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Jindo et al. Chem. Biol. Technol. Agric.
https://doi.org/10.1186/s40538-021-00253-4

Open Access

REVIEW

Potential utilization of satellite remotesensing for field‑based agricultural studies
Keiji Jindo1* , Osamu Kozan2 , Kohtaro Iseki3, Bernardo Maestrini1, Frits K. van Evert1, Yilma Wubengeda4,
Egidio Arai5, Yosio Edemir Shimabukuro5, Yoshito Sawada6 and Corné Kempenaar1

Abstract
Using satellite remote-sensing is a useful approach for agriculture to monitor plant and soil conditions and provide
decision-making support to farmers. Recently, several types of tools and indices by the satellite remote-sensing havebeen developed for monitoring drought stress, changes in land uses, and crop–soil water relations. Although thesetechniques are powerful tools, especially in developing countries and regions where precise data of crop evaluationand yield statistics are not accessible, it is quite difficult for beginners to select the most suitable tool or index for theirobjectives. Major difficulties are in the specificity of the terminology, differences among the sensors (e.g., active vs.
passive sensors), interpretation of imaginary data, and multidisciplinary topics. This work offers non-expert readers
basic knowledge of remote-sensing use in agriculture, presenting advancements in the field and future insights. We
review different sensors that are frequently used, such as optical sensors and synthetic aperture radar, as well as thefollowing different purposes for using remote-sensing in agriculture: drought stress detection, monitoring land use
in tropical regions (e.g., deforestation), and estimating water balance evapotranspiration in emerging and developing countries. Additionally, the integrated approach of remote-sensing data into the crop simulation model, which
can provide a more holistic view of the plant and field status over cropping season, is described for the field-based
studies.
Keywords: Satellite remote-sensing, Drought stress, Land-use map, Evapotranspiration, Deforestation, PALSAR
Introduction
Technologies of remote-sensing (RS) can be aircraftbased or satellite-based, enabling us to classify anddetect objects to identify features and characteristics ofthe Earth system made from propagated signals suchas electromagnetic radiation. There exist a wide rangeof different series of RS types from proximal RS to satellite RS. More than a thousand active satellites RS are
orbiting earth these days, and many of them are forearth observation by satellite RS, which is equippedwith one or more sensors and other materials. Information resources derived from satellite imageries are used
*Correspondence: keiji.jindo@wur.nl
1
Agrosystems Research, Wageningen University & Research, P.O. Box 16,
6700 AA Wageningen, The Netherlands
Full list of author information is available at the end of the article

extensively in the agriculture sector and support farmers’
decision-making [1]. For example, a long-run remotelysensed product of the LANDSAT and MODIS are wellknown tools to monitor biomass in an agricultural field.
At the same time, the tropical Rainfall Measuring Mission (TRMM), operated by the National Aeronautics andSpace Administration, the USA (NASA), and The Japan
Aerospace Exploration Agency (JAXA), provides precipitation data, which is often considered essential for fieldmanagement [2]. Not only the rainfall, but also othervariety of valuable information is available by RS technologies, including evapotranspiration, drought, soil condition and landscape [3–6].
The idea of applying satellite data in the agriculture
sector has been sprout around in the 1960s and 1970sin the USA. The use of Landsat data in a project called
Large Area Crop Inventory Experiment (LACIE) was© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
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Fig. 1 Schematic representation of the relationship between remote sensing measurements (black boxes) and surface or canopy parameters (redboxes) for different applications (blue boxes). Original source from: [24]initiated in November 1974, which was 2 years after
the first launch of NASA Earth Resources Technology
Satellite (ERTS), later renamed as Landsat-1. The mostpopular social news was the estimation of winter wheat
production in the Soviet Union in the 1970s by using
LACIE [7]. In those days, simple indicators were developed to monitor the plant growths at a large scale, suchas the Green Index Number (GIN), calculated by thepercentage of pixels with greenness in a specific field[8, 9]. For the late 1970s and 1980s, more indicators
have been developed and used along with other information such as meteorological data (e.g., precipitationand temperature). In the last 1980s, it was the first time
to use the Normalized Difference Vegetation Index(NDVI), calculated by the wavelengths of near-infrared[10] and green, to monitor drought [11]. Then, using
high-resolution satellite data, chlorophyll measurement was quantified [12], while evapotranspiration rate
was quantified to monitor the energy/water balance forthe irrigation system [13]. Given that crop properties,
including above-biomass and leaf area index (LAI), canbe estimated or monitored from space through opticalvegetation indices (VI) as well as Synthetic Aperture
Radar (SAR). As shown in Fig. 1, there exist different
applications of satellite RS measurements in the agricultural domain. Nowadays, there exist more than 510

indicators of VIs, according to the https://​w ww.​index​
datab​ase.​de.
As aforementioned, multiple satellite RS applications
have been used in agriculture, such as creating a land
cover and land use, monitoring plant growth and drought
stress, and estimating water use efficiency. From a historical perspective, the use of RS data in agriculture has
evolved over the past decades in its temporal and spatial
resolution. This has expanded the usage of satellite RS
data from the analysis carried out at the landscape levelto the field level. A second major change occurred in
the resolution of the optical sensors, with the possibilityof measuring an increasing number of bands, including
narrow bands. This has brought to the development of
numerous vegetation indices and major improvementsin the automatic detection of non-valid pixels such as
clouds and shadows. A third big change was the usage ofactive sensors such as SAR, which provide measurements
cloud-independent, resulting in an improved regularity of image availability, or satellite sun-induced fluorescence, which could provide valuable information on theefficiency of the photosynthetic process.
Although the great progress of RS technologies, mostof its application to the agricultural domain has been
restricted to special scientific studies, and few applications are made for farmers’ practices. This is partlyJindo et al. Chem. Biol. Technol. Agric.

