High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields

Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping

Land-cover monitoring is one of the core applications of remote sensing. Monitoring and mapping changes in the distribution of agricultural land covers provide a reliable source of information that helps environmental sustainability and supports agricultural policies. Synthetic Aperture Radar (SAR) can contribute considerably to this monitoring effort. The first objective of this research is to extend the use of time series of polarimetric data for land-cover classification using a decision tree classification algorithm. With this aim, RADARSAT-2 (quad-pol) and Sentinel-1 (dual-pol) data were acquired over an area of 600 km2 in central Spain. Ten polarimetric observables were derived from both datasets and seven scenarios were created with different sets of observables to evaluate a multitemporal parcel-based approach for classifying eleven land-cover types, most of which were agricultural crops. The study demonstrates that good overall accuracies, greater than 83%, were achieved for all of the different proposed scenarios and the scenario with all RADARSAT-2 polarimetric observables was the best option (89.1%). Very high accuracies were also obtained when dual-pol data from RADARSAT-2 or Sentinel-1 were used to classify the data, with overall accuracies of 87.1% and 86%, respectively. In terms of individual crop accuracy, rapeseed achieved at least 95% of a producer’s accuracy for all scenarios and that was followed by the spring cereals (wheat and barley), which achieved high producer’s accuracies (79.9%-95.3%) and user’s accuracies (85.5% and 93.7%).All RADARSAT-2 images have been provided by MDA and CSA in the framework of the SOAR-EU2 Project ref. 16375. This study was supported by the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under projects TEC2017-85244-C2-1-P, ESP2015-67549-C3-3 and ESP2017-89463-C3-3-R

Explotación sinérgica de datos multiespectrales y radar para la estimación de variables biofísicas de la vegetación mediante tecnologías de sensoramiento remoto

Las variables biofísicas de la vegetación (VBV) son indicadores directos del crecimiento y productividad de los cultivos. Los sistemas de observación de la Tierra (EO–Earth observation) presentan oportunidades sin precedentes para el monitoreo de las variables biofísicas del trigo. Sentinel–2 (S2) es una constelación de satélites que forma parte de las misiones Sentinel del programa Copernicus de EO. El período de revisita, así como su resolución espacial y espectral, han convertido a S2 en un sistema de EO trascendental para el monitoreo de VBV. Los sistemas ópticos de EO se ven limitados con frecuencia por las condiciones climáticas tales como nubosidad o precipitaciones. En este sentido, la tecnología radar, presenta nuevas oportunidades para el monitoreo de VBV que deben explorarse en profundidad. Sentinel–1 (S1) es una constelación radar de la familia Sentinel. Debido a la complejidad de la interacción de la señal radar con las superficies cultivadas y al ruido aditivo inherente de speckle, la estimación de VBV con tecnología radar aún sigue siendo un desafío. El objetivo de esta tesis doctoral es desarrollar modelos de estimación de variables biofísicas del trigo, en una zona irrigada de cultivo intensivo al sureste de Argentina, basados en medidas in situ de la vegetación, a partir de: i) datos multiespectrales de S2; ii) datos radar de S1; y iii) la sinergia S1 & S2. Para abordar la problemática planteada, se desarrollaron en primer lugar, modelos de estimación del índice de área foliar, del contenido de clorofila de la cubierta vegetal y del contenido de agua del trigo, utilizando una base de datos multitemporal de VBV tomadas in situ, algoritmos de aprendizaje automático, una base de datos de espectros de reflectividad bidireccional de la vegetación simulados con un modelo de transferencia radiativa y datos multiespectrales de S2. Se obtuvieron modelos híbridos de estimación de estas VBV que se ajustaron con alta precisión a los datos de campo y se logró reconstruir con éxito la curva fenológica del cultivo de trigo. En segundo lugar, se implementó un modelo de estimación de LAI basado en datos radar de S1 adquiridos en diferentes geometrías de adquisición. Se probó que la estructura tridimensional de la vegetación cuando es observada desde ángulos de incidencia local diferentes proporciona información muy valiosa que puede ser utilizada para mejorar los modelos existentes. Por último, se desarrolló una estrategia de fusión de datos de S1 & S2 para reconstruir series temporales de VWC. Se aplicaron varios modelos de procesos Gaussianos de salidas múltiples para analizar la correlación cruzada existente, en el dominio de la frecuencia, entre los canales ópticos y radar. La combinación sinérgica de datos radar y ópticos mostró ser un novedoso enfoque para abordar el monitoreo de variables biofísicas del trigo en regiones intensamente cultivadas con frecuente nubosidad

