© The Authors, 2025, Published by the Universidad del Zulia*Corresponding author: henry.pacheco@utm.edu.ec
Keywords:
Spectral indices
Kc
Change detection
U AV
GEE
Remote sensing applied for the estimation of crop coecient and detection of forest cover
changes
Teledetección aplicada para la estimación del coeciente del cultivo y detección de cambios en la
cobertura boscosa
Sensoriamento remoto aplicado para estimar o coeciente de cultivo e detectar mudanças na
cobertura orestal
Henry Antonio Pacheco Gil¹*
Cristhian Martin Delgado Marcillo
1
Roger Adrián Delgado Alcívar
1
Luis Fernando Fernández Zambrano
1
Néstor Erick Caal Suc
2
Emilio José Jarre Castro
3
v. Fac. Agron. (LUZ). 2025, 42(1): e254211
ISSN 2477-9407
DOI: https://doi.org/10.47280/RevFacAgron(LUZ).v42.n1.XI
Crop production
Associate editor: Dra. Evelyn Pérez Pérez
University of Zulia, Faculty of Agronomy
Bolivarian Republic of Venezuela
¹Universidad Técnica de Manabí. Facultad de Ingeniería
Agrícola. Grupo de Investigación: Geomática y Agricultura
4.0. Portoviejo, Provincia de Manabí, Ecuador.
2
Magíster en Estrategias para el Desarrollo Rural y
Territorial. Universidad de San Carlos de Guatemala. Grupo
de Investigación: Geomática y Agricultura 4.0. Cobán, Alta
Verapaz, Guatemala.
3
Magíster en Ingeniería Ambiental. Universidad Técnica
de Manabí. Facultad de Ingeniería Agrícola. Grupo de
Investigación: Funcionamiento de Agroecosistemas e
Implicaciones Agrícolas Frente al Cambio Climático.
Portoviejo, Provincia de Manabí, Ecuador.
Received: 23-09-2024
Accepted: 23-12-2024
Published: 05-02-2025
Abstract
With the objective of applying remote sensing techniques for
crop coecient estimation and detection of changes in forest cover,
in order to generate information that contributes to the sustainable
management of agricultural and forestry resources, a study was
conducted based on the theoretical foundations of agriculture 4.0,
through the implementation of advanced technologies and intelligent
data integration to optimize the entire agricultural production cycle.
The methodology adopted includes the capture and processing
of multispectral images from satellite platforms and unmanned
aerial vehicles (UAVs), in order to obtain geometric and spectral
information on various crops. Calculations of spectral indices
(NDVI, NDMI, NDWI, Kc) and analysis of forest stand losses
were performed using advanced software tools in GIS environment
and the Google Earth Engine platform. The drone images made
it possible to calculate the NDWI to classify soil moisture in
high, moderate and low levels. Satellite images facilitated the
identication of relationships between crop evaporation coecient
(Kc) and climatic parameters, as well as the detection of areas
with forest losses in the Carrizal river basin. The results suggest
strategies for the development of precision agriculture activities,
promoting the substitution of conventional practices for sustainable
development mechanisms based on geospatial technologies. This
study contributes to the literature by demonstrating the application
of advanced geospatial technologies to optimize agricultural
production and sustainability.
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2025, 42(1): e254211 January-March. ISSN 2477-9409.
