Sentinel-2 based Remote Evaluation System for a Harvest Monitoring

of Sugarcane Area in the Northeast Thailand Contract Farming

Soravis Supavetch

Department of Civil Engineering, Kasetsart University, Bangkok, Thailand

Keywords: Sentinel-2, Harvest Activity Detection and Remote Sensing for Agricultural Monitoring.

Abstract: Sugarcane is one of five important agricultural crops (Rice, Cassava, Sugarcane, Hevea, and Palm) and its

critical to Thai’s economy. From these important, several decades that government pays attention to

support the industry and help to stabilize the sector, enabling sugarcane mills to maintain their profitability

even during times of depressed sugar prices in the world market. This role of sugarcane supply chain

consists of the growers, millers and associated logistics personnel. Each miller has thousands of

smallholders who grow sugarcane with their contract but the farmer can sign more than one contract each

crop season depending on various factors such as prices and a loaning rate. Highly these competitive need

an efficient monitoring procedure to control their contract in a harvesting peak period. The monitoring of

the farmland requires tracking of them at an individual level that almost impossible for field visits. The

initiated idea of this research is from the study of the European Common Agricultural Policy (CAP) that

plan to use remote sensing data (Control with Remote Sensing: CwRS) for controlling and monitoring

agriculture land in growing season support a subsidy administration in the post-2020 timeframe. From the

aims of the control with remote sensing in CAP and also in this sugarcane industry, the purpose of this study

is checking the claimed parcels in an office in order to reduce the number of field visits. This paper

introduces an approach for that objective which using Sentinel-2 data for a harvest detection. An algorithm

(or a processing chain) in which demonstrated in this paper are an atmospheric correction, vegetation

harvest index processing, data composite (cloud-free and the bare soil inspection), and geostatistical

calculation of farmland for harvesting indicator. The results show an ability of the detection using remote

sensing and the discussion for future improvement are explained in a conclusion.

1 INTRODUCTION

Thailand is the world’s second-biggest sugar exporter

which product 14.3 million tons in 2017. Every year,

sugarcane business activities are involving a farming

and contract management. These contract between

mill and growers which mostly are the smallholders.

At the end of the season (harvest period), mill sends

their employee to monitor for keeping a contract in

which grower should send their products to the

factory with at least following the value that declared.

Over the last few years, Sentinel data become a

key technology in which EU community used to

support the monitoring of agricultural area responding

to the food security policy such as the European

Common Agricultural Policy (CAP). The use of

those satellite data reduces a field visit and shift

operation from sample inspections to large-scale

monitoring (Kanjir et al., 2018) For example, DHI

GRAS has recently completed a pilot study together

with the Danish Agrifish Agency that utilizes Sentinel

data within the field of grass mowing and catch crop

monitoring (European Space Agency). Another

example is the SEN4CAP project that aims to

provide, validated algorithms, products and best

practices for agriculture monitoring relevant for

management of various of CAP measurements on

crop diversity, activity identification, detection of

fallow land, catch crops, land abandonment, and so on

(Devos et al., 2017).

On the same objective of those considering remote

sensing data to support the monitoring of agriculture

activities and environment-related phenomena in

Thailand agriculture industry. This study

demonstrates the utilization of using Sentinel

technology for implementation in the sugarcane

industry Thailand, the details of that implementation

can be explained in the following sections.

234

Supavetch, S.

Sentinel-2 based Remote Evaluation System for a Harvest Monitoring of Sugarcane Area in the Northeast Thailand Contract Farming.

DOI: 10.5220/0007723002340241

In Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2019), pages 234-241

ISBN: 978-989-758-371-1

2 OBJECTIVE

The aim of this study is to develop the methodology

in a parcel-based technique for a sugarcane

harvesting activity detection at a farm level of

sugarcane industry’s Thailand using Sentinel-2 data.

3 STUDY AREA

The study area is located in the northeast of Thailand

(Figure 1), where sugarcane is mainly cultivated

under rain-fed conditions. Planting time is at the end

of the rainy season, October-November. The

remaining moisture in the soil supports the

germination of cane and guarantees its survival

through the dry season. The age of sugar cane in

which planted during November-December to the

March-May, will be 4-6 month. Then the coming

rainy season will enable the sugarcane to root deeply

into the soil and grow. The sugarcane will be strong

and can endure dryness when there is a shortage of

rain, which may occur during July-September.

Agriculturists in this region regard planting during

this period more suitable than planting in May,

because it more expense of mowing weeds and it is

easy to find species.

Figure 1: Northeast of Thailand, the study area.

4 SATELLITE DATA

Ministry of Industry controls the period for

crushing sugarcane in 2018 (production year) by

allowing the mills to start crushing at 15th of

November 2018 until the end of May or June.

Thus, Sentinel-2 data during December 2018 were

collected from the Copernicus Open Access

Hub (https://scihub.copernicus.eu/) for these harvest

monitoring process. The plantation area, which is in

a mill’s contract, is used to be a boundary of

sugarcane farm for processing Sentinel-2 data to

determine the cut during the investigation period.