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attributed that most cases of RS application to agriculture have been done only by the RS experts who are
non-expert of field agronomy. To make the applicationof satellite RS more implementable on agricultural fields,
rudimentary knowledge on RS utilization should be
widely shared across the different disciplines concerning
agriculture.
This paper illustrates the overview of the utilization ofsatellite RS in the agricultural domain. To achieve effective RS uses for agriculture, especially in the regions
where the historical data or statistics for crop growth andyield are not fully available, reviews are done on several
indices and tools for land use and land cover (LULC),
drought detection, and water productivity. As an example
of RS application to field-based studies, the application of
the satellite RS into the crop growth model (CGM) is also
described.
Using satellite RS data for different purposesLand use and land cover

It should be highlighted that satellite data can help create a land-use land cover (LULC) map. Several factors
cause LULC changes: increasing pressure of food production due to the resource scarcity concerning population growth; changing market opportunities; abroad
policy intervention; loss of adaptive capability andincreased vulnerability; and changes in social organization and resource access attitudes. There is a need for
accurate LULC maps to measure the impacts of LULC
changes related to pasture expansion, agricultural intensification, urbanization, deforestation, and biodiversity loss. Satellite RS data and techniques are helpful for
mapping LULC classes due to the synoptic view of large
geographic regions and the relatively low costs. In the
1990s, the Advanced Very High-Resolution Radiometer(AVHRR) onboard the U.S. National Oceanic and Atmospheric Administration (NOAA) polar-orbiting meteorological satellite data were used to produce global land
cover maps at spatial resolutions of 8 km [14] and 1.1 km[15]. In the 2000s, a global LULC map from 2000 was
made available by the Global Land-Cover (GLC 2000)
project [12], which used a dataset of 14 months of daily
global images obtained by the SPOT-4 VEGETATION
sensor.
Another example is the Moderate Resolution Imaging
Spectroradiometer (MODIS) Collection 5 annual global
land cover products generated at 500-m spatial resolution (2001–2018) using supervised classifications of
Terra and Aqua MODIS reflectance data [16]. Nowadays,
the increasing availability of time series of satellite sensor images has become available. Their contribution toenvironmental management, biodiversity conservation,and land use planning emphasizes the need for specificPage 3 of 16

approaches to properly deal with the unique characteristics and the amount of these datasets [17].
An example in Brazil is the digital platform of CANASAT, launched in 2007, to monitor the sugarcane fields
where some of the farmers are still burning the sugarcane
harvest, which triggers the climate change effect (Fig. 2).
Using multispectral and multitemporal images, CANASAT can not only measures sugarcane field size, including three sub-classes (ratoon, expansion, and renovation),but also identify the type of harvesting (mechanization or
manual harvesting by burning) [18, 19]. The time-series
approaches provide the patterns of LULC changes intime and incorporate seasonality and phenological cycles
of vegetation into the analyses [20]. In that manner, satellite sensors with high-frequency acquisitions capability
provide time series of observations that can improve land
surface characterization at regional, national, and global
scales. That is the case of the PROBA-V sensor, launchedin 2013 as a continuity program of global land coverage
provided by the SPOT VEGETATION [21]. The PROBAV datasets are produced every 2 days at 300 m spatial resolution and every 5 days at 100 m spatial resolution (seeFig. 3). The images are available in surface reflectanceformat to be transformed as vegetation indices or fraction images derived from Linear Spectral Mixing Model(LSMM). The PROBA-V 100 m data have been used for
LULC classification in African countries [22], crop area
mapping of globally spread agricultural areas in Belgium,
Russia, Ukraine, and Brazil [23], and crop mapping of
Mato Grosso state, Brazil [24] among other applications.
The most common parameters derived from satellite
RS are the vegetative indices (VI), and the typical ones
are NDVI, EVI, and SAVI. Generally, the coarse/medium
satellite RS data (LANDSAT, ASAR, MODIS) are the data
resources for those Vis, which can be scaled up to theregional level. And these resources are long-run productsthat make users overlook the trends of production fromthe past to the present. This feature enables to the creation of maps of land use land change in a certain period[25]. It is recently reported that GCVI performs betterfor estimating canopy greenness than traditional indexes(NDVI and EVI), given that the green wavelength is more
responsible than red reflectance [26].
Drought indices for the detection of target populationsof the environment

Increases in agricultural drought are a serious issue on
global food production, particularly in the semi-arid andarid regions where the precipitation goes more variable
at the same time with a decrease in mean annual rainfall[27] and the drought events simultaneously consequently
accelerate soil degradation [28]. The development ofdrought-tolerant crop variety is the most important andJindo et al. Chem. Biol. Technol. Agric.