2-6 |
Resumen
Con el objetivo de aplicar técnicas de teledetección para la
estimación del coeciente del cultivo y la detección de cambios
en la cobertura boscosa, de tal manera de generar información que
contribuya al manejo sostenible de los recursos agrícolas y forestales
se realizó un estudio con base en los fundamentos teóricos de la
agricultura 4.0, mediante la implementación de tecnologías avanzadas
y la integración inteligente de datos para optimizar el ciclo completo
de producción agrícola. La metodología adoptada incluye la captura
y procesamiento de imágenes multiespectrales provenientes de
plataformas satelitales y de vehículos aéreos no tripulados (UAV),
con el n de obtener información geométrica y espectral de diversos
cultivos. Se realizaron cálculos de índices espectrales (NDVI, NDMI,
NDWI, Kc) y análisis de pérdidas de masas forestales utilizando
herramientas avanzadas de software en ambiente GIS y la plataforma
Google Earth Engine. Las imágenes de drones permitieron calcular
el NDWI para clasicar la humedad del suelo en niveles alto,
moderado y bajo. Por su parte las imágenes satelitales facilitaron
la identicación de relaciones entre el coeciente de evaporación
del cultivo (Kc) y los parámetros climáticos, así como la detección
de áreas con pérdidas de bosque en la cuenca del río Carrizal. Los
resultados sugieren estrategias para el desarrollo de actividades en
agricultura de precisión, promoviendo la sustitución de prácticas
convencionales por mecanismos de desarrollo sostenible basados
en tecnologías geoespaciales. Este estudio aporta a la literatura al
demostrar la aplicación de tecnologías geoespaciales avanzadas para
optimizar la producción agrícola y la sostenibilidad.
Palabras clave: índices espectrales, Kc, detección de cambios, UAV,
GEE.
Resumo
Com o objetivo de aplicar técnicas de sensoriamento remoto para
a estimativa do coeciente de culturas e a detecção de mudanças
na cobertura orestal, a m de gerar informações que contribuam
para o gerenciamento sustentável dos recursos agrícolas e orestais,
foi realizado um estudo com base nos fundamentos teóricos da
agricultura 4.0, por meio da implementação de tecnologias avançadas
e da integração inteligente de dados para otimizar todo o ciclo de
produção agrícola. A metodologia adotada inclui a captura e o
processamento de imagens multiespectrais de plataformas de
satélite e veículos aéreos não tripulados (VANTs), a m de obter
informações geométricas e espectrais de várias culturas. Os cálculos
dos índices espectrais (NDVI, NDMI, NDWI, Kc) e a análise das
perdas de povoamentos orestais foram realizados por meio de
ferramentas de software avançadas em um ambiente de SIG e na
plataforma Google Earth Engine. As imagens de drones permitiram
que o NDWI fosse calculado para classicar a umidade do solo em
níveis altos, moderados e baixos. As imagens de satélite facilitaram a
identicação das relações entre o coeciente de evaporação da cultura
(Kc) e os parâmetros climáticos, bem como a detecção de áreas com
perda de oresta na bacia do rio Carrizal. Os resultados sugerem
estratégias para o desenvolvimento de atividades de agricultura de
precisão, promovendo a substituição de práticas convencionais
por mecanismos de desenvolvimento sustentável baseados em
tecnologias geoespaciais. Este estudo contribui para a literatura ao
demonstrar a aplicação de tecnologias geoespaciais avançadas para
otimizar a produção agrícola e a sustentabilidade.
Palavras-chave: índices espectrais, Kc, detecção de mudanças, UAV,
GEE.
Introduction
Recent advances in geospatial technologies provide new options
in agricultural sciences (Fuentes-Peñailillo et al., 2024; Masi et
al., 2023). According to Kganyago et al. (2024) and Karunathilake
(2023), remote sensing has the potential to evolve adaptive
agricultural practices by providing continuous information on crop
status at various scales. This is especially crucial in a context of
historically generated weather patterns on land. However, in Ecuador,
where a large part of the population depends on agricultural activities,
research in these areas is scarce.
One of the challenges facing remote sensing is the analysis of crop
moisture. Basharat et al. (2023), Chen and Liu (2020), and Mehedi
et al. (2024) explored low-cost geospatial techniques to increase
agricultural yields and reduce environmental impact.
Quantication of ‘plant water stress’ is proposed as an indicator to
improve irrigation practices by considering the interaction between
soil water availability, atmospheric demand and plant physiology.
Munaganuri and Yamarthi (2024) proposed an innovative
approach based on remote sensing and articial intelligence to
optimise irrigation, using convolutional neural networks to classify
remotely sensed images and capture crop water requirements.
