The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal Anomalies in Satellite Time Series

Consistent, continuous, and long time series of global biophysical variables derived from satellite data are required for global change research. A novel climatology fitting approach called CACAO (Consistent Adjustment of the Climatology to Actual Observations) is proposed to reduce noise and fill gaps in time series by scaling and shifting the seasonal climatological patterns to the actual observations. The shift and scale CACAO parameters adjusted for each season allow quantifying shifts in the timing of seasonal phenology and inter-annual variations in magnitude as compared to the average climatology. CACAO was assessed first over simulated daily Leaf Area Index (LAI) time series with varying fractions of missing data and noise. Then, performances were analyzed over actual satellite LAI products derived from AVHRR Long-Term Data Record for the 1981-2000 period over the BELMANIP2 globally representative sample of sites. Comparison with two widely used temporal filtering methods-the asymmetric Gaussian (AG) model and the Savitzky-Golay (SG) filter as implemented in TIMESAT-revealed that CACAO achieved better performances for smoothing AVHRR time series characterized by high level of noise and frequent missing observations. The resulting smoothed time series captures well the vegetation dynamics and shows no gaps as compared to the 50-60% of still missing data after AG or SG reconstructions. Results of simulation experiments as well as confrontation with actual AVHRR time series indicate that the proposed CACAO method is more robust to noise and missing data than AG and SG methods for phenology extraction

Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning

The estimation of biophysical variables is at the core of remote sensing science, allowing a close monitoring of crops and forests. Deriving temporally resolved and spatially explicit maps of parameters of interest has been the subject of intense research. However, deriving products from optical sensors is typically hampered by cloud contamination and the trade-off between spatial and temporal resolutions. In this work we rely on the HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to generate long gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectances. An artificial neural network is trained on PROSAIL inversion to predict monthly biophysical variables at 30 m spatial resolution with associated, realistic uncertainty bars. We emphasize the need for a more thorough analysis of uncertainty, and propose a general and scalable approach to combine both epistemic and aleatoric uncertainties by exploiting Monte Carlo (MC) dropout techniques from the trained artificial network and the propagation of HISTARFM uncertainties through the model, respectively. A model recalibration was performed in order to provide reliable uncertainties. We provide new high resolution products of several key variables to quantify the terrestrial biosphere: Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Canopy Water Content (CWC) and Fractional Vegetation Cover (FVC) are at 30 m Landsat spatial resolution and over large continental areas. Two study areas are considered: the large heterogeneous but moderately cloud covered contiguous United States, and the homogeneous but largely cloud covered Amazonia. The produced vegetation products largely agree with the test dataset (R = 0.90, RMSE = 0.80 m2/m2 and ME = 0.12 m2/m2 for LAI, and R = 0.98, RMSE = 0.07 and ME = 0.01 for FAPAR) providing low error and high accuracy. Additionally, the validation considers a thorough comparison with operational and largely validated medium resolution products, such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Copernicus Global Land Service. Our products presented a good agreement and consistency with both MODIS (R = 0.84 and R = 0.85 for LAI and FAPAR, respectively) and Copernicus (R = 0.92 and R = 0.91 for LAI and FAPAR, respectively). To foster a wider adoption and reproducibility of the methodology we provide an application in GEE and source code at:https://github.com/IPL-UV/ee_BioNet

Development of an earth observation processing chain for crop biophysical parameters at local and global scale

