=Paper=
{{Paper
|id=Vol-2761/HAICTA_2020_paper36
|storemode=property
|title=Evaluation of a Satellite Drought Indicator Approach and its Potential for Agricultural Drought Prediction and Crop Loss Assessment. The Case of BEACON Project
|pdfUrl=https://ceur-ws.org/Vol-2761/HAICTA_2020_paper36.pdf
|volume=Vol-2761
|authors=Emanuel Lekakis,Stylianos Kotsopoulos,Gregory Mygdakos,Agathoklis Dimitrakos,Ifigeneia-Maria Tsioutsia,Polimachi Simeonidou
|dblpUrl=https://dblp.org/rec/conf/haicta/LekakisKMDTS20
}}
==Evaluation of a Satellite Drought Indicator Approach and its Potential for Agricultural Drought Prediction and Crop Loss Assessment. The Case of BEACON Project==
https://ceur-ws.org/Vol-2761/HAICTA_2020_paper36.pdf
Evaluation of a Satellite Drought Indicator Approach
and its Potential for Agricultural Drought Prediction and
Crop Loss Assessment. The Case of BEACON Project
Emanuel Lekakis1, Stylianos Kotsopoulos2, Gregory Mygdakos2, Agathoklis
Dimitrakos2, Ifigeneia-Maria Tsioutsia2, Polimachi Simeonidou2
1
Agroapps P.C. 54-56 Themistokli Sofouli, Thessaloniki, 54655, Greece; e-mail:
mlekakis@agroapps.gr
2
Agroapps P.C. 54-56 Themistokli Sofouli, Thessaloniki, 54655, Greece
Abstract. BEACON is a market-led project that couples Earth Observation (EO)
with weather intelligence and blockchain to deliver a toolbox for timely, cost-
efficient and actionable insights for the Agricultural Insurance (AgI) sector.
BEACON enables insurance companies to exploit the untapped market potential
of AgI, while contributing to the redefinition of existing products and services.
The Damage Assessment Calculator of BEACON employs remote sensing in
order to improve the quality and cost-effectiveness of agri-insurance by: i)
increasing the objectivity of the experts’ field inspections; ii) reducing the cost
of field visits and iii) increasing farmers’ confidence in the estimation results.
This paper provides an approach that employs a satellite derived agricultural
drought indicator, implemented in the operational workflow of BEACON that
can be used by AgI companies to improve the prediction and crop loss
assessment due to drought.
Keywords: BEACON; Agricultural Insurance; Drought; NDVI-Anomaly.
1 Introduction
The agriculture sector is highly vulnerable to drought, as it depends directly on
water availability (Tsakiris and Tigkas, 2007; Meng et al., 2016). Although each crop
differs in its resilience to water stress, drought can cause crop failure if the weather
conditions are adverse during the most sensitive stages of crop growth (Peña-Gallardo
et al., 2019). Droughts are difficult to measure and quantify (Vicente-Serrano et al.,
2016), and consequently a wide range of drought indices have been developed to
provide tools for quantifying their effects on vegetation.
Vegetation is constantly monitored for conditions of drought using vegetation
indices (VIs). Among them, NDVI is the one most often used for monitoring the
environmental conditions (e.g. grassland status, land degradation, desertification, and
drought) all over the world (Hassan et al., 2018). NDVI is commonly calculated using
image data from polar orbiting satellites, which carry sensors detecting radiation in red
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and infrared wavelengths. The Moderate Resolution Imaging Spectroradiometer
(MODIS) is most widely used for drought monitoring. There are several NDVI-based
approaches for detecting and mapping drought using its long-term mean for a pixel or
region at a given time. A simple and common NDVI-based approach for monitoring
drought, is the relative NDVI Anomaly (NDVIA) (Anyamba and Tucker, 2012;
Nanzad et al., 2019).
The use of anomaly isolates the variability in the vegetation signal and establishes
meaningful historical context for the current NDVI to determine relative drought
severity. NDVIA provides a powerful tool for agricultural drought monitoring to ease
identify drought-sensitive areas and estimate possible crop damage. This possibility
emerges from the well-established potential of remote sensing to detect change on
vegetation.
This short paper presents the methodology developed in BEACON project, for crop
loss assessment due to drought, based on the NDVIA and ground truth data made
available by AgroSeguro, the Spanish AgI provider. The dataset consisted of drought
damage percentage occurred on wheat and barley. A validation procedure was
followed to test the accuracy of the developed EO product against in-situ data.
