Summary: The province of Mendoza can administrate water using digital tools that are used for the Science of Earth, and that way to optimize the use of resource, with an intrinsic impact on Economic Science, it is said projections on its productive array. Thus, early development of abilities on this kind of tools that takes part of the so-called Administration 4.0, allows to the professional future of Economic Science and more specifically to the Public Administrators, being more competitive, keeping up online with the new demands that visualize by the digital revolution that are undertaking.

1. Smart Management for the Public Sector
Authors
Lic. & Prof. Daniel Guillermo Cavaller Riva, Cdor Cristian Darío Ortega Yubro, Lic. Héctor
Nicolás Sosa, Becario Inv. Martín Mauricio VILLODAS, translation: María Mercedes
CAVALLER
{daniel.cavaller; cristian.ortega, hector.sosa}@fce.uncu.edu.ar
http://fce.uncu.edu.ar
Summary: The province of Mendoza can administrate water using digital tools that are used for the
Science of Earth, and that way to optimize the use of resource, with an intrinsic impact on Economic
Science, it is said projections on its productive array. Thus, early development of abilities on this kind
of tools that takes part of the so-called Administration 4.0, allows to the professional future of
Economic Science and more specifically to the Public Administrators, being more competitive,
keeping up online with the new demands that visualize by the digital revolution that are undertaking.
Keywords: Satellite maps, Landsat, historiography from rovers of Mendoza, irrigation, river flow,
comparative satellite maps versus data of water geopositioned, surface temperatures, Google Earth
Engine, GEE, image analytic, data analytic, algorithm, folium, Phython, Jupiter, Machine Learning.

2. 1. Introduction
The Province of Mendoza must administrate water knowing for them among others, the
spatial distribution and temporal from surface water (Pekel et al., 2014), using digital tools
that are used for Science of Earth, and that way optimize the use of resource, with an
intrinsic impact on Economic Science, it is said projections of its productive array. The water
resource can be analyzed with satellite images applying automatic learning algorithm,
managing precise information contributes to the decision making that must be carried out to
the public sector. The conjoin of data are obtained from Google Earth Engine. Those data
allow to build models that proportionate evidence from state and the change of the ecotones
that indicates the areas of transition among biomes.
The target for the current investigation is to validate multitemporal and multispectral satellite
images, detecting automatically water surfaces, , with the application of specific algorithms
for this kind of data analysis, through the available tools, such as API from integrated GEE
to Jupyter Notebook launched on the Colab application from Google , with Python to classify
multitemporal and satellite images with multiple sensors, like the obtained Landsat 8
(Shelestov, Lavreniuk, Kussul, Novikov, & Skakun, 2017). Another selected tool is code
editor from GEE, that use javascript. GEE contains different methods of rendering images,
and measuredalgorithm from which it can infer on the superficial reflectance, algorithm that
needs an atmospheric compensation such as temperature and water vapor, among others.
2. Study Area
The water resources on the Province of Mendoza are monitored with control sensors of
heterogeneous flow, what can be consulted on the Irrigation Department website, control
platform denominated Model Distribution Indicator Operator («MDIO», s. f.). The MDIO
system monitors the six flows.
3. Methodology
It is used the API Python of GEE («Python installation | Google Earth Engine | Google
Developers», s. f.) on Google Colab («Google Colaboratory», s. f.) which is a version with
Jupyter Notebooks, and the code editor from GEE, that use javascript language from which
obtains images from time series , and it is applied different algorithms that allows to obtain
detailed and specific information. Those images are denominated data set.
3.1. Data Set
The data set from consulted satellite is applied for the coordinates from the Province of
Mendoza, latitude -32.9946 and longitude -69.1280, resultingfrom Potrerillos Dam. The data
set are obtained from captured images by the Landsat 8 satellite.

3. Image Nº 1 - Potrerillos Dam Coordinates
3.1.1. USGS Landsat 8 Collection 1 Tier 1 TOA
Reflectance
Those data set («USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance», s. f.) shows the
superficial reflectance atmospherically corrected from the satellite sensors, that takes part
from NASA program. Those data are atmospherically corrected including clouds mask,
shadows, water and produced snow, as well as saturation mask by pixel. The image
resolution is from 30 and 15 meters respectively, and the images are approximately captured
once every two weeks around the world, containing multispectral and thermal data from
each corner of the planet. To obtain data from GEE code editor:
var landsatCollection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA');
3.1.2. Normalized Difference WaterIndex (NDWI)
The NDWI(McFeeters, 2013) is a remotely sense index associated with liquid water in virtue
to the satellite bands that takes as an observation to monitor the related changes with water
content on the water bodies, in this case, using the green wavelength band and nearby
infrared wavelength band. The bands to elaborate the model with the GEE editor:
 B3: green superficial reflectance
 B7: infrared superficial reflectance from short wave 2.
𝑁𝐷𝑊𝐼 =
𝐵3 − 𝐵7
(𝐵3 + 𝐵7)