Unmanned aerial vehicles (UAVs) have also been used extensively
in precision agriculture. Manfreda and Dor (2023) provided a review
on the history, commercial, social aspects and current applications of
UAVs in agriculture. Work has also been developed for the estimation
of crop Kc from the Leaf Area Index (LAI) to improve water balance
calculations (Eliades et al., 2022).
Soil moisture has been studied using multispectral UAV and
satellite imagery, together with articial intelligence algorithms. Bai
et al. (2021), Datta and Faroughi (2023) and Ge et al. (2021) presented
research that has advanced soil moisture prediction at various depths.
Wu et al. (2024) were able to predict moisture at 5, 10, 20 and 40
cm depth in citrus orchards using multi-modal remotely sensed UAV
data. Khose and Mailapalli (2024) conrmed that the ratio vegetation
index (RVI) has the greatest potential for estimating surface soil
moisture using UAV imagery and machine learning algorithms.
In the context of remote sensing, several satellites orbit the Earth
providing multispectral images. In this research, Sentinel-2 imagery,
available through the European Space Agency’s Copernicus platform,
was used. De la Guardia et al. (2024) used Sentinel-2 and ERA-5
Land data to calculate the evapotranspiration of a bean crop in Brazil.
Sabie et al. (2024) used Landsat Sentinel-2 (HLS) data to calculate
crop coecients and estimate evapotranspiration at the eld level,
with high agreement between calculations and eld data.
In addition, cloud computing elements were considered to
analyse multi-temporal datasets such as MODIS Land Cover Type
(MCD12Q1) and Hansen Global Forest Change (2000-2021) products
in monitoring the dynamics of dierent forest types and canopy cover,
facilitating image identication and calculation of multi-temporal
spectral indices. Del Valle and Jiang (2022), Kumari et al. (2024),
Lemesios and Petropoulos (2024) and Yi et al. (2024) highlighted
the importance of the Google Earth Engine (GEE) platform for the
identication of multi-temporal images of vegetation types and forest
cover, using historical remotely sensed data.
Considering the potential of these geospatial technologies in
agriculture, the objective of this study was to apply remote sensing
techniques for crop coecient estimation and forest cover change
detection in order to generate information that contributes to the
sustainable management of agricultural and forestry resources.
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Pacheco et al. Rev. Fac. Agron. (LUZ). 2025, 42(1): e254211
3-6 |
Materials and methods
Location of the study area
The research was carried out in agricultural sectors of the Province
of Manabí, Ecuador (Figure 1).
Traditional crops of the region and forested areas located within
the Portoviejo and Carrizal Chone river basins were selected.
cloud cover (<30 %), focusing on a lemon crop in Santa Ana, Manabí.
The images were processed in ArcGIS, calculating spectral indices
(Table 1) with map algebra tools, providing relevant information on
vegetation and crop conditions.
Table 1. Spectral indices calculated with the multispectral images.
Index name Formula Reference
Normalised Dierence
Vegetation Index
NDVI Toosi, et al. (2022)
Normalised Dierence
Humidity Index
NDMI Mc Feeters
(1996)
Normalised Water Dierential
Index
NDWI Mc Feeters (1996)
Evapotranspiration Kc Terink et al. (2015)
Soil and crop water conditions
ArcGIS software tools were used to analyse the green, red, red-
edge and near-infrared bands captured by the eBee SQ agricultural
drone (SenseFly, Switzerland). Moisture indices (NDMI and MDWI)
and crop evaporation coecient (Kc) were calculated using the
equations shown in Table 1. The indices and Kc were calculated
for each 10 m resolution cell, using the multispectral bands of the
Sentinel image, and then averaged for each plot, according to crop.
Subsequently, descriptive statistical analyses of the indices were
carried out to assess the water conditions of the soils and crops.
Climatological conditions
Data from the La Teodomira Meteorological Station, belonging to
the hydrometeorological network of the Portoviejo river basin of the
National Institute of Meteorology and Hydrology of Ecuador, were
analysed.
