Efficiency of SPIRITS (Software for Processing and Interpretation of

Remotely Sensed Image Time Serie) to Ecological Modeling:

New Functionalities and Use Examples

ASMAE ZBIRI1, AZEDDINE HACHMI1, FATIMA EZZAHRAE EL ALAOUI-FARIS1,

HERMAN EERENS2, DOMINIQUE HAESEN2

1Department of Biology, Mohammed V University, Faculty of Science, MOROCCO

2Vlaamse Instelling Voor Technologisch Onderzoek (VITO), BELGIUM

Abstract: ─ We studied the effectiveness of SPIRITS processing software used to monitor drought. In this

article, we propose practice steps and we prove that ecological modeling can be available with remote sensing

data on a larger scale (for any place in the world) with SPIRITS. The studies summarize some important

analyses of remote sensing time series at high temporal and medium spatial resolution. The Software for the

Processing and Interpretation of Remotely sensed Image Time Series (SPIRITS) is a stand-alone flexible

analysis environment created to facilitate the processing and analysis of large image time series and ultimately

for providing clear information about vegetation status in various graphical formats to ecological modeling.

The examples of operational analyses are taken from several recent drought monitoring articles. We conclude

with considerations on SPIRITS use also in view of data processing requirements imposed by the coming

generation of remote sensing products at high spatial and temporal resolution, such as those provided by the

Sentinel sensors of the European Copernicus program.

Key-Words: ─ SPIRITS, Processing and Interpretation, Remote sensing time series, Ecological modeling.

Received: April 25, 2021. Revised: October 17, 2022. Accepted: November 23, 2022. Published: December 26, 2022.

1 Introduction

In order to perform a perfect statistical analysis, it

is essential to ensure the quality of the time series

through a careful analysis of the dataset or sample

studied. This filtering allows the systematic

elimination of possible outliers. However, these

outliers do not always have to be eliminated, as in

exceptional cases it may be worthwhile to take

them into account in certain analyses. Scientific

researchers must be open to all new scientific data

and software. The research methods are also

developing with the advancement of technology

(Zbiri and Hachmi [1], 2022, Zbiri et al., 2022 [2],

Hachmi et al., 2021 [3]). Similarly, AI integrated

with blockchain has been found to positively

impact water management and climate control

(Lin, Petway [4]). AI can manage and reduce

energy consumption within smart cities (Şerban &

Lytras, 2020 [5]). Studies have identified that

blockchain applications can improve sustainable

practices in supply chain management and

agricultural practices (Kshetri, 2021 [6]).

Similarly, within nano-technology applications,

AI has provided benefits through better precision

in agricultural water distribution delivering

positive impacts on the efficient use of natural

resources.

There are many methods of pre-processing data

derived from remote sensing. In our study, the

processing module is used to transform the spatial

and temporal information of the various decadal

image input data as required.

Vegetation data is captured during the period

February to April, while soil moisture and

precipitation data are captured between the

months of November to February for a whole

decade. For SPIRITS users or analysts who need

to use remote sensing software or improve

programming efficiency, this is another article

about SPIRITS with an extensive collection

practical for processing remotely sensed data used

for environmental risk modeling.

The first paper was published in 2015 about

analysis for crop monitoring with the SPIRITS

software (Eerens et al., 2014 [7]).

Despite the high demand for in-house models, this

pioneering guidebook is the only complete,

focused resource of expert guidance on building

and validating accurate, state-of-the-art

Environmental risk management models. Written

by a proven authorial team with international

experience, this hands-on road map can be used

such as the fundamentals of processing data using

WSEAS TRANSACTIONS on SIGNAL PROCESSING

DOI: 10.37394/232014.2022.18.24

Asmae Zbiri, Azeddine Hachmi,

Fatima Ezzahrae El Alaoui-Faris,

Herman Eerens, Dominique Haesen

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SPIRITS software. However, many types of

indices from 360 to 552 images of a WGS-84

projection are processed with SPIRITS such us:

Copernicus Global Land Service (CGLSSWI) soil

water data for 2007-2017 version 3 from MetOp-

A / ASCAT with a spatial resolution of 11 km and

geographic coordinates (Long / Lat) (Wagner et

al., 1999 [8], Bauer-Marschallinger et al., 2018

[9]). The Normalized Difference Vegetation Index

(NDVI) is calculated from MODIS L1B Terra

surface reflectances and corrected using the

MODIS algorithms by the United States Land

Observation and Resources Center (EROS) to

produce NDVI eModis (Tucker, 1979 [10],

Jenkerson et al., 2010 [11]). Data of absorbed

photosynthetically active radiation and dry matter

productivity derived from Copernicus World

Terrestrial Service (CGLSFAPAR and

CGLSDMP) from 2007 to 2017 version 2 which

corresponds to values of reflectance absorbed by

canopy and mass flows of carbon (Claverie et al.,

2013 [12]).