Cereal yield modeling in Finland using optical and radar remote sensing : Special Issue Global Croplands

Peer reviewe

Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

Agricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships

Quantitative Estimation of Surface Soil Moisture in Agricultural Landscapes using Spaceborne Synthetic Aperture Radar Imaging at Different Frequencies and Polarizations

Soil moisture and its distribution in space and time plays an important role in the surface energy balance at the soil-atmosphere interface. It is a key variable influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Due to their large spatial variability, estimation of spatial patterns of soil moisture from field measurements is difficult and not feasible for large scale analyses. In the past decades, Synthetic Aperture Radar (SAR) remote sensing has proven its potential to quantitatively estimate near surface soil moisture at high spatial resolutions. Since the knowledge of the basic SAR concepts is important to understand the impact of different natural terrain features on the quantitative estimation of soil moisture and other surface parameters, the fundamental principles of synthetic aperture radar imaging are discussed. Also the two spaceborne SAR missions whose data was used in this study, the ENVISAT of the European Space Agency (ESA) and the ALOS of the Japanese Aerospace Exploration Agency (JAXA), are introduced. Subsequently, the two essential surface properties in the field of radar remote sensing, surface soil moisture and surface roughness are defined, and the established methods of their measurement are described. The in situ data used in this study, as well as the research area, the River Rur catchment, with the individual test sites where the data was collected between 2007 and 2010, are specified. On this basis, the important scattering theories in radar polarimetry are discussed and their application is demonstrated using novel polarimetric ALOS/PALSAR data. A critical review of different classical approaches to invert soil moisture from SAR imaging is provided. Five prevalent models have been chosen with the aim to provide an overview of the evolution of ideas and techniques in the field of soil moisture estimation from active microwave data. As the core of this work, a new semi-empirical model for the inversion of surface soil moisture from dual polarimetric L-band SAR data is introduced. This novel approach utilizes advanced polarimetric decomposition techniques to correct for the disturbing effects from surface roughness and vegetation on the soil moisture retrieval without the use of a priori knowledge. The land use specific algorithms for bare soil, grassland, sugar beet, and winter wheat allow quantitative estimations with accuracies in the order of 4 Vol.-%. Application of remotely sensed soil moisture patterns is demonstrated on the basis of mesoscale SAR data by investigating the variability of soil moisture patterns at different spatial scales ranging from field scale to catchment scale. The results show that the variability of surface soil moisture decreases with increasing wetness states at all scales. Finally, the conclusions from this dissertational research are summarized and future perspectives on how to extend the proposed model by means of improved ground based measurements and upcoming advances in sensor technology are discussed. The results obtained in this thesis lead to the conclusion that state-of-the-art spaceborne dual polarimetric L-band SAR systems are not only suitable to accurately retrieve surface soil moisture contents of bare as well as of vegetated agricultural fields and grassland, but for the first time also allow investigating within-field spatial heterogeneities from space