Loss of forest masses
Using the Google Earth Engine platform, a JavaScript code was
programmed to analyse forest stand loss. The Hansen Global Forest
Change 2000-2023 collection was accessed, which provided layers
on forest cover (trees over 5 metres), vegetation loss (Loss) and
gain (Gain). The aected area was quantied and represented on a
thematic map, assessing its inuence on erosion processes.
Results and discussion
Close remote sensing
As a result of this research, high-precision photogrammetric
products with a resolution of 2 cm.pixel
-1
were generated. The data
obtained allowed the production of orthophotos, spectral bands, point
clouds and digital terrain and surface models.
Soil and crop water conditions
The orthophoto (Figure 2a) shows a soil under dierent moisture
conditions, partially covered by pine nut and cocoa crops. Normalised
Dierence Wetness Index (NDWI) values (Figure 2b) ranged from
-0.530059 to -0.866279, where minimum values represent vegetation,
intermediate values indicate soils with high or moderate moisture,
and maximum values reect dry soil (Table 2).
The Natural Breaks method, built into Arcgis tools, is based on
natural groupings inherent in the data. Classes are created so that
similar values are grouped together and dierences between classes
are maximised.
b
Figure 1. Location of the study area in the Province of Manabí,
Ecuador.
Photogrammetric ight for the capture of aerial images
On September 17, 2023, in the middle of the dry season for the
study area, in the Ecuadorian coastal region, a photogrammetric ight
was carried out over an area of approximately 10 ha. The Ebee SQ
Agricultural Drone (SenseFly, Switzerland) was used, instrumented
with the Parrot Sequoia multispectral camera, which captures RGB
images and the green, red, red edge and near infrared bands. The ight
was planned with Emotion AG software, where the area of interest,
ight path, height at 95 metres above the ground and an overlap of 80
% between the photographs were dened. At the end of the ight, the
data were downloaded and the images were processed in the PIX4D
photogrammetry software to generate orthomosaics, point clouds,
spectral bands, digital terrain and surface models.
Six ground control points (GCP) were dened to support the
photogrammetric ight. The points were distributed strategically,
uniformly covering the ight area to guarantee adequate precision
in the adjustment of the images obtained. To obtain the precise
coordinates of the control points, RTK (Real-Time Kinematic)
equipment (Topcon model GR-5, Japan) was used, which allowed
centimetric precision to be achieved in the location of the points. The
coordinates were registered in the geodetic system with the support
of the permanent station REGME POEC 42008M003, located in
Portoviejo, guaranteeing consistency with the ocial cartography of
Ecuador and remote sensing data.
The GCPs were manually entered into the project, specifying their
precise coordinates obtained with the high precision GPS. Within
Pix4D, the GCPs were manually marked on various images where
they were visible to correctly align and georeference the model,
adjusting the positions of the images according to the control points.
Downloading Sentinel 2 imagery
Sentinel-2 imagery was downloaded from the Copernicus Open
Access Hub, selecting parameters such as date, geographic extent and
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2025, 42(1): e254211 January-March. ISSN 2477-9409.
4-6 |
Table 2. Reclassication of the normalised dierence humidity
index.
Color
NDWI
Range
Clasication
≤ -0.593540 Vegetation
-0.593540
-0.517160
Soil with high humidity
-0.517160
-0.435378
Soil with moderate moisture
≥ -0.435378 Dry soil
Features are divided into classes whose boundaries are set where
there are relatively large dierences in data values. In this case it was
veried by eldwork and visual interpretation of the orthophoto, that
the classes dened in Table 2 correspond to the coverages visualised
in the verication.
The method of classication of natural changes (Natural
Breaks) made it possible to dierentiate the coverages according to
the NDWI ranges. These results are in agreement with the studies
of Chandramohan et al. (2024) and Judge et al. (2021), who used
dierent data sources to measure soil moisture.