The European Centre for Medium-Range Weather

Forecasting (ECMWF) is one of the best providers

of high-quality climate data series at different time

scales (Woods, 2006 [13]). The forecast charts are

free for anyone to access, redistribute and adapt -

even for commercial applications - as part of their

open data Strategy for 2021-2030

(https://www.ecmwf.int/ [14]).

The indices averages were obtained by the final

processing of the eMODIS-based inputs which

consisted of the spatial segmentation of the

imageries using a mask surrounding Moroccan

rangeland and Global Land Cover 2000 (Mayaux et

al., 2004 [15]).

In this paper, we briefly summarize the SPIRITS

architecture and main functionalities, and then we

present the latest functional developments of the

software from 2014 to today. The most commonly

used functionalities of the software are illustrated

with real case examples. In particular, we focus on

the time series analysis performed for the

production of drought monitoring on arid land.

2 Remote Sensing Data Analysis

SPIRITS

After the official presentation of SPIRITS

(Software for the Processing and Interpretation of

Remotely sensed Image Time Series) at the GSDI

conference in Addis Ababa in November 2013, the

users’ community has been rapidly growing and

increasingly providing feedback about possible

improvements beyond the main functionalities

summarized in the following sections. As a result

of this interaction with users and developers, new

versions, including the upgrades described below,

are periodically released. The latest available

version is dated March 2015 (version 1.3.0) and is

available at http://spirits.jrc.ec.europa.eu. This

section provides a detailed list of the new

developments included in version 1.3.0 (Eerens et

al., 2014 [7]).

SPIRITS is a Windows-based software aiming at

the analysis of remotely sensed earth observation

data. Although it includes a wide range of general-

purpose functionalities, the focus lies on the

processing of time series of images, derived from

low-resolution sensors such as SPOT

VEGETATION, NOAA-AVHRR, METOP-

AVHRR, TERRA-MODIS, ENVISAT-MERIS, and

MSG-SEVIRI. SPIRITS has been developed by

VITO’s

remote sensing unit on behalf of (and

sponsored by) the European Commission’s Joint

Research Centre (EC

-JRC

) in Ispra, Italy. The

JRC-MARS

group (Monitoring Agricultural

Resources) continuously supplies the EC

directorates with agro-statistical information on

crop areas and yields for Europe and the major

production areas of the world. SPIRITS is a free

software environment for analyzing satellite-

derived image time series in crop and vegetation

monitoring, available at:

https://mars.jrc.ec.europa.eu/asap/.

GLIMPSE modules can only be accessed via a

command line interface, but they can be scripted to

set up complex processing chains. SPIRITS

provides a convenient GUI, which enables you to

guide the GLIMPSE modules via an up-to-date

interface and run them in the background. However,

gradually, a number of new tools were incorporated

without a relationship with GLIMPSE, for example,

the import of imagery in external formats, reproject

data, adapt metadata of a satellite image HDR,

resampling and generation of maps, and the

extraction of regional databases (Eerens, Haesen,

2016 [16]).

VITO: Vlaamse Instelling voor Technologisch Onderzoek Flemish

Institute for Technological Research.

EC: European Commission.

JRC: Joint Research Center.

MARS: Monitoring of agricultural resources.

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Asmae Zbiri, Azeddine Hachmi,

Fatima Ezzahrae El Alaoui-Faris,

Herman Eerens, Dominique Haesen

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Generic Format Image Import

The Generic file format is designed to efficiently

store and organize large amounts of numerical data,

including satellite images. It is already used for a

number of remote sensing data sources (eMODIS-

TERRA NDVI (250 m), MetOp-A /ASCAT SWI

(12.5 km), SPOT-VEGETATION and Proba-V

Fraction of Absorbed Photosynthetically Active

Radiation (FAPAR) and Dry Matter Productivity

(DMP) (11 km) provided through the Copernicus

program) and Advanced SCATterometer sensors.