2 Materials and Methods
2.1 Satellite data
The NDVI MODIS/Terra daily Level-2G product (GMOD09Q1) was used for
drought monitoring. This product is provided as a service federation of the GIMMS
MODIS NASA-UMD with a spatial resolution of 250 m and as a composite 8-day
product, since 2001. To develop the methodology, the MODIS NDVI datasets were
downloaded and cropped to the provided parcels extent. The total period that was taken
into account corresponded to the growing season from 17 November to 26 June (2017,
2018 and 2019). The number of 8-day records per growing season was 29. After image
acquisition, the Relative NDVIA was calculated as follows:
NDVIAi, j = 100 × (NDVIi, j - NDV Iave, j ) / NDVIave, j (1)
where i subscript denotes the year, j subscript denotes the period (8-day), and NDVIave,j
is the average value for the same period j from a number of years (the multi-year mean
of the specific 8-day). The Relative NDVIA indicates the variation of the current 8-
day composite compared to the long-term average, where a positive value denotes
enhanced vegetation conditions compared to the average, and a negative value
indicates comparatively poor vegetation conditions (Vaani and Porchelvan, 2017). The
historical average was based on the NDVI values of the corresponding 8-day period
from 2001 until present.
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2.2 In-situ data
In BEACON, in-situ data were made available by AgroSeguro that manages AgI on
the Spanish market at national level. Drought damage originated from the rural area of
Ávila and Segovia. In this region the predominant crops are rainfed barley and wheat,
covering 26% and 22% of the total area, respectively. Wheat and barley growing
seasons begin by the middle of December and last until early July. The most frequent
damaging weather phenomena in the regions are drought and hail. Number of damaged
parcels was 91 (2017), 13 (2018), 42 (2019). The parcel size ranged between 1 ha and
10 ha, with the majority of the parcels area being approximately 4 ha and an average
size of ≈5.7 ha. Based on the spatial resolution of 250 m, the NDVIA pixels
corresponding to each parcel ranged from 1 to 21. Damage severity caused by drought
ranged between 10% and 100%. Approximately 75% of the samples were used to
develop the methodology, while 25% were used to validate the developed EO product.
2.3 Indicator-impact functions
To relate the level of drought severity, expressed through NDVIA, to the number of
reported impacts and the onset of drought (Naumann et al., 2014), indicator-impact
functions were derived. These sigmoidal regressions are used to quantify the drought
impact in terms of production (damage) or economic return (loss) (Bachmair et al.,
2015). The performed regressions were based on specific crop data (winter wheat and
barley crop damage) and localized climatic conditions, therefore, several agro-
meteorological aspects had to be taken into account, such as:
§ The developed methodology for drought applies only for winter wheat and
barley.
§ The crop loss assessment methodology is the same for winter wheat and barley
crops.
§ Vegetation stress obtained through the calculation of NDVIA for the growing
seasons of wheat and barley (2017-2019) was attributed only to drought events.
§ Variation of intensity of vegetation stress reflects the variation of drought
severity throughout the growing season.
§ Under natural conditions, the severity of vegetative drought evolves gradually
in time.
§ Barley and wheat yields are vulnerable to drought during spring, responding in
both short drought timescales (1–3 months) and medium (4–6 months)
timescales.
§ Peña-Gallardo et al. (2019) found no major differences in precipitation patterns
among districts in Spain during a drought study. Differences found were
focused mainly in the average maximum and minimum temperatures. Therefore
the methodology could be expanded to include winter wheat and barley crops
in the entire Spain.
In wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), drought stress
can occur at any growth stage (Sallam et al., 2019). Although barley is less dependent
on water availability at germination and the grain filling stage than wheat, Peña-
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Gallardo et al. (2019) found that the temporal responses of these crops to drought
conditions were very similar in both space and time.
3 Results and discussion
For the development of the methodology an indicator-impact function was derived
by correlating the drought severity with the in-situ assessment of the damage. The
drought severity was defined as the sum of the absolute values below zero of the
NDVIA during a certain period of time. Bachmair et al. (2015) and Naumann et al.
(2014) proposed this approach using meteorological drought indicators. The
accumulation time period was limited from the 1st of April to the end of June,
corresponding to the reproductive and senescence stages. A study by Hernandez-
Barrera et al. (2016) demonstrated that during spring, precipitation deficit is the most
influential climate factor affecting cereal growth.
