CENTRO DE EXPERIMENTACIÓN PESQUERA DE ASTURIAS

essen-tial inversion processing steps together in one GUI toolbox. The toolbox should: (1) consist of a suite of leaf and canopy models, (2) be user friendly, (3) allow easy comparison of different models, (4) automate retrieval strategies to map vegetation properties, and (5) be freely avail-able. A scientific GUI toolbox that aims to cover all these points has been developed during this Thesis project, called ARTMO.

2.5 ARTMO: Software framework for vegetation properties retrieval

ARTMO (Automated Radiative Transfer Models Operator) is a scientific GUI software package developed at the Laboratory of Earth Observation (LEO) at the University of Valencia [Verrelst et al., 2011; Rivera, 2011]. The software structure has been developed according to a three-tier architecture and two connecting levels in MATLAB and connected with a MySQL server running underneath. A detailed description of the architecture can be found in [Rivera, 2011].

Since its first version, the ARTMO package consists of three different conceptual blocks: (1) radiative transfer models (RTMs), (2) Retrieval toolboxes and (3) Tools. Figure 2.10 shows the RTMs, retrieval algorithms and tools that was implemented in v1.00 [Rivera, 2011].

FIGURE2.10: ARTMO’s v.1.00 RTMs, inversion module and tools as developed in [Rivera, 2011].

A major weakness of ARTMO v1.00, however, was that its logical tier structure did not permit the expansion of its modules. Therefore, one of the Thesis’ objectives was to improve the conceptual development and implementation of ARTMO as a "modular software framework" by redesigning its structure according to a dynamic extendable modular architecture. It eventually led to the current version 3.03, which is modular and consists of various new modules. By following a basic logical structure that allows communication between the included modules, the software package can be customized and extended by any programmer. The conceptual development of the modular software framework is hereafter presented. A general description of each module along with Manual and Installation Guides can be consulted at ARTMO’s website:

http://ipl.uv.es/artmo/.

2.5.1 ARTMO’s software framework

From v3.00 onwards, ARTMO is built out of four different module types: (1) Main, (2) radia-tive transfer models (RTMs), (3) Retrieval toolboxes, and (4) Tools. The latter three are now removable modules that are built on top of the Main module. Figure 2.11 shows the general outline of the modular software framework. Each module provides the programmer with a stan-dardized set of properties and functions that allow communication between modules. The main module is responsible for creating the underlying MySQL database and provides the script that takes care of the communication between the modules and the underlying database. The RTM module runs the configured radiative transfer models, the Retrieval toolboxes analyze the perfor-mance of different biophysical parameter retrieval methods, and Tools module provides scripts for post-processing information generated by the RTM module. Each module is formed by one or more ’processing units’, which defines a set of scripts and GUIs that executes a specific task.

FIGURE2.11: Module’s outline of the ARTMO Framework.

The architecture of all modules has been developed in a structure of three tiers (client, logic anddatabase) and two connection levels.

Theclient tier consists of a set of GUIs developed for entering and reading input and out-put data to each element of the logical layer. This structure is applied to each modules. The logic tier consists of a set of four basic elements: (1) builder, (2) connector, (3) core and (4) auxiliary. Each element is formed by one or more scripts; its behavior depends on the module it belongs. Thedatabase tier contains the logical database element. This element is common to all modules. The database is designed in MySQL. The first level refers to all elements to be installed locally and consists of the presentation (i.e., all GUIs) and logic tier (i.e. all scripts), the second level refers to the communication between the logic tier and the MySQL database.

The database can be accessed remotely. Figure 2.12 shows the layout of components and com-munication between components. The four module types are subsequently described according to the basic elements.

2.5 ARTMO: SOFTWARE FRAMEWORK FOR VEGETATION PROPERTIES RETRIEVAL 25

FIGURE2.12: Visual overview of Three-tiered modules in ARTMO framework.

Main module:

 Builder: Stores default information of the general settings and the different main GUI menus of the ARMO Framework.

 Core: Is responsible for checking and storing each processing unit configuration data in order to allow the communication among all modules.

 Connector: creates the database and contains a set of auxiliary scripts that allows the communication between all modules of logical tier and database.

RTM module:

 Builder: Stores all the input and output setup parameters of RTM and the configuration of tables created by the connector element; also creates sub-menus in each menu of the main GUI. Table 2.1 lists ARTMO’s implemented RTMs. In this module each implemented RTM is a processing unit.

 Core: Runs the main script of the configured RTM.

 Connector: Defines the MySQL storage syntaxes of input and output data to each RTM.

For instance, Figure 2.13 illustrates a relational database structure for PROSPECT-4.