This thesis’ topics embrace remote sensing for Earth observation, specifically in Earth vegetation monitoring. The Thesis’ main objective is to develop and implement an operational processing chain for crop biophysical parameters estimation at both local and global scales from remote sensing data. Conceptually, the components of the chain are the same at both scales: First, a radiative transfer model is run in forward mode to build a database composed by simulations of vegetation surface reflectance and concomitant biophysical parameters associated to those spectrum. Secondly, the simulated database is used for training and testing nonlinear and non-parametric machine learning regression algorithms. The best model in terms of accuracy, bias and goodness-of-fit is then selected to be used in the operational retrieval chain. Once the model is trained, remote sensing surface reflectance data is fed into the trained model as input in the inversion process to retrieve the biophysical parameters of interest at both local and global scales depending on the inputs spatial resolution and coverage. Eventually, the validation of the leaf area index estimates is performed at local scale by a set of ground measurements conducted during coordinated field campaigns in three countries during 2015 and 2016 European rice seasons. At global scale, the validation is performed through intercomparison with the most relevant and widely validated reference biophysical products. The work elaborated in this Thesis is structured in six chapters including an introduction of remote sensing for Earth observation, the developed processing chain at local scale, the ground LAI measurements acquired with smartphones, the developed chain at global scale, a chapter discussing the conclusions of the work, and a chapter which includes an extended abstract in Valencian. The Thesis is completed by an annex which include a compendium of peer-reviewed publications in remote sensing international journals

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)

Optimized and automated estimation of vegetation properties: Opportunities for Sentinel-2