The Generic importer makes use of Image TIFF

(.tiff) with n resolution for inspecting data and

attributes and converting GeoTIFF imagery into

SPIRITS format. This tool allows a large set of

current and future data sources to be easily

integrated into the SPIRITS processing chain.

SPIRITS works with a fixed set of input image

periodicity, namely: daily, 10-daily, monthly and

annual images. The primary global daily datasets of

SM2RAIN rainfall (mm/day) employed in this

study are acquired from ASCAT at a spatial

resolution of 12.5 km (Brocca et al., 2019 [17]).

Ten years (from 2007 to 2017) of cumulative value

composite of SM2RAIN images at 250-m spatial

resolution were exploited for drought monitoring.

Simple Python and Matlab codes for the extraction

of SM2RAIN-ASCAT rainfall at one or multiple

station(s)\location(s) are available at

https://zenodo.org/record/3451685. The SM2RAIN

code in Python is available at

https://zenodo.org/record/2203560.

The SM2RAIN code in Matlab is available at

http://hydrology.irpi.cnr.it/download-area/sm2rain-

code/. A GeoTIFF version of the SM2RAIN-

ASCAT dataset is available at

https://zenodo.org/record/3520620.

With this SPIRITS toolbox, we can process and

examine time series of low and medium-resolution

sensors. It can be used to perform and automatize

many spatial and temporal processing steps on time

series and to extract spatially aggregated statistics

(Table 1). Vegetation indices and their anomalies

can be rapidly mapped and statistics can be plotted

in seasonal graphs to be shared with analysts and

decision-makers (Figures 1 and 2).

Table 1. Main SPIRITS functionalities according to the SPIRITS program menu.

Create location

of processed

files

Firstly we must activate SPIRITS commanded Windows in ‘libs/util’ files and Launch GLIMPSE/Setup GLIMPSE in the

‘GLIMPSE’ file. Start work with SPIRITS Executable Jar File/SPIRITS/General Menu. We can use the File tool to create a new

project or to adapt HDR.

Import data

(Images)

The first important thing when we import an image from Geotiff to ENVI form is the name of IMG :

(prefix_(YYYYMMDDsufix). Exp: NDVI_20210301i/Period (YYYYMMDD).

Rename

Specification: 2001001 = .*.

Filename: "africa_north.*.C05.NDVI.MOD44.D16.R000250.MODAPS.v1"

- Input pattern: africa_north.*.C05.NDVI.MOD44.D16.R000250.MODAPS.v1*

- Output pattern: a_%0i

- In case the filenames have extensions,

Exp: "africa_north.*.C05.NDVI.MOD44.D16.R000250.MODAPS.v1.img" and

"africa_north.*.C05.NDVI.MOD44.D16.R000250.MODAPS.v1.hdr"

- Input pattern africa_north.*.C05.NDVI.MOD44.D16.R000250.MODAPS.v1.*

- Output pattern a_%0i%1

Adapt HDR

Add a date in image HDR without a date or period (YYYYMMDD).

We also find the data needed for the map info:

Origin = (-2890000.000000000000000,4182500.000000000000000)

Pixel Size = (250.000000000000000,-250.000000000000000)

Scaling and

Reclassification

Rescale, reclass or modify the original image values, and change the data type. The overlay of the land cover layer data and the

corresponding satellite image consists of producing the information contained in each pixel of this image. To extract the values

of each pixel, we superimposed a vector file of the pastoral areas on a raster of the rangeland classes studied.

Reprojecting

Using the EPSG (4326) for output we convert the file from the arbitrary spatial reference to the spheroid projection/ or using

existing HDR. Using the wkt file we created from the spatial reference set of the original tiff.

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Fatima Ezzahrae El Alaoui-Faris,

Herman Eerens, Dominique Haesen

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Rasterize SHP

The vector shapefile is converted into a raster file while keeping all the significant information such as the different regions and

their administrative boundaries in the ten Moroccan pastoral zones. This means that a vector that contains ten areas will be

transformed into a ten-area raster for coordinates (X/Y) plus spatial information or metadata of an identical satellite image. The

type of input field determines the type of output raster. Rasterize an ESRI shapefile (SHP) into an ENVI raster image file (IMG)

while keeping all the significant information. The raster file will be used in classes to create the RUM.