TABLE2.1: RTMS implemented into ARTMO

Model scale Reference language

Prospect-4 leaf Feret et al. [2008] Matlab

Prospect-5 leaf Feret et al. [2008] Matlab

FluorMODleaf leaf Pedrós et al. [2010] Compile file

DLM leaf Stuckens et al. [2009] Matlab

4SAIL canopy Verhoef et al. [2007] Matlab

FluorSAIL canopy Zarco-Tejada et al. [2006] Compile file

FLIGHT canopy North [1996] Compile file

SLC combined (soil/leaf/canopy) Verhoef and Bach [2007] Mex file (Matlab) SCOPE combined (soil/leaf/canopy) Van Der Tol et al. [2009] Matlab

F^IGURE2.13: Table’s structure to RTM generic implemented.

Retrieval module:

 Builder: Provides basic information to the main module of the retrieval models imple-mented, creates sub-menus in each menu of the main GUI and stores the settings to run the retrieval model.

 Core: Runs the main script of the retrieval model and the different programed scripts in an auxiliary element. Table 2.2 shows retrieval algorithms implemented.

 Connector: Defines the MySQL storage syntaxes of input settings of retrieval models and the output statistics analyzed and allows the communication between the core element and the database. Chapters 3 to 5 explain all developed retrieval toolboxes in more detail.

2.5 ARTMO: SOFTWARE FRAMEWORK FOR VEGETATION PROPERTIES RETRIEVAL 27

TABLE2.2: ARTMO’s retrieval toolboxes.

Spectral Indices LookUp-Table (LUT) Machine learning regression

Generic spectral index formulations with up to 10 different bands.

63 cost functions according to Leonenko et al. [2013]

13 Machine learning regression algorithms according to Camps-Valls et al. [2013]

Tools module:

 Builder: Provides basic information to the main module, creates access in ARTMO’s main GUI window.

 Core: Processes the stored information by the processing units of the RTM module in the database.

 Connector: Takes care of the communication. The table’s syntaxes required by each processing unit is managed by the connector.

In v3.03 the following tools are fully operational:

1. Graphics tool: The Graphics tool allows the simulated spectra to be viewed and exported.

2. Sensor tool: The Sensor tool enables configuring the band settings of a specific sensor.

Once configured, an RTM can then generate outputs according to the bands settings of a chosen sensor.

Finally, it is expected that the development of modular and dynamic tools will contribute to the understanding of the interactions between solar light and vegetation elements. ARTMO’s modular framework provides a platform that not only allows researchers, developers and users of RTM to develop new methods or optimize retrieval strategies, but also presents an intuitive toolbox that supports the use and inter-comparison of various RTMs as well possibilities of executing mapping strategies for local-to-regional applications, ranging from precision farming to environmental monitoring issues.

Spectral Indices based retrieval 3

Contents

3.1 Abstract . . . 30 3.2 Introduction . . . 31 3.3 ARTMO software package . . . 33 3.4 Assessment and mapping applications . . . 39 3.5 Results & Discussion . . . 41 3.6 Conclusions . . . 49

This chapter is based on:

Juan Pablo Rivera Caicedo, Jochem Verrelst, Jesús Delegido, Frank Veroustraete and José Moreno (2014)

On the semi-automatic retrieval of biophysical parameters based on spectral index optimization.

Remote Sensing (Submitted).

29

3.1 Abstract

Regression models based on spectral indices are typically empirical formulae enabling the map-ping of biophysical parameters derived from Earth Observation (EO) data. Due to its empirical nature, it remains nevertheless uncertain to what extent a selected regression model is the most appropriate one, until all band combinations and curve fitting functions are assessed. This pa-per describes the application of a Spectral Index (SI) assessment toolbox in the Automated Radiative Transfer Models Operator (ARTMO) package. ARTMO enables semi-automatic re-trieval and mapping of biophysical parameters from optical remote sensing observations. The SI toolbox enables the assessment of biophysical parameter retrieval accuracy of established as well as new and generic SIs. For instance, based on the SI formulation used, all possible band combinations of formulations with up to ten bands can be defined and evaluated. Several options are available in the SI assessment: calibration/validation data partitioning, the addition of noise and the definition of curve fitting models. To illustrate its functioning, all two-band combinations according to simple ratio (SR) and normalized difference (ND) formulations as well as various fitting functions (linear, exponential, power, logarithmic, polynomial) have been assessed. HyMap imaging spectrometer (430-2490 nm) data obtained during the SPARC cam-paign in Barrax, Spain, have been used to extract leaf area index (LAI) and leaf chlorophyll content (LCC) estimates. For both SR and ND formulations the most sensitive regions have been identified for two-band combinations of green (539-570 nm) with longwave SWIR (2421-2453 nm) for LAI (r²: 0.83) and far-red (692 nm) with NIR (1340 nm) or shortwave SWIR (1661-1686 nm) for LCC (r²: 0.93). Polynomial, logarithmic and linear fitting functions led to similar best correlations, though spatial differences emerged when applying the functions to HyMap imagery. We suggest that a systematic SI assessment is a strong requirement in the quality assurance approach for accurate biophysical parameter retrieval.