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Fig. 2 Monitoring sugarcane field area in Sao Paulo State, Brazil, by using CANASAT platform

feasible solution [29]. The target population of environments (TPE) is a major concept of environmental classification to be targeted in drought-tolerant breeding. Thisis based on the notion that the genotype-by-environment
interactions cause non-linear plant responses to theseries of environments [30, 31]. As for drought stress,
the tolerant genotypes or species vary based on different
drought circumstances associated with frequency, intensity, and duration of plant water scarcity [32, 33]. In addition, also soil types and farmer’s practices should accountfor the TPE. For example, plants grown on soils with highwater permeability and low fertility are more vulnerable
to drought stress [34]. Farmers’ practices such as irrigation and plowing also affect the intensity of drought
stress [35].
Although elucidating a TPE of the target region is
critically important to determine breeding objectives
for drought tolerance, it has not been easy because of alack of reliable data on historical drought events and its
patterns. Especially for drylands in Africa, the meteorological records and crop statistics are scarce and sometimes not available. Satellite RS can detect the occurrenceof drought stress spatially and temporally, and thus it isthought to be a powerful tool to elucidate TPEs across theregion. So far, drought indices and its application to agricultural fields have been well studied and summarized inprevious studies [36, 37]. However, little consideration

has been made for the application of satellite RS to crop
breeding. This section focused on applying satellite RSderived drought indices to detect TPEs and provide theinformation needed for drought-tolerant breeding.
As for drought stress in agricultural fields, TPE could
be defined as the historical pattern of drought occurrence characterized by the severity, frequency, and duration of plant water scarcity. As a method without satellite
RS data, the climate-based indices such as standardized
precipitation index (SPI) and standardized precipitation
evapotranspiration index (SPEI) have been used to identify such historical drought events [38]. However, plantdrought stress cannot be defined only by precipitation.
Thus climate-based drought indices are restricted to an
agricultural area with uniform soil type, same crop species, and same agricultural management. On the other
hand, because the application of satellite RS enables us
to evaluate the spectral reflectance of the ground surface covered by crop canopy, it reflects the direct featureof crop growth and physiological status, including theeffects of soil types and crop species. Therefore, satellite RS-derived drought indices are suitable to know thecrop status for wide ranges of agricultural areas, including various crop species, multiple soil types, and different
farmer’s practices.
Normalized difference vegetation index (NDVI) andland surface temperature (LST) are widely used forJindo et al. Chem. Biol. Technol. Agric.

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Fig. 3 The land-use map using PROBA-V, including forests (dark green), agricultural area (light green/yellow areas) and bared land (purple/
magenta)satellite RS-derived drought indices, and both are theprimary products of spectral reflectance [39]. The vegetation condition index (VCI; Eq. 1) represents the relativevalue of NDVI to the whole fluctuation range of the time
records [40]:VCI =NDVIt − NDVIt.min.
NDVIt.max − NDVIt.min(1)

NDVIt.max and NDVIt.min are the maximum andminimum values within the time records of NDVI for theperiod t (Fig. 4), where NDVIt is the value at a period t
in a year. The lower VCI represents the lower vegetation
mass than its potential, meaning severer stress. Sahooet al. [41] detected agricultural drought events from ahistorical transition of VCI and showed that the detecteddrought event corresponded to SPI. On the other hand,
they also reported that VCI was mainly related to severe
drought, but failed to see moderate drought. This might
be because there is time lag between drought onset andthe start of vegetation/NDVI decrease under moderate drought conditions [42]. Another problem is that the
decline in NDVI is not always caused by drought stress.
For example, disease and insect damage and delay insowing time also decrease VCI. Therefore, VCI needs
corroborative use with other indexes such as soil moisture data or yield statistics [43].
The temperature condition index (TCI; Eq. 2) is similar
to VCI but uses LST instead of NDVI [44]. This index isused based on the assumption that LST increases underdrought conditions due to decreased latent heat flux
from crop canopy and bare soil. Because the stomatalclosure and reduction of leaf transpiration are the initialplant response to drought stress [45], the increase in LST
occurs much prior to reducing plant biomass. Therefore,
compared to NDVI, LST is sensitive to vegetation water
status, and there is little time lag, and thus real-time
drought monitoring is possible:

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Fig. 4. Historical changes of NDVI in the West Africa region during 2001–2012

TCI =(LSTt.max − LSTt ),(LSTt.max − LSTt.min )(2)

where ­LSTt is the value at a period t in a year. ­LSTt.max
and ­LSTt.min are the maximum and minimum values
within the time records of LST for the period t. The
lower TCI represents severer drought stress. TCI usually has been used as a complementary indicator of VCI
for drought detection [46]. Seiler et al. [47] reported thatthe combined use of these indices explained more than
70% of maize yield variation, and it was well aligned with
precipitation patterns. However, actual drought intensity
still differs even for pixels with the same TCI values due
to different vegetation sizes. For example, TCI values are
possibly the same for pixels of drought surface with larger
vegetation and wet surface with small vegetation.
The VCI and TCI address the historically abnormal
drought events at a target area, although careful consideration with other meteorological data records and landuse information is needed for their application. Kukunuri
et al. [48] reported that the combined use of VCI and TCI
could successfully detect the historical drought without
requiring a priori information. However, those values
cannot be compared with those in other areas with different environments because the absolute values of NDVI
and LST are largely affected by the local settings such as
soil fertility, air temperature, and solar radiation. Therefore, VCI and TCI are available to define the TPEs for
relatively small areas with similar basal environments.
On the other hand, spatial comparison of the index
value is possible for vegetation and temperature condition index (VTCI; [49]) and temperature–vegetation dryness index (TVDI; [50]). Both are similar indices obtained
from scatterplots of NDVI and LST for an imaged area
(Fig. 5):VTCI =