Extract ROI

The zoning corresponds to the division of the territory concerned by the analysis of its contour following a technique of

reduction of certain information included in the images. This zoning will make it possible to locate the samples. Two types of

zoning are envisaged: one based on the administrative boundaries of the rangelands; and one based on the application of a mask

whose vector layer is in raster format. Corresponds to the division of the territory concerned by the analysis of its contour

following a technique of reduction of certain information included in the images. ROI will make it possible to locate the

samples.

Resampling

he resolution of the image describes its content: the higher the resolution, the more detailed the image content. The resolution of

a digital image can be of different types: spatial, spectral, temporal, or radiometric. Our digital images, used in this study, will

be in spatial resolution. Modification of the resolution, or resampling of the input data, is necessary for SPIRITS calculations to

be performed at the same spatial scale. The resampling method allows having the same spatial information for the input and

output images, by changing the resolution in the increasing or decreasing direction.

Extract RUM

Prepare RUM database for the data which will be extracted from input images, using rasterized shp as regions. In this case

without distinguishing land-use classes - just extracting overall mean values for the regions.

SPIRITS processing has been carried out for the

evaluation of image times series used to study

drought in Moroccan rangeland. However, these

steps are standard for any issue and area (cropland,

forest, water, and ocean). Aside from other

programs, this interface helps you to view your

images, convert their formats, and change their

pixel sizes and projections. Thus, the calculation of

an average image of a large series of images is

quickly performed by spirits. The current study was

taken up to investigate the utility of spatiotemporal

analysis established by a multidisciplinary program

that can handle enough satellite data processing

problems.

Fig. 1: SPIRITS tasks and results extract from software developers presentation/“VITO_Unesco_Brazil_201607_SPIRIT

S_Intro”.

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Fatima Ezzahrae El Alaoui-Faris,

Herman Eerens, Dominique Haesen

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Fig. 2: SPIRITS Quick Look of SM2RAIN anomaly.

3 Results of Data processed by

SPIRITS

In the first experiment of drought monitoring using

SWI and NDVI, SPIRITS allowed us to extract

decadal data from the image series and calculate

index averages. Maps are generated easily and on a

spatiotemporal scale (Zbiri et al., 2019a [18]).

Then through a second attempt to calibrate the spatial

reflectances with the SPIRITS data we were able to

create an algorithm for the two phenological indices

FAPAR and DMP. FAPAR and DMP data, both from

SPOT VEGETATION and PROBA_V, pose

estimation problems and therefore their evaluation

and validation was essential for further studies (Zbiri

et al., 2019b [19]).

Thus, many statistical techniques exist in the

identification of outliers in FAPAR and DMP indices.

In our study, a methodology for rapid assessment of

estimates quality of these indices was used. NDVI

values are used to compare FAPAR and DMP values

recovered fewer than two assumptions. Once

estimation errors found in phenological indices are

corrected, the estimation of the productivity of our

rangelands is carried out by soil moisture index SWI,

for the period before April (spring), from a

polynomial regression-based algorithm (Zbiri et al.,

2021 [20]).

Over time, however, improvements to instruments

and data availability and advancements in algorithms

and computation methods allowed for the

development of different change-detecting techniques,

such as time series analysis and temporal trajectory-

based change detection. As a result, the detection of

climate–vegetation interactions and land cover

classification are no longer implemented as separate

and independent activities as was typical in past

studies. The problem is the efficiency of these

methods and the formulation of an exact response

with low errors to manage environmental risk.

Processing a time series of images is a fatal step.

Once data is reliable study can be completed and the

theory’s more or less accurate.

With SPIRITS it was possible to model the

relationship between European Center for Medium-

Range Weather Forecasts dataset and NDVI

eMODIS-TERRA.

Most weather forecasts today are based on the output

of complex computer programs, known as forecast

models, which typically run on supercomputers and

provide predictions on many atmospheric variables

such as temperature, pressure, wind, and rainfall. A

forecaster examines how the features predicted by the

computer will interact to produce the day's weather

(http://www.atmo.arizona.edu/students/courselinks/sp

ring17/atmo336s2/lectures/sec6/weather_forecast_at

mo170.html [21]).