La Biosfera es uno de los principales sistemas que conforman la Tierra. Su estudio permite comprender la relación entre la vegetación y el ciclo del carbono y cómo éste puede ser afectado por los cambios en los niveles de CO2 y los usos de suelo. Para el estudio de estas dinámicas a escala global y local, han sido desarrollados diversos modelos que son representaciones de la realidad en una escala y complejidad más simple. Parte de las variables de entrada de estos modelos son obtenidas mediante medidas de teledetección gracias al Global Climate Observing System (GCOS), que ha determinado un conjunto de 50 variables climáticas esenciales que contribuyen a los estudios de cambio climático que lidera la Convención Marco de las Naciones Unidas y el Panel Intergubernamental del Cambio Climático. En esta lista está incluido el índice de área foliar (LAI).El contenido de clorofila en hoja (LCC) es otro parámetro biofísico clave para los estudios de biosfera. El estudio de las propiedades de la vegetación desde el espacio requiere: (1) Métodos óptimos para el procesamiento y la estimación de la información y, (2) Disponibilidad de datos espaciales. Los métodos de procesado y estimación de parámetros biofísicos son necesarios ya que el sensor solo mide los flujos de energía reflejados por las cubiertas vegetales distribuidos espacialmente. Por ello, han sido desarrollados diversos modelos, que van desde complejos modelos con base física hasta modelos estadísticos o la combinación de los anteriores. En el desarrollo de esta tesis se ha reunido una amplia variedad de ellos. la Agencia Espacial Europea (ESA) ha desarrollado la misión Sentinel-2 que está especialmente diseñada para el monitoreo de las propiedades de la vegetación, con las capacidades operativas que cumplen los requerimientos espectrales, espaciales y temporales. Los datos que proporcionará la misión Sentinel-2 permitirán garantizar la continuidad de las misiones Spot y Landsat, aportando un tiempo de revisita menor, mejora de la amplitud de barrido, mayor resolución espectral y una mejor calibración y calidad de imagen. Para el procesamiento y la extracción de información de parámetros biofísicos han sido desarrollados diferentes paquetes computacionales por diversos grupos de investigación. Esta tesis pretende suministrar un conjunto de herramientas computacionales, dinámicas y flexibles que permitan automatizar y evaluar el potencial de los diferentes métodos que en la actualidad han sido publicados y están disponibles para su libre uso. Presenta los resultados científicos de la evaluación del impacto de diferentes parámetros de ajuste en los principales métodos de estimación de parámetros biofísicos, centrándonos en datos simulados del satélite Sentinel-2, previsto para ser lanzado en 2015. Para dicho trabajo se han reunido los principales métodos de estimación que van desde las simples relaciones espectrales hasta los complejos modelos de transferencia radiativa (RTM). Para esto, hemos implementado un conjunto de herramientas informáticas que permiten el diseño y evaluación de diversas estrategias de regularización como son la normalización de los datos, la sinergia entre datos simulados por RTM y datos de campañas de campo o de laboratorio, adición de modelos de ruido a los datos simulados y un amplio conjunto de métodos de regresión tanto paramétricos como no paramétricos. Este trabajo constituye la continuación de mi trabajo Final del Máster de Teledetección, donde he desarrolló una herramienta informática llamado ARTMO (por sus siglas en inglés Automated Radiative Transfer Models Operator) que reunió los RTM de la familia Prospect, SAIL y FLIGTH. Se implementó el método de estimación por tablas de búsqueda (LUT). Esta tesis presenta la evolución de ARTMO que pasa de ser una herramienta informática rígida que no permitía de manera sencilla la ampliación de sus funciones, a un flexible marco de desarrollo (framework software), donde ARTMO se convierte en una plataforma de soporte de diversos módulos implementados de manera independiente. Esta nueva versión de ARTMO permite a cualquier grupo de investigación desarrollar y compartir nuevas funciones, algoritmos y métodos de estimación de parámetros biofísicos. Además, hemos establecido las bases para la creación de una red tanto de usuarios como de desarrolladores en torno al estudio de las propiedades de la vegetación, sirviendo de apoyo para el estudio de nuevos algoritmos de estimación, diseño de nuevos sensores ópticos o para su uso en el campo de la educación.The biosphere is one of the main components of the Earth’s system since it regulates exchanges of energy and mass fluxes at the soil, vegetation and atmosphere level. To know the links between vegetation and the terrestrial energy, water and carbon cycles, and how these might change due to eco-physiological responses to elevated CO2 and changes in land use is of vital importance for the study of the biosphere. To study these exchanges, several kinds of models (scale and target) have been developed. In view of these models, the Global Climate Observing System (GCOS) aims to provide comprehensive information on the total climate system, involving a multidisciplinary range of physical, chemical and biological properties, and atmospheric, oceanic, hydrological, cryospheric and terrestrial processes. Fifty GCOS Essential Climate Variables (ECVs) are required to support the work of the United Nations Framework Convention on Climate Change (UNFCCC) and the Intergovernmental Panel on Climate Change. In support of these terrestrial models, but also in support of monitoring local-to-global vegetation dynamics, this Thesis focuses on improved estimation of vegetation properties from optical RS data, and more specifically leaf area index (LAI) and leaf chlorophyll content (LCC). Although LCC is currently not considered as an ECV due to the lack of a globally applicable retrieval algorithm, it is a key variable in vegetation studies. Monitoring the distribution and changes of LAI and LCC is important for assessing growth and vigour of vegetation on the planet. The quantification of these essential vegetation properties are fundamentally important in land-atmosphere processes and parametrization in climate models. LAI variable represents the amount of leaf material in ecosystems and controls the links between biosphere and atmosphere through various processes such as photosynthesis, respiration, transpiration and rain interception. LCC provides important information about the physiological status of plants and photosynthetic activity, therefore is related to the nitrogen content, water stress and yield forecasting The European Space Agency (ESA)’s forthcoming Sentinel-2 mission is particularly tailored to the monitoring vegetation properties mapping, with operational monitoring capabilities that goes beyond any existing operational mission. A pair of Sentinel-2 polar-orbiting satellites will provide systematic global acquisitions of high-resolution multispectral imagery (10-60 m) with a high revisit frequency on a free and open data policy basis. With the pair of satellites in operation it has a revisit time of five days at the equator (under cloud-free conditions) and 2–3 days at mid-latitudes. Sentinel-2 images will be used to derive the highly prioritized time series of ECVs such as LAI. Sentinel-2 images will also be used provide various experimental variables, e.g. biochemical variables such as LCC. This Thesis is dedicated to tackle the stated recommendation and turn it into consolidated guidelines. The undertaken road map was to work on both generating scientific outputs, as well on developing software to automate the retrieval routines. All essential tools to deliver a prototype retrieval approach that could be embedded into an operational Sentinel-2 processing scheme have been prepared into a scientific software package called ARTMO (Automated Radiative Transfer Models Operator). Physically-based approaches but also latest statisticallybased methods have been implemented into the software package and systematically evaluated. The retrieval methods have been applied to the estimation of LAI and LCC from simulated Sentinel-2 data, but the majority of investigated methods can essentially be applied to derive any detectable vegetation biochemical or biophysical variable. The fundamentals of ARTMO has been laid during J.P. Rivera’s MSc thesis project and has been further developed during the course of my PhD Thesis. The toolbox is built on a suite of radiative transfer models and image processing modules in a modular graphical user interface (GUI) environment. ARTMO has been mainly developed and tested for processing (simulated) Sentinel-2 data in a semiautomatic way, but in principle data from any optical sensor can be processed