For the accumulation of the NDVIA values, the median of the pixel values,
corresponding to a parcel, was selected for the regression which was considered as the
most representative to be linked with the damage. An example of the procedure is
illustrated in Fig. 1.
70 200
Absolute Negative Sum
50
Relative NDVIA
30 100
10
0
-10
-30 -100
-50
-70 -200
15/10 4/11 24/11 14/12 3/1 23/1 12/2 4/3 24/3 13/4 3/5 23/5 12/6
Date
Fig. 1. Relative NDVIA and Absolute Negative Sum during the growing season 2016-2017.
Gridded datasets of the SPEI indicator were acquired for the growing seasons of
2017-2019 from the SPEIbase1. According to SPEI values, wheat and barley crops
faced severe drought during the 2017 and 2019 seasons, justifying the small number
of in-situ data provided for 2018. Mutsotso et al. (2018) found that external signs of
stress expressed through the NDVI, lagged SPEI approximately one month. However,
they supported that SPEI correlated well with NDVIA. SPEI values are illustrated in
Fig. 2.
1
http://spei.csic.es/map/maps.html#months=1#month=8#year=2019
247
2,5 2016-2017
2,0 2017-2018
1,5 2018-2019
1,0
0,5
SPEI
0,0
-0,5
-1,0
-1,5
-2,0
-2,5
Oct Nov Dec Jan Feb Mar Apr May Jun
Month
Fig. 2. Fluctuation of SPEI for Ávila and Segovia regions, during the growing seasons.
To select the best function type, the goodness of fit between the drought severity
expressed as the absolute negative sum of the relative NDVIA and the damage
percentage was assessed assuming linear, power-law, exponential and sigmoidal
relationships. The sigmoidal relationship that provided the best explanation of variance
with a correlation coefficient of r = 0.53, is the following:
y = (78.6 + 106.1 × x 0.31 ) / (2.43 + x 0.31 ) (2)
where y is the expected impact in terms of % damage, x is the absolute negative sum
of the Relative NDVIA. Fig. 3 provides the indicator-impact function plotted against
the provided damage data.
100
Damage Assessment (%)
80
60
40
20
0
0 50 100 150 200 250 300 350 400 450
Absolute Negative Sum of NDVIA
Fig. 3. Drought-damage function depicting the relationship between reduction in crop
production and drought severity.
A number of 28 drought damage cases were used to characterize the accuracy of the
developed methodology and evaluate the performance of the EO data product. The
following statistical criteria were applied, namely i. the mean error (ME), ii. the root
mean square error (RMSE), iii. the normalized RMSE (nRMSE), iv. the coefficient of
residual mass (CRM) and v. the correlation coefficient (r) between the observed and
predicted data (Antonopoulos, 2000). Table 1 provides the results of the statistical
analysis.
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Table 1. Statistical criteria between observed and calculated drought damage.
Metric Value
ME (%) -3.29
RMSE (%) 8.28
nRMSE% 9.88
CRM 0.04
r 0.69
4 Conclusions
According to the statistical analysis results, the following conclusions are drawn
based on the performance of the indicator-impact equation to describe crop damage
due to drought:
• The developed methodology slightly underestimates drought damage.
• The RMSE% value indicates good performance of the methodology to
describe the drought damage.
• The nRMSE is considered acceptable and indicates relatively good
correspondence between observed and predicted damage.
Overall the methodology describes fairly well the selected cases. However, the
number of damage cases used for the validation cannot be considered adequate for safe
results at this point. During the two-year pilot phase of the project, the algorithms for
crop loss are expected to be further refined with new data that will be collected.
5 Summary
BEACON solution employs a multi-satellite approach in order to provide safe and
reliable estimates on crop damage, for any type of AgI. BEACON takes into account
damage by hail, floods, wild-fires and drought which are the four most devastating
hazards of agricultural production worldwide. This paper presents a simple crop-
specific methodology that takes advantage of the historical MODIS NDVI records to
synthesize a service for drought damage quantification. The service will support AgI
companies in assessing and calculating damage to proceed with indemnity payouts of
claims. In-situ data that will originate from AgI companies, most of which are early
adopters of the BEACON’s solution, will be used to further fine-tune the algorithms.
Acknowledgments. This project has received funding from the European Union's
Horizon 2020 Research and Innovation programme under grant agreement No 821964.
The insurance data were kindly supplied by AgroSeguro.
249
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