LSTNDVIi.max − LSTNDVIi,
LSTNDVIi.max − LSTNDVIi.min(3)

Fig. 5 Determination of parameters for VTCI and TVDI fromscatterplot of NDVI and LST. The red and blue lines represent thewarm and cold edges drawn from pixels with maximum andminimum LST values at the given NDVI. NDVI and LST were obtained
from T

TVDI =

LSTNDVIi − LSTNDVIi.min.
LSTNDVIi.max − LSTNDVIi.min(4)

NDVIi is NDVI of the target area at a given time, and
LSTNDVIi is the LST value of the same pixel. ­LSTNDVIi.
­ STNDVIi.min are maximum and minimum LSTs
max and Lof pixels where NDVI value is the same as NDVIi. Thesemaximum and minimum values are generally calculated from linear regression between LST and the warmor cold edges in the scatterplot represented by the redand blue lines in Fig. 5 [51]. The lower VTCI and higher
TVDI represent severer drought stress. These indiceswell address the problem of TCI, which does not consider the effect of vegetation size on the LST. Because
VTCI and TVDI are calculated from LSTs of pixels withthe same NDVI, they can successfully detect the spatial
distribution of drought stress even for an imaged areaJindo et al. Chem. Biol. Technol. Agric.

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including vegetation heterogeneity. However, users needto pay attention that the warm and cold edges are takenfrom NDVI and LST of multiple crop surfaces, and theactual intensity of drought stress exposed is different for
crop species, even for the same TVDI values.
Difficulties with using VTCI and TVDI in the TPE
detection are that the relationship between LST and
NDVI varies over different time points because of thedaily or seasonal variability in background environments
such as air temperature and solar radiation including theeffect of solar illumination angle. This indicates that thewarm and cold edges are different for each of the images
in the time series, and thus, it is hard to compare thedrought intensity among the images of different days.
The temperature vegetation precipitation dryness index(TVPDI) is the combined index between TVDI and precipitation data provided from the tropical rainfall measuring mission (TRMM) [52]. The precipitation data wereused to normalize the basal environment of each NDVILST space taken from different seasons to detect the
spatiotemporal drought status. However, given that environmental factors, especially air temperature and solar
radiation, largely affect LST, the NDVI-LST space is hard
to apply for large areas with different climate zones suchas continental or sub-continental bases. Another difficulty is that LST data are rarely available during the wet
season due to the effect of the cloud, which deeply covers the imaged area. This drawback could be partly overcome by the microwave temperature vegetation index(MTVDI) calculated by microwave sensors instead of thebasic optical sensors based on that the microwave sensors are less affected by atmospheric conditions [53].
The above-mentioned drought indices are viable tools
to detect drought occurrence and have been improvedby additional information from other sensors. However,there are still restrictions for the long-term spatiotemporally drought detections in agricultural fields to understand the TPE. First, most indices can catch either oneof the historical drought events of a pixel (VCI and TCI)
or spatial distribution of drought at a given short period(VTCI and TVDI). Second, even the improved indiceswith multi-sensors detecting spatiotemporal drought
events (TVPDI) cannot apply fully for long-term analysis because of the limitation of long past data of additional sensors. Third, plant stomatal response regulating
the transpiration rate and the LST to its water status isdifferent for crop species [54]. Therefore, the relationship between the LST and drought intensity is diverse
depending on crop species, and thus the LST should be
evaluated together with other physiological indices suchas photosynthesis (PRI; [55]), chlorophyll fluorescence(SIF; [56]), and chlorophyll content (MTCI; [57]).

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Normalized difference water index (NDWI) is one of
the comprehensive drought indices available for historical and spatial drought detection [58]. This index represents the liquid water amount inside the vegetation. Thisis a primary index calculated from the following equation, and the lower values represent the occurrence ofdrought stress:

NDWI =RNIR − RMIR,RNIR + RMIR

(5)

where RNIR and RMIR are the reflectances of near-infrared and midinfrared, which corresponds to band 2 (841–876 nm) and band 6 (1628–1652 nm) for MODIS, band
4 (760–900 nm), and band5 (1550–1750 nm) for Landsat
EM and ETM + , and band 5 (845–885 nm) and band6(1560–1660 nm) for Landsat OLI, respectively. Jackson
et al. [59] reported that changes in vegetation water content during crop growth periods were better explainedby the regression model using NDWI than NDVI. On
the other hand, because the regression model highly
depends on the plant architecture, NDWI is not available
to diverse terrain and climate area due to land cover heterogeneity and soil type [60].
To overcome the drawback of the NDWI application,a normalized difference drought index (NDDI; Eq. 6)
was developed [61]. NDDI focused on the notion thatthe difference between NDVI and NDWI increases withdecreasing vegetation water content:

NDDI =NDVI − NDWI.
NDVI + NDWI

(6)

