions, (ii) winter planting under rainfed conditions, (iii) late planting under support-irrigation. Trials were conducted at the INIA station of Colmenar de Oreja (Madrid). Different traits were assessed to inform about water status (canopy temperature at anthesis and stable carbon isotope composition (delta C-13) of the flag leaf and mature grains), root performance (root traits and the oxygen isotope composition (delta O-18) in the stem base water), phenology (days from sowing to heading), nitrogen status/photosynthetic capacity (nitrogen content and stable isotope composition (delta N-15) of the flag leaf and mature grain together with the pigment contents and the nitrogen balance index (NBI) of the flag leaf), crop growth (plant height (PH) and the normalized difference vegetation index (NDVI) at anthesis), grain yield and agronomic yield components. For most of the parameters assessed, data analysis demonstrated significant differences among genotypes within each treatment. The level of significance was determined using the Tukey-b test on independent samples, and ideotypes were modelled from the results of principle component analysis. The present data shed light on traits that help to define specific ideotype characteristics that confer genotypic adaptation to a wide range of agronomic conditions produced by variations in planting date, water conditions and seasonThis study was supported by the Spanish projects PID2019-106650RB-C21 and PCIN-2017-063, from the Ministerio de Ciencia e Innovacion, Spain. FZR is a recipient of a research grant (FI-AGAUR) sponsored by the Agency for Management of University and Research Grants (AGAUR) in collaboration with the University of Barcelona (UB) . We thank the personnel from the exper-imental station of INIA at Colmenar de Oreja (Aranjuez) for their continued support of our re-search. We thank the members of the Integrative Crop Ecophysiology Group for their assistance during the data assessment of the study. We extend our thanks to The Water Research Institute (IdRA) for their financial support to cover laboratory analyses. JLA acknowledges support from ICREA Academia, Generalitat de Catalunya, Spain. We thank Dr. J.Voltas from the University of Lleida, Spain, for his support with the delta 18O water analyses

Artificial intelligence applications in precision agriculture to predict the effect of Root-Knot nematodes and grafting on vegetable crop health from proximal remote sensing machines

In precision agriculture, the Normalized Difference Vegetative Index (NDVI) considers the spectral characteristics of healthy green vegetation. This index is an effective way of detecting the green state of plants. This is why we choose to use NDVI as a reference index to predict the effect of Root-Knot nematodes and grafting on vegetable crop health from proximal remote sensing machines. These machines were used to estimate different physiological, biochemical, and agronomic parameters as indicators of stress (GA, GGA, SPAD, and canopy temperature). Leaf level pigments were measured using a handheld sensor (SPAD). Canopy vigor and biomass were assessed using vegetation indices derived from RGB images and the NDVI was measured with a portable spectroradiometer (Greenseeker). The plant level water stress was assessed indirectly by plant temperature using an infrared thermometer. We conclude that the grafted plants were less stressed and more protected against nematode attack. The comparison of NDVI index predicted by AI models showed that artificial neural network MLP demonstrated the best prediction performance than the linear regression method. However, their R-squared decreased from 0.820 to 0.772, and NRMSE increased from 12.3% to 12.4%, respectively

Bridging the genotype-phenotype gap for a Mediterranean pine by semi‐automatic crown identification and multispectral imagery

Summary: Progress in high‐throughput phenotyping and genomics provides the potential to understand the genetic basis of plant functional differentiation. We developed a semi‐automatic methodology based on unmanned aerial vehicle (UAV) imagery for deriving tree‐level phenotypes followed by genome‐wide association study (GWAS). An RGB‐based point cloud was used for tree crown identification in a common garden of Pinus halepensis in Spain. Crowns were combined with multispectral and thermal orthomosaics to retrieve growth traits, vegetation indices and canopy temperature. Thereafter, GWAS was performed to analyse the association between phenotypes and genomic variation at 235 single nucleotide polymorphisms (SNPs). Growth traits were associated with 12 SNPs involved in cellulose and carbohydrate metabolism. Indices related to transpiration and leaf water content were associated with six SNPs involved in stomata dynamics. Indices related to leaf pigments and leaf area were associated with 11 SNPs involved in signalling and peroxisome metabolism. About 16-20% of trait variance was explained by combinations of several SNPs, indicating polygenic control of morpho‐physiological traits. Despite a limited availability of markers and individuals, this study is provides a successful proof‐of‐concept for the combination of high‐throughput UAV‐based phenotyping with cost‐effective genotyping to disentangle the genetic architecture of phenotypic variation in a widespread conifer.This work was supported by the Spanish Government, grant number RTI2018‐094691‐B‐C31 (MCIU/AEI/FEDER, EU)