4. It is affirmed that the NDWI major than zero assumes that represents water surfaces, while
the minor or equal to zero assumes that are surfaces that are not from water. To express
the function from GEE code editor:
image.normalizedDifference(['B3', 'B7'])
4. Obtained results
With the analysis of time series from satellite images for a specific vector, it can obtain the
variability on the time of water surfaces, and perform with that data set, a predictive analysis
what can lead on preventive actions on the use of water resource creating a Machine
Learning.
5. Conclusions and Projections
5.1. Conclusions
The available data catalogs from GEE («Earth Engine Data Catalog | Google Developers»,
s. f.) contains a continuous satellite image monitoring. It can develop models that contributes
to the political development of state for an intelligent and automatized administration to the
productive array. Therefore, the early development of abilities on this kind of tools that takes
part of the denominated Administration 4.0, allows to the professional future of Economic
Science being more competitive and being online with the new demands that are visualize
for the digital revolution that are undertaking.
5.2. Projections
The utilization of the GEE catalog allows the generation of several combined lines of relative
studies to:
 Time and Climate:
o Temperature of the marine and terrestrial surface
o Climatic models to generate predictions in long term historic interpolations of
surface variable.
o Atmospheric data
o Meteorological data that describe the predicted conditions and measures on
short periods time, including precipitations, temperature, wet and wind, and
other variables.}
 Analysis of Satellite Images:
o Collections Landsat images, that is a joint program of USGS and NASA, from
which has been continuously observing the Earth since 1972 up to this day.
o Collection of Sentinel images through Copernic Program which is an initiative
headed by the European Commission in joint with the European Space
Agency.
o Collection of MODIS images, through the Terra and Aqua satellite from NASA
have been acquiring images from Earth daily since 1999, including daily
images, reflectance of surface and derived products as vegetation indexes
and snow cover.
 Geophysical:

5. o Digital models of elevation that describes the form of terrain from Earth and
derivated products as the data base of hydrology
o Land cover maps that describes the physical landscape in terms of form of
soil cover, like woods, meadows and water.
o Data about farmland, key to understand the worldwide consumeof water and
the agricultural production that includes the extension from farming land, the
crop possession and the irrigation source.
Bibliographic References
Google Colaboratory.(s. f.). Recuperado 18 de enero de 2020, de
https://colab.research.google.com/notebooks/welcome.ipynb
McFeeters, S. K. (2013).Using the Normalized Difference Water Index (NDWI) within a Geographic
Information System to Detect Swimming Pools for Mosquito Abatement:A Practical Approach.
Remote Sensing,5(7),3544-3561.doi:10.3390/rs5073544
MIDO. (s. f.). Recuperado 31 de diciembre de 2019,de http://www.irrigacion.gov.ar/telemetria
MODIS Collections in Earth Engine | Earth Engine Data Catalog. (s. f.). Recuperado 25 de enero de 2020,de
https://developers.google.com/earth-engine/datasets/catalog/modis?hl=es
Nieve en Mendoza. (s. f.). Recuperado 25 de enero de 2020,de http://estaciones.ianigla.mendoza-
conicet.gob.ar/nieve/
Pekel, J.-F., Vancutsem,C., Bastin,L., Clerici,M., Vanbogaert,E., Bartholomé,E., & Defourny, P. (2014).A
near real-time water surface detection method based on HSV transformation ofMODIS multi-spectral
time series data. Remote Sensing ofEnvironment,140,704-716.doi:10.1016/j.rse.2013.10.008
Python installation | Google Earth Engine | Google Developers. (s.f.). Recuperado 18 de enero de 2020,de
https://developers.google.com/earth-engine/python_install?hl=es
Shelestov,A., Lavreniuk, M., Kussul,N., Novikov, A., & Skakun, S. (2017).Exploring Google Earth Engine
Platform for Big Data Processing:Classification ofMulti-Temporal Satellite Imageryfor Crop Mapping.
Frontiers in Earth Science, 5. doi: 10.3389/feart.2017.00017
TerraClimate:Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces,University of Idaho.
(s. f.). Recuperado 4 de enero de 2020, de https://developers.google.com/earth-
engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE?hl=es
USGS Landsat8 Collection 1 Tier 1 TOA Reflectance. (s.f.). Recuperado 8 de febrero de 2020, de
https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_TOA?hl=es

6. Links
● Google Earth Engine, conjunto de datos de aguas superficiales globales
https://developers.google.com/earth-engine/tutorial_global_surface_water_01
https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD44W
https://global-surface-water.appspot.com/map
● Agua superficial global, conjunto de datos 1984 - 2018.
https://global-surface-water.appspot.com/download
http://www.arcgis.com/home/item.html?id=5d65be95ccc341d587896a81794021bf
● Visor de agua superficial, Naciones Unidas.
https://www.sdg661.app/
https://www.sdg661.app/data-products/surface-water-viewer
● Librería folium para visualizar mapas
https://python-visualization.github.io/folium/
● Indicador NDWI,índice diferencial de agua normalizado,hidratación de la vegetación yla humedad del
suelo
https://greenurbandata.com/2019/04/30/ndwi/
● Biblioteca de código abierto de mapas interactivos
https://leafletjs.com/
● Ciencias de la Tierra. Google Earth Engine