The higher values of NDDI indicate lower vegetationwater percentage and the occurrence of drought stress.
Due to the involvement of NDVI as a parameter, theeffects of vegetation size and soil type are incorporated
in this index. Thus, it is widely available to multiple cropping areas and is more sensitive to drought than the single use of NDVI or NDWI. Orimoloye et al. [62] applied
NDDI to assess historical and spatial drought severity and found the specific areas and periods of drought
occurrence. Because the NDDI represents a temporalplant water status and is little affected by meteorologicalconditions other than drought, it seems to be suitable touse for the TPE detection, although further studies areneeded to clarify the relationship between NDDI andphysiological factors related to plant drought responses.
Synthetic aperture radar (SAR) sensor

Another type of satellite RS data used in agriculture is
the synthetic aperture radar (SAR) system. Unlike theoptical sensor, microwave electromagnetic radiation is a
key component in an active SAR system transmitted toJindo et al. Chem. Biol. Technol. Agric.

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objectives and received the reflected echoes on the land
surfaces. This system enables to determine the structureof each object on the surface. SAR uses the motion of theradar antenna over a target region to provide finer spatial resolution than conventional beam-scanning radars.
Since SAR measures the distance between terrain features and the antenna, geometric distortions such as
foreshortening, layover and shadow occur in SAR image[63]. The returned signal which has been recorded by thesensor is usually expressed as backscattering coefficient(σ0), which is dependent on soil moisture content [64],but is also affected by surface characteristics, radar angle
and wavelength and so on [65]. Surface characteristics,
such as roughness and vegetation cover, have a significant
influence on the backscattering coefficient (σ0); thus,
many theoretical, empirical and semi-empirical models
have been developed to relate the SAR backscatter coefficient to soil moisture [66, 67].
An attractive trait of the SAR system is the advantage
of a long wavelength, which can penetrate through thecloud. At the same time, the optical sensors have a reliance on sunlight, which limits proper data acquisition.
Especially, this beneficial advantage can be helpful in frequently clouded areas in tropical regions. Another feature of SAR is that full polarimetric SAR comprises fourcombinations of polarization (HH, HV, VH, VV) thatcan monitor different phenology stages of plant growth,such as plant height [68] and biomass [69]. The sensitivity of the SAR data to the soil status enables monitoring
field conditions and management on the ground [70] anddrenching with water in the paddy rice area [71]. This
trait also provides an empirical estimation of soil moisture content [72, 73]. L-band SAR yields good results
under various canopy types because it has a higher penetration power through vegetation canopy to reach thesoil surface [74]. Compared with other bands, L-band
SAR has more sensitivity to soil moisture change due tothe larger emission [75–77] distorted measurement ofsoil moisture probably occurs in C-band because of highsensitivity to vegetation cover [78,79]. However, L-bandhas difficulty identifying certain crop types, given thatits wavelength is 1.2 GHz, which penetrates some plants
with a height between 6 and 10 cm.
Furthermore, spatial high-resolution (< 10 m) of existing SAR data resources such as SENTINEL and PALSAR
is a suitable tool for detecting variation in crop growthand soil property within a field and monitoring smallholder farming. Perennial crops are in the tropical region
an easily detectable crop for identification and estimationof biophysical properties such as cashew [80], rubber [81,82], almond [83], and palm [84, 85]. Creating a land-use
map covering different crop locations using SAR data is
an attractive approach to understanding and monitoring

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the field management at a community level (Fig. 6).
Mizuno et al. [86] exhibit the land-use map in Tanjung
Leban village, Indonesia, using five imagery scenes from
Fine Beam Duam (FBD)-ALOS-PALSAR. The map was
created based on a ratio of two backscatters of HH and
HV in dry and rainy seasons, which were later processedby interferometry. The underlying key advantage was the
combination of the strong points of HH and HV measurements of L-band. While HH has a sensitivity of distinguishing the state of the ground surface penetrating plant
canopy, HV is more sensitive to above-biomass [80, 87].
Another contribution of the SAR data to the agriculture sector can be seen as a component for creating a digital elevation map (DEM). The DEM data of The Shuttle
Radar Topography Mission (SRTM) is extensively usedworldwide to estimate elevation, slope, and watershed.
Other DEM data are also applied in agriculture such as
TanDEM, Advanced Spaceborne Thermal Emission,and Reflection (ASTER)-Global Digital Elevation Model(GDEM), ALOS world 3D-30m (AW3D30), TerraSAR-X
add-on for Digital Elevation Measurement (TanDEMXDEM), Advanced Spaceborne Thermal Emission and
Reflection (ASTER)-Global Digital Elevation Model(GDEM).
The Copernicus program’s policy for free and fully
open access has boosted remarkably the application of
the SENTINEL in agriculture. It is noteworthy to outline
some constraints of the SAR data. The limited frequent
visits are a major constraint to monitor the phenological development and field condition. Secondly, the future
study is required to improve the high accuracy of soil
moisture measurement. When it comes to creating a
retrieval soil moisture measurement by using multi-layers, the soil depth contribution to the L-band radiometersignal (~ 5 cm) is different from that of the C-band SAR
backscatter signal (∼2 cm) [87, 88]. The high-resolution
data often suffer from the saturation of values in highresolution satellite data [30].
One future study can be the combined application
between SAR and other sensor technologies. For example, combining SAR and hyperspectral imagery (e.g.,
Rapideye) can create more accurate land cover maps
where different crop types are cultivated [89]. Even cocoa
crops, which preferably grow under a shadow, can bedelineated with this combination in an agroforest system[90].
Water productivity in agriculture based on surface energybalance model
Evapotranspiration and water scarcity

The water scarcity problem in the agricultural field canbe addressed by increasing the amount of crop yield
per specific amount of water used. Estimation of actualJindo et al. Chem. Biol. Technol. Agric.