Remote sensing
Sentinel 2 images (Figure 2a) were found with cloud cover below
30 % for 11 months of the year, allowing NDVI to be calculated
(Figure 3b).
Figure 2. a) RGB photo ortho generated by Pix4D software and b)
Normalised Dierence Wetness Index (NDWI).
Crop evapotranspiration coecient of lemon crops
The crop coecient (Kc) reects the water requirements at each
stage of lemon development, and is key for irrigation planning and
design in the studied area. For a comprehensive understanding of the
data, in terms of the inter-annual variability of Kc, it is essential to
consider its relationship with the phenological stages of the crop and
how these are aected by the annual weather conditions.
The data in Table 3 show the interannual variations of Kc for the
lemon crop, highlighting the month of March with 0.25 indicating
the lowest water requirements, for the nal months of harvest or the
beginning of a new phase of vegetative development, when the crop
reduces its transpiratory activity, as the fruits have been removed and
there is a period of recovery.
On the other hand, the maximum Kc value (0.62) was recorded
during August, indicating high water requirements in conditions of
low rainfall and high evapotranspiration, in addition to the fact that
the crop is in a critical phase of fruit development, where a peak in Kc
is observed due to the high water demand to sustain the rapid growth
of the fruit.
The decrease in Kc values during September to November is
explained by the fact that the crop is generally at an advanced stage
of fruit development or ripening. As the fruits reach the right size and
enter the ripening stage (September to November), vegetative growth
decreases, reducing transpiration and hence Kc.
The variation of the crop coecient (Kc) throughout the year is
crucial for optimising agricultural irrigation scheduling. This parameter,
which reects the ratio between reference evapotranspiration (ETo)
and crop evapotranspiration, varies according to rainfall, temperature
and plant development stage, allowing irrigation to be adjusted more
eciently (Das et al., 2023).
According to Lobos et al. (2017) the water demand of a crop is
mainly determined by two factors, the climatic conditions of the sector
and the level of development of the plants. The development stage of
the crop is dened by the crop coecient (Kc), which indicates the
water consumption of a plant according to its phenological stage.
Toosi et al. (2022) report a good number of investigations that relate
the Kc with the phenological development of the crop, highlighting
that the crop coecient (Kc) varies signicantly throughout its
phenological cycle, as it reects the water needs of the crop according
to its development. For lemon during the early stages, such as initial
growth and vegetative development, the Kc is low due to the limited
water demand of the young plants. As the crop progresses to active
growth and ripening stages, Kc increases, reecting a higher water
demand caused by increased transpiration and photosynthesis.
In this context, the use of the normalised dierence vegetation
index (NDVI) has been consolidated as an eective tool to estimate
the Kc of lemon at dierent phenological stages. This is because NDVI
Table 3. Climatic data from the INAMHI Teodomira Station and crop coecient (Kc), calculated with the multispectral bands of the
Sentinel image.
Month Jan Feb Mar Apr May Jun Aug Sep Oct Nov Dec
Prec (mm)
83.9 92.6 217.4 47.3 32 1.7 0.3 0.6 0.2 0 33.3
Temp (°C)
27.35 26.65 27.75 27.75 26.75 26.65 26.75 26.35 26.5 25.85 26.3
Hum (%)
80 83 84 84 85 84 83 80 79 78 78
Eva (mm)
99 85 130.2 120 89.6 91.7 115.5 137.6 144.3 134.5 122.4
NDVI
0.91 0.57 0.36 0.49 0.59 0.48 0.42 0.47 0.47 0.32 0.31
Lemon Kc
0.40 0.26 0.25 0.41 0.32 0.39 0.62 0.37 0.35 0.38 0.41
Figure 3. a) Sentinel 2 image and b) NDVI for the study area in
Manabí Province, Ecuador.
a
b
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Pacheco et al. Rev. Fac. Agron. (LUZ). 2025, 42(1): e254211
5-6 |
measures the amount and vigour of vegetation, correlating directly
with the level of evapotranspiration and hence with the Kc of the
crop. Recent research has shown that there is a signicant correlation
between NDVI values and Kc in lemons, especially during the active
growth and ripening stages, allowing for more accurate and ecient
water management (Ippolito et al., 2023).