Metabolome Profiling Supports the Key Role of the Spike in Wheat Yield Performance

Although the relevance of spike bracts in stress acclimation and contribution to wheat yield was recently revealed, the metabolome of this organ and its response to water stress is still unknown. The metabolite profiles of flag leaves, glumes and lemmas were characterized under contrasting field water regimes in five durum wheat cultivars. Water conditions during growth were characterized through spectral vegetation indices, canopy temperature and isotope composition. Spike bracts exhibited better coordination of carbon and nitrogen metabolisms than the flag leaves in terms of photorespiration, nitrogen assimilation and respiration paths. This coordination facilitated an accumulation of organic and amino acids in spike bracts, especially under water stress. The metabolomic response to water stress also involved an accumulation of antioxidant and drought tolerance related sugars, particularly in the spikes. Furthermore, certain cell wall, respiratory and protective metabolites were associated with genotypic outperformance and yield stability. In addition, grain yield was strongly predicted by leaf and spike bracts metabolomes independently. This study supports the role of the spike as a key organ during wheat grain filling, particularly under stress conditions and provides relevant information to explore new ways to improve wheat productivity including potential biomarkers for yield prediction.This work was supported by the Spanish Ministry of Economy and Competitiveness (MEC) [grant number AGL2016-76527- R]. We also acknowledge EIG CONCERT-Japan (EC) / PCIN-2017-063 (MINECO, Spain) project

Comparison of proximal remote sensing devices of vegetable crops to determine the role of grafting in plant resistance to meloidogyne incognita

Proximal remote sensing devices are novel tools that enable the study of plant health status through the measurement of specific characteristics, including the color or spectrum of light reflected or transmitted by the leaves or the canopy. The aim of this study is to compare the RGB and multispectral data collected during five years (2016–2020) of four fruiting vegetables (melon, tomato, eggplant, and peppers) with trial treatments of non-grafted and grafted onto resistant rootstocks cultivated in a Meloidogyne incognita (a root-knot nematode) infested soil in a greenhouse. The proximal remote sensing of plant health status data collected was divided into three levels. Firstly, leaf level pigments were measured using two different handheld sensors (SPAD and Dualex). Secondly, canopy vigor and biomass were assessed using vegetation indices derived from RGB images and the Normalized Difference Vegetation Index (NDVI) measured with a portable spectroradiometer (Greenseeker). Third, we assessed plant level water stress, as a consequence of the root damage by nematodes, using stomatal conductance measured with a porometer and indirectly using plant temperature with an infrared thermometer, and also the stable carbon isotope composition of leaf dry matter.. It was found that the interaction between treatments and crops (ANOVA) was statistically different for only four of seventeen parameters: flavonoid (p < 0.05), NBI (p < 0.05), NDVI (p < 0.05) and the RGB CSI (Crop Senescence Index) (p < 0.05). Concerning the effect of treatments across all crops, differences existed only in two parameters, which were flavonoid (p < 0.05) and CSI (p < 0.001). Grafted plants contained fewer flavonoids (x¯ = 1.37) and showed lower CSI (x¯ = 11.65) than non-grafted plants (x¯ = 1.98 and x¯ = 17.28, respectively, p < 0.05 and p < 0.05) when combining all five years and four crops. We conclude that the grafted plants were less stressed and more protected against nematode attack. Leaf flavonoids content and the CSI index were robust indicators of root-knot nematode impacts across multiple crop typesY.H. acknowledges the support of the Tunisian government from the Ministery of Higher Education and Scientific Research. J.L.A. acknowledges support from the Institucio Catalana d'Investigacio i Estudis Avancats (ICREA) Academia, Generalitat de Catalunya, Spain. S.C.K. is supported by the Ramon y Cajal RYC-2019-027818-I research fellowship from the Ministerio de Ciencia e Innovacion, Spain. Thanks are also given to the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Regional Development Fund (FEDER) for funding the project AGL2013-49040-C2-1-R and to the Ministry of Science and Innovation from the Spanish Government for funding the AGL2017-89785-R, and to the European Regional Development Fund (FEDER) AGL2017-89785-R, and for providing the FPI grant PRE2018-084265 to AMF. This research was also supported by the COST Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu accessed on 29 April 2022)