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Fig. 6 Land-use map in Indonesia by using SAR data (source: [60])evapotranspiration (ECa) is the only way to express theactual amount of water consumed. Therefore, measuring
actual evapotranspiration plays a more significant rolein coping up with the water scarcity problem. Seiler andMoene [91] summarized the major methods to estimate
the actual evapotranspiration as “lysimeter, flux towers, crop coefficient consideration, and satellite RS algorithms”. The central concept of actual evapotranspiration
is to assess the actual amount of water used by the crop
influenced by different factors. Lysimeter and flux towers estimate actual evapotranspiration per point, and it
requires intensive data collection [91]. Crop coefficientconsideration is not a practical way to estimate actual
evapotranspiration due to the limited availability of aconfidential coefficient value for the “natural vegetation”[91].
Surface energy balance is one of the satellite RS
approaches to estimate spatial and temporal ECa of crop
growth. Bastiaanssen et al. [92] created a surface energybalance model for land (SEBAL) to assess the waterproductivity using actual evapotranspiration and biomass of a crop. The main inputs of SEBAL models aresatellite images (Landsat), DEM, land use, and weather

data (Fig. 7). These inputs are used to directly estimates
albedo, vegetation index, and surface temperature to
be used as input on the final stage to calculate from soil
heat flux, sensible heat flux, and net radiation (see Fig. 7)[92]. SEBAL has been used worldwide to carry out aspatial variation of water productivity, yield, and ETa
for different crops under the different agro-ecologicalzone. SEBAL proved the Al-Khazir Gomal Basin, Iraq,
by providing spatially variable ETa and recommending the results for “monitoring irrigation water needs”[93]. Understanding SEBAL measured ECa, biomass,
and water productivity of a field or a basin supports theassessment of irrigation field’s water management, decision-making on designing and constructing of irrigation
infrastructures, and equitable water allocation [13, 93,94]. And in the end, we can estimate the biomass production, that is, a function of light use efficiency (LUE),
absorbed photosynthetically active radiation (APAR),change in time (∆t), and environmental stress factors
accumulated above-ground biomass for a certain period
is expressed as follows:Jindo et al. Chem. Biol. Technol. Agric.

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Page 10 of 16

Fig. 7 Summarized scheme of SEBAL model. Source: [95]Biomass = LUEt

0APARt.

All of the PAR cannot be absorbed by the canopy due
to reflecting and transmitting the nature of the leaves.
Therefore, only a fraction of PAR will be absorbed by thechlorophyll, called “Absorbed photosynthetically activate
radiation (APAR) as follows:

APAR = −0.161 + 1.257(NDVI) × PAR.
SEBAL water productivity (WP) for water‑related
decision‑making

Much research used the spatial variation of WP, yield,
and ETa to identify the gaps and to recommend themeasures [13, 96, 97]. Water managers and irrigationengineers are always interested in having an overview
of how water productivity is distributed over a basin
or irrigation system level to help make decisions about
improving water management [13]. It is always possible
to find water productivity from a field experiment for asingle location and crop growth. Having different crops
and bigger areas makes it difficult to have information
on a larger scale [13]. Since SEBAL is also used to assess
the variation of WP under one system, its map provides
information where water productivity is low, so water
resources and irrigation managers could plan a better
water distribution [13].

Knowledge of spatial variability of WP, yield, and actual
evapotranspiration will make decision-making effective.
The use of SEBAL for African vineyards shows how waterproductivity and yield spatial distribution with the distribution of the farmer interest, “farming strategies” of the
farmers are seen in SEBAL WP, yield, and ETa variation.
Bashir et al. [98] assessed the ETa value of sorghum fields
in 18 different farming groups in Sudan. A recommendation was made for the farmers and irrigation engineers
to understand how much water needs to be delivered to
specific farms and increase water productivity for eachfarm [98]. In contrast, spatial distribution in Indus andGanges river basins in India showed a significant spatial
variation of rice yield and variation of water productivity
for both wheat and rice crops, and the cause of the variation and scope of improvement were spatially analyzed[96]. Water fluxes in paddy rice area in India by using
SEBAL covering evapotranspiration variability was created to make heterogeneous cropping calendars for decision support system at regional level [99].
Monitoring water resource and biomass development
is a crucial tool for farming systems in semi-arid and arid
regions, and SEBAL was used again to assess the spatial
variation of wheat yield, water productivity, and evapotranspiration in different countries such as Mexico, India,
and Egypt [13]. ETa spatial variation study carried out inthe Hetao irrigation district, Northern China; ETa showsa significant variation in the study area, giving a higher

Jindo et al. Chem. Biol. Technol. Agric.