Forest cover loss
Figure 4 shows, in red polygons, the areas where losses of
vegetation cover were recorded, according to the calculations made
in the Hansen Global Forest Change collection for the period 2000-
2023. The sum of the area of the polygons with forest stand loss
resulted in 58 km², which represents 21 % of the native forest stand
in the study area, located in the upper and middle parts of the Carrizal
river basin.
Figure 4. Spatial distribution of forest cover loss in the Carrizal
river basin.
The loss of vegetation cover found in this study is directly related
to deforestation, associated with the expansion of the agricultural
frontier, which has profoundly aected hydrological processes,
reducing the capacity of the basins to regulate the water cycle.
The loss of these 58 km² of forest could be inuencing the
reduction of water retention capacity in the forest, increasing surface
runo and the risk of erosion and ash oods (Elogne et al., 2023).
Additionally, considering the reports of Pedroza-Parga et
al. (2022), a signicant increase in soil erosion due to the loss of
vegetation mass can be expected. According to the authors, areas with
vegetation loss show a sediment erosion of 58.6 t.ha
-1
, while areas
with intact vegetation show lower values of approximately 26.3 t.ha
-1
.
These results underline that vegetation on the soil surface helps
to reduce ow velocity and particle removal, conrming that proper
vegetation management positively inuences hydrological processes,
particularly inltration, runo and topsoil protection.
In the Carrizal river basin, the loss of forest cover is driving
erosion in the upper and middle reaches of the drainage sub-basins.
The resulting erosive processes generate large amounts of sediment
that accumulate in the drainage network and reach the mouth of the
Chone River estuary in Bahía de Caráquez. This accumulation of
sediments in the watercourses has diverse hydrological consequences,
aecting the dynamics of the ecosystems and the safety of the
hydraulic infrastructure.
Strategies for precision agriculture
Remote sensing-based precision agriculture oers a key
opportunity to improve the agricultural sector in the province of
Manabí, Ecuador. This technology allows continuous monitoring of
crops, using satellite imagery to detect problems such as water stress
and deforestation. By obtaining accurate data on crop health, cover
and water conditions, farmers can make informed decisions to apply
inputs more eciently, optimising the use of resources such as water
and fertilisers.
In addition, remote sensing facilitates agricultural planning by
being a tool for analysing soil conditions, which can help identify
critical areas, providing real-time information that enables preventive
measures to be implemented. In this way, the technology improves
decision-making and the competitiveness of the sector, favouring
more sustainable practices and increasing farmers’ resilience.
Conclusions
Close remote sensing with unmanned aerial vehicles (UAVs) was
very useful in generating very high spatial resolution orthophotos of
the order of 2 cm.pixel
-1
, which can be a very useful input in decision
making related to the optimal use of water resources.
Normalised Dierence Wetness Index (NDWI) values, calculated
with spectral bands from a photogrammetric ight, ranged from -0.53
to -0.86, where minimum values represent vegetation, intermediate
values represent high and moderate moisture soils, and maximum
values represent dry soil.
The minimum (0.23) and maximum (0.62) Kc values for the lemon
crop indicate the water consumption needs of the plant according to its
phenological stage, with the minimum value coinciding with the nal
stage of harvest and the maximum with the time of fruit development.
Remote sensing was optimal for the analysis of environmental
conditions through the use of multispectral images from the Sentinel
2 satellite.
The Carrizal river basin has experienced a substantial loss of
forest mass of 58 km
2
, representing a 21 % decrease in native forest
over a 20-year period.
The monitoring of agricultural activities and environmental
conditions, using remote sensing techniques, integrates data sets that
allow the various stakeholders to make informed, optimal and timely
decisions in time and space to promote the sustainable development
of the region.
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