Radiative transfer model inversion using high-resolution hyperspectral airborne imagery – Retrieving maize LAI to access biomass and grain yield

Mapping crop within-field yield variability provide an essential piece of information for precision agriculture applications. Leaf Area Index (LAI) is an important parameter that describes maize growth, vegetation structure, light absorption and subsequently maize biomass and grain yield (GY). The main goal for this study was to estimate maize biomass and GY through LAI retrieved from hyperspectral aerial images using a PROSAIL model inversion and compare its performance with biomass and GY estimations through simple vegetation index approaches. This study was conducted in two separate maize fields of 12 and 20 ha located in north-west Mexico. Both fields were cultivated with the same hybrid. One field was irrigated by a linear pivot and the other by a furrow irrigation system. Ground LAI data were collected at different crop growth stages followed by maize biomass and GY at the harvesting time. Through a weekly/biweekly airborne flight campaign, a total of 19 mosaics were acquired between both fields with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400 to 850 nanometres (nm) at different crop growth stages. The PROSAIL model was calibrated and validated for retrieving maize LAI by simulating maize canopy spectral reflectance based on crop-specific parameters. The model was used to retrieve LAI from both fields and to subsequently estimate maize biomass and GY. Additionally, different vegetation indices were calculated from the aerial images to also estimate maize yield and compare the indices with PROSAIL based estimations. The PROSAIL validation to retrieve LAI from hyperspectral imagery showed a R² value of 0.5 against ground LAI with RMSE of 0.8 m²/m². Maize biomass and GY estimation based on NDRE showed the highest accuracies, followed by retrieved LAI, GNDVI and NDVI with R² value of 0.81, 0.73, 0.73 and 0.65 for biomass, and 0.83, 0.69, 0.73 and 0.62 for GY estimation, respectively. Furthermore, the late vegetative growth stage at V16 was found to be the best stage for maize yield prediction for all studied indices

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation

HemiPy: A Python module for automated estimation of forest biophysical variables and uncertainties from digital hemispherical photographs

Digital hemispherical photography (DHP) is widely used to derive forest biophysical variables including leaf, plant, and green area index (LAI, PAI and GAI), the fraction of intercepted photosynthetically active radiation (FIPAR), and the fraction of vegetation cover (FCOVER). However, the majority of software packages for processing DHP data are based on a graphical user interface, making programmatic analysis difficult. Meanwhile, few natively support analysis of RAW image formats, while none incorporate the propagation or provision of uncertainties. To address these limitations, we present HemiPy, an open‐source Python module for deriving forest biophysical variables and uncertainties from DHP images in an automated manner. We assess HemiPy using simulated hemispherical images, in addition to multiannual time‐series and litterfall data from several forested National Ecological Observatory Network (NEON) sites, as well as comparison against the CAN‐EYE software package. Multiannual time‐series of PAI, FIPAR and FCOVER demonstrate HemiPy's outputs realistically represent expected temporal patterns. Comparison against litterfall data reveals reasonable accuracies are achievable, with RMSE values close to the error of ~1 unit typically attributed to optical LAI measurement approaches. HemiPy's PAI, FIPAR and FCOVER outputs demonstrate good agreement with CAN‐EYE. Consistent with previous studies, when compared to simulated hemispherical images, better agreement is observed for PAI derived using gap fraction near the hinge angle of 57.5° only, as opposed to values derived using gap fraction over a wider range of zenith angles. HemiPy should prove a useful tool for processing DHP images, and its open‐source nature means that it can be adopted, extended and further refined by the user community