(2021) 8:58

ETa in arable lands but gave low ETa in sandy and desert
land [100]. They concluded that this model could be well
implemented in the Hetao area, with a relative error of
14.6% at field scale on 5–7 days basis and 5.6% at regional
scale on an annual basis.
Critical points of SEBALWith the availability of free and high-resolution satellite
images, it will be possible to assess more precisely thespatial variability of WP, yield, and ETa for smallholder
farmers having a limited field size. Recently, a new article regarding scaling-up to 30-m evapotranspiration rate
in several countries has been published using GGE [101].
However, even though SEBAL is has been proved to estimate ETa, biomass, and WP effectively in many parts of
the world, few main points need to be carefully considered. (1) The validation of SEBAL ETa with ground datais always recommended for different agroecology; (2)pySEBAL is a new version of the SEBAL scripting toolthat automates the selection of hot and cold pixels. Thismodel reduces the risk of the wrong choice of hot andcold pixels and cut-off the image processing time. Even
though SEBAL has been validated in different areas, theautomated selection of hot and cold pixel SEBAL needs
to be validated with ground measured data for different
agro-climatology. (3) Biomass accumulation calculation
with absorbed photosynthetically active radiation considers environmental stress factors such as moisture, heat,and vapor pressure stress. All the environmental factor
stress needs to be validated and adjusted for the target.
Development of evapotranspiration models after SEBAL
modelsOver two decades, a large number of new methods ofevapotranspiration have been reported since the establishment of the SEBAL model [92] as the starting point.
Apart from the surface energy balance models (SEB),SEBAL is the major representative. Other methods
have been developed based on different methodologies,
such as the Penman–Monteith method, Priestley–Taylor methods, and surface temperature–vegetation index(Ts-VI) space method [102]. These methods are contrasting approaches in terms of the degree of the complexity,
type of the variables derived from satellite RS, and theunderpinning process. Another trend is the integrated
approach with other related subject for other purposessuch as water balance method for the application at basin
scale as well as water–carbon linkage methods [103] with
the quantification of global gross primary productivity(GPP) together with evapotranspiration [104].
With the large development of remote RS technologies, those methods have been established. However,Page 11 of 16

some pitfalls exist in this development process, according to Zhang et al. [102]. Firstly, there is little informationon the research focused on identifying the uncertainty
and minimizing its propagation. Secondly, the integrated
approach using different evaporation methods can bea valuable asset to overcome the limitations. Thirdly, it
requires further development of the remote RS technologies in terms of resolution and resolution.
A recent approach for the evapotranspiration measurement
using SEBAL‑based models

It should be noteworthy that the new approaches inrecent years remarkably contribute to the improvement of the evapotranspiration measurement which theabove-mentioned difficulties have been partly resolved.
Especially, the latest research trends of monitoring water
management with a higher resolution map based on theevapotranspiration rate have gradually increased. The
common underpinning structure is the synergetic use
between high-resolution data (e.g., SENTINEL) andother satellite medium resolution RS (MODIS and/or
Landsat). For example, “Agriwater”, an R programming
package, was created for spatial modeling of energy balance and actual evapotranspiration in Brazil using SENTINEL, Landsat, and MODIS [105]. In contrast, theco-application of the enhanced SEBAL model, called“SEBAl” and SENTINEL-2 and MODIS LST, enables
monitoring monthly 10-m evapotranspiration rates in
Lebanon [106]. The high resolution (10m) mapping for
spatial variability in evapotranspiration, biomass andyield Tanzania has been developed based on SEBAL, Sentinel-2, and Landsat [107]. Also, another approach using
machine learning solely for MODIS data should be highlighted to improve the actual evapotranspiration measurement in Brazil [108]. SEBALIGEE, a web-based modelin Google Earth Engine, is also developed for the 30-m
evapotranspiration rates for a wide range of crop types indifferent countries [101]. To minimize the gap between
the simulation and the ground-truth data, the integration of soil moisture measurement using SENTINEL into
SEBAL model has been developed [109].
Assimilation of satellite RS data into crop growth model

Crop simulation models (CSM), generally structured
based on system analysis of plant phenology over different stages of plant development, are a useful tool to forecast crop growth and yield prediction on in-field scale up
to regional or country level. The origin of many CSMs
was sprouted in the 1960s by pioneer scientists such as
C.T. de Wit, Monteith [110, 111], and Duncan et al. [112].
Their main focus was the canopy assimilation related tolight interception and canopy architecture. As time wentby, the development of whole crop models was graduallyJindo et al. Chem. Biol. Technol. Agric.