Constraining the carbon budgets of croplands with Earth observation data

Cropland management practices have traditionally focused on maximising the production of food, feed and fibre. However, croplands also provide valuable regulating ecosystem services, including carbon (C) storage in soil and biomass. Consequently, management impacts the extents to which croplands act as sources or sinks of atmospheric carbon dioxide (CO2). And so, reliable information on cropland ecosystem C fluxes and yields are essential for policy-makers concerned with climate change mitigation and food security. Eddy-covariance (EC) flux towers can provide observations of net ecosystem exchanges (NEE) of CO2 within croplands, however the tower sites are temporally and spatially sparse. Process-based crop models simulate the key biophysical mechanisms within cropland ecosystems, including the management impacts, crop cultivar, soil and climate on crop C dynamics. The models are therefore a powerful tool for diagnosing and forecasting C fluxes and yield. However, crop model spatial upscaling is often limited by input data (including meteorological drivers and management), parameter uncertainty and model complexity. Earth observation (EO) sensors can provide regular estimates of crop condition over large extents. Therefore, EO data can be used within data assimilation (DA) schemes to parameterise and constrain models. Research presented in this thesis explores the key challenges associated with crop model upscaling. First, fine-scale (20-50 m) EO-derived data, from optical and radar sensors, is assimilated into the Soil-Plant-Atmosphere crop (SPAc) model. Assimilating all EO data enhanced the simulation of daily C exchanges at multiple European crop sites. However, the individually assimilation of radar EO data (as opposed to combined with optical data) resulted in larger improvements in the C fluxes simulation. Second, the impacts of reduced model complexity and driver resolution on crop photosynthesis estimates are investigated. The simplified Aggregated Canopy Model (ACM) – estimating daily photosynthesis using coarse-scale (daily) drivers – was calibrated using the detailed SPAc model, which simulates leaf to canopy processes at half-hourly time-steps. The calibrated ACM photosynthesis had a high agreement with SPAc and local EC estimates. Third, a model-data fusion framework was evaluated for multi-annual and regional-scale estimation of UK wheat yields. Aggregated model yield estimates were negatively biased when compared to official statistics. Coarse-scale (1 km) EO data was also used to constrain the model simulation of canopy development, which was successful in reducing the biases in the yield estimates. And fourth, EO spatial and temporal resolution requirements for crop growth monitoring at UK field-scales was investigated. Errors due to spatial resolution are quantified by sampling aggregated fine scale EO data on a per-field basis; whereas temporal resolution error analysis involved re-sampling model estimates to mimic the observational frequencies of current EO sensors and likely cloud cover. A minimum EO spatial resolution of around 165 m is required to resolve the field-scale detail. Monitoring crop growth using EO sensors with a 26-day temporal resolution results in a mean error of 5%; however, accounting for likely cloud cover increases this error to 63%

Optical and radar remote sensing applied to agricultural areas in europe

The global population growth, as well as the social and economic importance that the agricultural sector has in many regions of the world, makes it very important to develop methods to monitor the status of crops, to improve their management, as well as to be able to make early estimates of the agricultural production. One of the main causes of uncertainty in the production of crops is due to the weather, for example, in arid and semiarid regions of the world, periods of drought can generate big losses in agricultural production, which may result in famine. Thus, FAO, during their summit in June 2008, stressed the need to increase agricultural production as a measure to strengthen food security and reduce malnutrition in the world. Concern for increasing crop production, has generated, during the last decades, significant changes in agricultural techniques. For example, there has been a widespread use of pesticides, genetically modified crops, as well as an increase in intensive farming. In turn, the market influences crop rotations, and as a consequence, changes in the spatial distribution of crops are very common. Therefore, in order to make estimates of agricultural production, it is also necessary to map regularly the crop fields, as well as their state of development. The aim of this thesis is to develop methods based on remote sensing data, in the radar and optical spectral regions, in order to monitor crops, as well as a to map them. The results of this thesis can be combined with other techniques, especially with models of crop growth, to improve the prediction of crops. The optical remote sensing methods for classifying and for the cartography of crops are well established and can be considered almost operational. The disadvantage of the methods based on optical data is that they are not applicable to regions of the world where cloud coverage is frequent. In such cases, the use of radar data is more advisable. However, the classification methods using radar data are not as well established as the optical ones, therefore, there is a need for more scientific studies in this field. As a consequence, this thesis focuses on the classification of crops using radar data, particularly using AIRSAR airborne data and ASAR satellite data

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops