Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Intra-field Canopy Nitrogen Retrieval from Unmanned Aerial Vehicle Imagery for Wheat and Corn Crops in Ontario, Canada

The optimization of crop nitrogen fertilization to accurately predict and match the nitrogen (N) supply to the crop N demand is the subject of intense research due to the environmental and economic impact of N fertilization. Excess N could seep into the water supplies around the field and cause unnecessary spending by farmers. Understanding the detailed spatial information about a crop status is known as a farming management technique called precision agriculture, which allows farmers to maximize their yield and profit while reducing the inputs of fertilizers, pesticides, water, and insecticides. The goal of this study is to document and test the applicability and feasibility of using Unmanned Aerial Vehicle (UAV) to predict nitrogen weight of wheat and corn fields in south-west Ontario. This is investigated using various statistical modelling techniques to achieve the best accuracy. Machine learning techniques such as Random Forests and Support Vector Regression are used, which provide more robust models than traditional linear regression models. The results demonstrate that most spectral indices have a non-linear relationship with canopy nitrogen weight and show high degree of multicollinearity among the variables. In this thesis, the final nitrogen prediction maps of wheat and corn fields using UAV images and the derived models are provided

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Development and Extrapolation of a General Light Use Efficiency Model for the Gross Primary Production

The global carbon cycle is one of the large biogeochemical cycles spanning all living and non-living compartments of the Earth system. Against the background of accelerating global change, the scientific community is highly interested in analyzing and understanding the dynamics of the global carbon cycle and its complex feedback mechanism with the terrestrial biosphere. The international network FLUXNET was established to serve this aim with measurement towers around the globe. The overarching objective of this thesis is to exploit the powerful combination of carbon flux measurements and satellite remote sensing in order to develop a simple but robust model for the gross primary production (GPP) of vegetation stands. Measurement data from FLUXNET sites as well as remote sensing data from the NASA sensor MODIS are exploited in a data-based model development approach. The well-established concept of light use efficiency is chosen as modeling framework. As a result, a novel gross primary production model is established to quantify the carbon uptake of forests and grasslands across a broad range of climate zones. Furthermore, an extrapolation scheme is derived, with which the model parameters calibrated at FLUXNET sites can be regionalized to pave the way for spatially continuous model applications

Besoin en eau et rendements des céréales en Méditerranée du Sud : observation, prévision saisonnière et impact du changement climatique