(2021) 8:58

carried out in the 1970s [113, 114]. One of the main traits
of CSMs is the degree of complexity which largely varies
depending on the objective of each model usage and its
design. Basically, there are two different main categories
of CSMs. Firstly, there is a group of statistical models,often named “empirical models” and “correlative models”.
And these models are normally expressed with a regression equation. Relatively, less volume of the dataset isrequired compared to the second group of CSMs. As a
sub-category of statistical models, there are three maintypes referred to as time-series, panel, and cross-sectional approaches. Secondly, we have dynamic models,frequently called “process-based models”, and these are
created based on the different equations for each different phenological stage of crop development.
Given that these models employ functions that estimate the proxy of the developing process of plant andsoil being for a certain time, it requires plenty of input
parameters to run the models, compared to the statisticalmodel. The advantage of this model is the availability of
exploring future scenarios under different field management and different field conditions. As an example, thedesign of DynAcof model based on the for agroforestry
coffee production is a good reference not only because it
covers different indicators such as yield, carbon sequestration, canopy cooling effect, but also it is oriented for
end-users (e.g., farmers, extension members) after simplifying the complexity to the more practical tool as ameta-model [115].
However, it is noteworthy that a series of limitations
and bottlenecks exist with applying CSM, such as therequirement of a large number of variables, calibration
for a new crop variety, and not a practical approach forunpredictable climate conditions. Furthermore, when it
comes to yield prediction in developing countries, while
many CGMs demand daily meteorological informationas variables, only limited data are available in a small
number of observations (e.g., weather stations) [116].
To tackle these difficulties, using satellite remote-sensing to combine with the CSMs is a well-known strategy to overcome some limitations of CSMs and narrow
the gap between the predicted and measured yield byincreasing the accuracy. Many scientific works have beenreported on the integrated approach, such as APSIM[117], WOFOST [118], ORYZA [119]. Historically, thisapproach was proposed already in the 1970s [120, 121].
Nowadays, a wide range of different assimilation methods of assimilation are present such as forcing, calibrating, and updating. The vegetative indices parameters(e.g., LAI) are often used for assimilation into the cropmodel.
Owing to the potential propagation of the uncertainty
derived from satellite RS application, it is recommendable

Page 12 of 16

to use multi-objective variables to obtain more accurate
simulation such as evapotranspiration and fraction ofabsorbed photosynthetically active radiation (FAPAR),according to Gumma et al. [122]. Another approach also
has been developed recently on the basis of the fourdimensional variation algorithm (4DVar), considering
the anisotropy of background error and time window to
overcome the difficulty with certain irregular time intervals of observation and heterogeneous impacts of observations on the effect of yield simulation [123]. In the caseof a non-linear model, which often occurs in CSM, various Kalman filter types have been used to estimate theuncertainty [124].

Conclusion
We conclude that the technical means of RS have
expanded rapidly in agriculture over the last decades.
The wide range of different agricultural themes using RS
technologies (e.g., LULC, drought indices, and evapotranspiration) has been shown for the enhancement offield-based studies. However, our understanding of thephysiological processes behind satellite observations has
evolved at a somewhat slower pace. Thus, an importantresearch gap between the actual state and desirable stateof the knowledge exists in understanding the profoundecological meaning of remote observations. We believethat the application of RS technologies for adopting
CSMs, which simulate a crop status over the seasons andpredict the yield, represents an important contribution
to understanding what we can observe from space. The
aforementioned RS data and tools, including vegetation
indices, drought indices, SAR data, and energy balance,
are applicable for this combined approach. For example,
rather than using NDVI solely for the understandingof the canopy status, integrating these indices within acrop model can speculate on other state variables, consequently serving a more holistic view for the sake of
better understanding of crop and field status (e.g., soil
moisture).
Multiple RS usages help us for field monitoring andsupport decision-making for field management. However, the availability of diverse data sources can be adouble-edged sword: the first challenge is to synthesize
many available data sources so that the end users’ information is accurate and actionable. The acquisition of theground-truth data is essential for this matter to crossvalidate the data from RS. To tackle this challenge, theintegrated approach between RS and CSM can provide
heterogeneous information across different phenological stages and estimate the uncertainty driven by RS data.
Another option is creating a meta-model to simplify
complex processes for other stakeholders (e.g., farmers,
cooperative members, extension officers) who work inJindo et al. Chem. Biol. Technol. Agric.

(2021) 8:58

their field on a daily/weekly basis. The second challenge
is technical concern about how such a large mass of data
can be analyzed at a larger scale (e.g., global and regional
scales). It requires the availability of data storage, computation capacity, and a suitable telecommunication environment. Fortunately, many recent tools such as Google
Earth Engine freely provide access to pre-processed RS
data and offer the possibility of conducting the computations on the cloud, thus greatly increasing the area andthe complexity of the operations that can be carried out.
The available data might make plans decisions, especiallyfor end-users in developing countries who monitor theiragricultural fields at farm, regional, and country levels.
Acknowledgements
Keiji Jindo wishes to acknowledge financial support (3710473400-1).

Page 13 of 16

5.

6.

7.
8.
9.
10.
11.

Authors’ contributions
KJ, OK, YW, KI, EA and YEA wrote the manuscript; BM, FKE, YS and CK collaborated to the text redaction and formatted the whole text. All authors read andapproved the final manuscript.

12.

Funding
Not applicable.

13.

Availability of data and materials
Not applicable.

Declarations

14.

Ethics approval and consent to participate
For this type of study formal consent is not required.

15.

Consent for publication
For this type of study formal consent is not required.

16.

Competing interests
The authors declare that they have no competing interests.

17.

Author details
1
Agrosystems Research, Wageningen University & Research, P.O. Box 16, 6700
AA Wageningen, The Netherlands. 2 Center for Southeast Asian Studies, Kyoto
University, Kyoto, Japan. 3 Japan International Research Center for Agricultural
Sciences (JIRCAS), Tsukuba, Japan. 4 Colorado State University, Fort Collins, CO,USA. 5 National Institute for Space Research (INPE), Av. dos Astronautas, 1758,
São José dos Campos, SP CEP: 12.227‑010, Brazil. 6 Japan Aerospace Exploration
Agency (JAXA), Tsukuba, Japan.
Received: 2 June 2021 Accepted: 4 September 2021

18.
19.

20.
21.

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