In the last decade, some authors have proposed a

completely new approach to using satellite soil

moisture for estimating and improving rainfall

prediction, doing hydrology backward. The algorithm

used for estimating rainfall is called SM2RAIN

(Brocca et al., 2014 [22]).

Recently, SPIRITS is used for processing the bottom-

up precipitation dataset (SM2RAIN-ASCAT) (Zbiri et

al., 2022 [23]). For the majority of the world’s land

areas, satellite-based precipitation estimates offer the

only possible source of near-real-time precipitation

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Fatima Ezzahrae El Alaoui-Faris,

Herman Eerens, Dominique Haesen

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accumulation information for operational

hydrological applications. However, it is difficult to

provide information at more precise levels.

The anomalies were calculated using two methods of

the similarity of each type of data used: absolute

difference (AbsDif) to the historical average of

SM2RAIN and relative difference (RelDif) to the

historical average of NDVI (Figure 3).

ADha (y,p) = X(y,p) − MEAN(p) (Eq 1),

Where X is the SM2RAIN estimate for a given period

p (from November to February) and MEANp is the

mean value of the SM2RAIN during period p, derived

from the previously described 10 years of SM2RAIN

time series.

RDha (y,p) = [X(y,p) − MEAN(p)]/MEAN(p) (Eq 2),

Where X is the NDVI estimate for a given period p

(from February to April) and MEANp is the mean

value of the NDVI during period p, derived from the

previously described 10 years of NDVI time series.

SM2RAIN rainfall and NDVI anomalies data is a

highly innovative idea in hydrological modeling.

SM2RAIN rainfall data is another key parameter such

as SM for detecting water stress related to a decrease

in NDVI. The anomaly was calculated by the

formulae shown in equations 1 and 2 with SPIRITS.

An anomalies time series of precipitation (SM2RAIN)

has been calculated to understand the rangelands’

water stress condition and correlate it with a decrease

in vegetation indices.

Overall, SM2RAIN-ASCAT rainfall demonstrates

efficiency in a new investigation over many arid areas

and soil typologies. Using SM2RAIN rainfall in arid

and semi-arid areas as a new way of monitoring the

yield of vegetation is a new challenge. These products

can be eﬀectively used for rainfall estimation on a

global scale (Zbiri et al., 2022 [23]).

Fig. 3: Temporal functionalities and calculation of dif

ferent images for anomaly assessment.

4 Conclusion

Currently, there are no future plans for further updates

or maintenance of SPIRITS. The further development

of SPIRITS unfortunately has stopped. For

educational purposes, the tool can still be used.

Actually, that was the main reason for the

development of SPIRITS.

The operational processing chains use the underlying

executable (GLIMPSE). The most important reason is

that during the last few years, researchers are

migrating more and more to other technologies.

Generally, they tend to write their own scripts,

typically in Python or R environments, typically using

the gdal and/or geopandas libraries. We would

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Fatima Ezzahrae El Alaoui-Faris,

Herman Eerens, Dominique Haesen

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seriously suggest to researchers to have a look at this

software.

Sincerely, the satellite image processing software is

magic for researchers who use a large amount of data.

It was perfectly reliable for our studies described in

this article. However, it still needs to be used by the

scientific community as a multidisciplinary tool and

is very practical for beginners and experts.

Importantly, for any scientific study, SPIRITS is a

reliable program for processing different types of data.

The results obtained with statistical analysis can be

used in assessing and monitoring any ecological

problem. However, we encourage researchers to use

this excellent tool of ecological modeling.

Acknowledgments:

The authors would like to thank Eerens Herman and

Haesen Dominique who are now retired. We would

like to acknowledge VITO for these free software

(SPIRITS) and data. We are disappointed that the

development of SPIRITS has been halted. But for

educational purposes, the tool can still be used.

Commonly, all data allow them to be used for

scientific purposes and to manage ecological and

humanitarian problems

(https://mars.jrc.ec.europa.eu/asap/). We thank the

editor-in-chief and Assistant Editor of the WSEAS

journal and we thank the Reviewers that reviewed the

paper.

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Herman Eerens, Dominique Haesen

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WSEAS TRANSACTIONS on SIGNAL PROCESSING

DOI: 10.37394/232014.2022.18.24

Asmae Zbiri, Azeddine Hachmi,

Fatima Ezzahrae El Alaoui-Faris,

Herman Eerens, Dominique Haesen

E-ISSN: 2224-3488

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