Le secteur agricole est l'un des piliers de l'économie marocaine. En plus de contribuer à 15% au Produit Intérieur Brut (PIB) et de fournir 35% des opportunités d'emploi, il a un impact sur les taux de croissance. Ces dernières sont affectées négativement ou positivement par les conditions climatiques et la pluviométrie en particulier. Lors des années de sécheresse, caractérisées par une baisse de la production agricole, en particulier celle des céréales, la croissance de l'économie marocaine a été sévèrement affectée et les importations alimentaires du royaume ont augmenté de manière significative. Dans ce contexte, il est important d'évaluer l'impact de la sécheresse agricole sur les rendements céréaliers et de développer des modèles de prévision précoce des rendements, ainsi que de déterminer l'impact futur du changement climatique sur le rendement du blé et leurs besoins en eau. Le but de ce travail est, premièrement, d'approfondir la compréhension de la relation entre le rendement des céréales et la sécheresse agricole au Maroc. Afin de détecter la sécheresse, nous avons utilisé des indices de sécheresse agricole provenant de différentes données satellitaires. En outre, nous avons utilisé les sorties du système d'assimilation des données terrestres (LDAS). Deuxièmement, nous avons développé des modèles empiriques de la prévision précoce des rendements des céréales à l'échelle provinciale. Pour atteindre cet objectif, nous avons construit des modèles de prévision en utilisant des données multi-sources comme prédicteurs, y compris des indices basés sur la télédétection, des données météorologiques et des indices climatiques régionaux. Pour construire ces modèles, nous nous sommes appuyés sur des algorithmes de machine learning tels que : Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) et eXtreme Gradient Boost (XGBoost). Enfin, nous avons évalué l'impact du changement climatique sur le rendement du blé et ses besoins en eau. Pour ce faire, nous nous sommes appuyés sur cinq modèles climatiques régionaux disponibles dans la base de données Med-CORDEX sous deux scénarios RCP4.5 et RCP8.5, ainsi que sur le modèle AquaCrop et nous nous sommes basés sur trois périodes, la période de référence 1991-2010, la deuxième période 2041-2060 et la troisième période 2081-2100. Les résultats ont montré qu'il y a une corrélation étroite entre le rendement des céréales et les indices de sécheresse liés à l'état de végétation pendant le stade d'épiaison (mars et avril) et qui sont liés à la température de surface pendant le stade de développement en janvier-février, et qui sont liés à l'humidité du sol pendant le stade d'émergence en novembre-décembre. Les résultats ont également montré que les sorties du LDAS sont capables de suivre avec précision la sécheresse agricole. En ce qui concerne la prévision du rendement, les résultats ont montré que la combinaison des données provenant de sources multiples a donné des meilleurs résultats que les modèles basés sur une seule source. Dans ce contexte, le modèle XGBoost a été capable de prévoir le rendement des céréales dès le mois de janvier (environ quatre mois avant la récolte) avec des métriques statistiques satisfaisants (R² = 0.88 et RMSE = 0.22 t. ha^-1). En ce qui concerne l'impact du changement climatique sur le rendement et les besoins en eau du blé, les résultats ont montré que l'augmentation de la température de l'air entraînera un raccourcissement du cycle de croissance du blé d'environ 50 jours. Les résultats ont également montré une diminution du rendement du blé jusqu'à 30% si l'augmentation du CO2 n'est pas prise en compte. Cependant, l'effet de la fertilisation au CO2 peut compenser les pertes du rendement, et ce dernier peut augmenter jusqu'à 27%. Finalement, les besoins en eau devraient diminuer de 13 à 42%, et cette diminution est associée à une modification de calendrier d'irrigation, le pic des besoins arrivant deux mois plus tôt que dans les conditions actuelles.The agricultural sector is one of the pillars of the Moroccan economy. In addition to contributing 15% in GDP and providing 35% of employment opportunities, it has an impact on growth rates that are negatively or positively affected by climatic conditions and rainfall in particular. During drought years characterized by a decline in agricultural production and in particular cereal production, the growth of the Moroccan economy was severely affected and the kingdom's food imports increased significantly. In this context, it's important to assess the impact of agricultural drought on cereal yields and to develop early yield prediction models, as well as to determine the future impact of climate change on wheat yield and water requirements. The aim of this work is, firstly to further understand the linkage between cereal yield and agricultural drought in Morocco. In order to identify this drought, we used agricultural drought indices from remotely sensed satellite data. In addition, we used the outputs of Land Data Assimilation System (LDAS). Secondly, to develop empirical models for early prediction of cereal yields at provincial scale. To achieve this goal, we built forecasting models using multi-source data as predictors, including remote sensing-based indices, weather data and regional climate indices. And to build these models, we relied on machine learning algorithms such as Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boost (XGBoost). Finally, to evaluate the impact of climate change on the wheat yield its water requirements. To do this, we relied on five regional climate models available in the Med-CORDEX database under two scenarios RCP4.5 and RCP8.5, as well as the AquaCrop model and we based on three periods, the reference period 1991-2010, the second period 2041-2060 and the third period 2081-2100. The results showed that there is a close correlation between cereals yield and drought indices related to canopy condition during the heading stage (March and April) and which are related to surface temperature during the development stage in January -February, and which are related to soil moisture during the emergence stage in November -December. The results also showed that the outputs of LDAS are able to accurately monitor agricultural drought. Concerning, cereal yield forecasting, the results showed that combining data from multiple sources outperformed models based on one data set only. In this context, the XGBoost was able to predict cereal yield as early as January (about four months before harvest) with satisfactory statistical metrics (R² = 0.88 and RMSE = 0.22 t. ha^-1). Regarding the impact of climate change on wheat yield and water requirements, the results showed that the increase in air temperature will result in a shortening of the wheat growth cycle by about 50 days. The results also showed a decrease in wheat yield up to 30% if the rising in CO2 was not taken into account. The effect of fertilizing of CO2 can offset the yield losses, and yield can increase up to 27 %. Finally, water requirements are expected to decrease by 13 to 42%, and this decrease is associated with a change in temporal patterns, with the requirement peak coming two months earlier than under current conditions

Development of Deep Learning Hybrid Models for Hydrological Predictions

The Abstract is currently unavailable, due to the thesis being under Embargo

IMPACT OF STARTER FERTILIZERS ON OPTIMUM RATE, TIMING AND EFFICIENCY OF NITROGEN FERTILIZER FOR GRAIN SORGHUM IN TEXAS

Nitrogen (N) management is critical for producing high yielding grain sorghum in Texas. Many producers utilize planter options for starter fertilizer application, including in furrow or 2x2 placements of N and phosphorus (P). N and P applied as starter fertilizers could affect optimum rate, timing and nitrogen use efficiency (NUE) of N fertilizer in grain sorghum. Advances in technology for remote sensing enable rapid acquisition of crop canopy spectral measurements. Analysis of spatial variability of crop canopy reflectance may enable site-specific nitrogen (N) management in grain sorghum. Two field studies were established in Burleson County, Texas during 2016 and 2017 to impose contrasting N status in grain sorghum. The first study employed a single side-dress application of increasing N fertilizer rates (0, 112,168,224, 280 kg ha^-1) for grain sorghum using three starter fertilizer applications. For the second study, timing (20, 42, 56, 65, 81 (2016) 32, 49, 66, 75, control (2017) days after planting) of side-dress N fertilizer application (168 kg N ha^-1) was evaluated for grain sorghum under three starter fertilizer applications. Starter fertilizer treatments (sub-plots) included ammonium polyphosphate (11-37-0) applied at 0, 56 (in-furrow) or 168 (2x2) L ha^-1. Spectral measurements (visible and NIR) of the sorghum canopy were made using ground-based at multiple dates during the growing season. Handheld sensors were also used to monitor N status throughout the season. Spectral indices and the handheld sensor values were used to evaluate and relate to crop biomass, grain yield and N content. The optimal N rate in 2016 was 168 kg ha^ -1, there was no significance across increasing rates in 2017. Starter fertilizers did not affect the increasing rates. The optimal timing of N in 2016 was <56 days after planting in 2016, in 2017 there was not a significant difference with delayed N application. Spectral measurements (visible and NIR) of the sorghum canopy were made using ground-based at multiple dates during the growing season. Handheld sensors were also used to monitor N status throughout the season. Spectral indices and the handheld sensor values were used to evaluate and relate to crop biomass, grain yield and N content