5 ALGORITHM

This study uses Sentinel-2 to detect the harvest

activity at a farm level. Bare soil appearance is a

key for determining that occurs from a cut activity

and various Vegetation Indices (VIs) can be used to

detect those cutting of sugarcane. Index

characteristics which convenient to indicate the cut

(the plant is disappearing) are the ability that can

eliminate another factor out of plant appearance

factor. These external factors are solar and viewing

geometry, soil background, and atmospheric effects

(Rondeaux et al., 1996). Some of these can be

controlled. The spectral reflectance of plant canopy

is obtained with a soil influenced value and

depending on plant density. In the harvest period,

plant density in reflection data between plant and no

plant (bare soil) can be extended by soil contribution

adjustment. Another factor i.e., atmospheric effects

uses SEN2COR, a processing tool, provides

atmospheric, terrain, and cirrus correction of Top-

Of-Atmosphere Level 1C to create Bottom-Of-

Atmosphere Level 2A product from Sentinel-2 data.

Subsequently, L2A product produced another layer

such as a Scene Classification (SC) map, the cloud

mask will be used in a composite process to create

15 days period image (cloud-free) for cutting

detection by using geostatistical of pixels in a land

parcel at the end of the algorithm.

5.1 Scene Classification and

Atmospheric Correction

Scene classification and atmospheric correction are

the processes in SEN2COR, the main output is the

Cloud Screening (shadows, cloud shadows, and

cirrus) (Louis et al., 2016). The classified result

contains 11 classes, only vegetation (no. 4) and bare

soils (no. 5) as demonstrated in Figure 2 are selected

for the processing algorithm in this study which is

used determining a cut activity over the sugarcane

pixels. Clouds, cloud shadows, and cirrus are

masked and not included in an analysis process in

order to avoid an error of the interpretation.

Atmospherically corrected images from

SEN2COR were first evaluated with the sampling

fields. The comparison of pixels over 10 sampling

Sentinel-2 based Remote Evaluation System for a Harvest Monitoring of Sugarcane Area in the Northeast Thailand Contract Farming

235

Figure 2: Scene Classification image in which only

vegetation and bare soils are selected for the procedure of

the detection.

(a)

(b)

Figure 3: Illustration of the comparison between the TOA

reflectance and BOA reflectance signature. The example

(a) shows a plot of the sugarcane experimental field, 05

December 2018, (b) shows a plot in a cloudy day over the

vicinity area, 12 December 2018.

on 05 December 2018 shows that the different

radiance typically peaks in the green reflectance and

lower values in the Red Edge, NIR, and SWIR

(Figure 3a). But the comparison on a cloudy day on

15 December 2018 shows the difference radiance

typically more different and quite symmetrically

shape (Figure 3b). Thus, NDVI or SAVI indices from

which TOA or BOA might not significant difference

in term of the ratio of the difference between

calculated bands especially on the harvest activity

detection.

5.2 Soil Adjusted Vegetation Index

(SAVI)

In a theory, SAVI is more reliable than Normalized

Difference Vegetation Index (NDVI) in some

circumstance due to reducing the influence of the

soil spectra in the reflectance. The soil contribution

through the reflectance is depending on plant

density, row effects, canopy geometry, wind effects,

and so on (Rondeaux et al., 1996). Experimental

study reveals that a given type of soil variability, the

(bare) soil reflectance at one wavelength is often

functionally related to reflectance in another

wavelength in linear relation (Jasinski and Eagleson,

1990). Several vegetation indices i.e., soil-adjusted

vegetation index (SAVI), transformed soil-adjusted

vegetation index (TSAVI), modified soil-adjusted

vegetation index (MSAVI), and environment

monitoring index (GEMI) have been developed

using the coefficient of these relationship for

reducing the effect of soil (Huete, 1998). The

selection of index for using depend on the known of

the coefficients of the soil line and that cannot be

generalized because of its variability.

For the detection of a harvest activity, SAVI

which was developed from (Huete, 1998) is selected

for use in this study due to its generalized of the

related coefficient. The study of those canopy

background adjustment factor in Thailand does not

exist thus the recommended factor from Sentinel

Hub (L=0.48) (European Space Agency) is used as

the following equation.

SAVI = (1+L)*(NIR-R) / (NIR+R+L)

(1)

First, the evaluation between SAVI and NDVI

was performed in three different types of covering of

sugarcane i.e., high density (P01), intermediate

density (P03) and low density (cut) (P02). The

following graph (Figure 4) demonstrated that NDVI

is lower reflectance and convinced that the ability of

SAVI is better for detecting the harvest over the land

parcel using the threshold method.

No data

Defective pixel

Dark features

Cloud shadows

Vegetation

Bare soils

Water

Cloud low probability

Cloud medium

probability

Cloud high probability

Thin cirrus

Snow or ice

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236

Figure 4: Comparison of SAVI and NDVI over the land

parcel i.e., high density (P01), intermediate density (P03)

and low density (cut) (P02).

5.3 Cloud-free Data Composite

The problems of single date remote sensing

analysis such as cloud contamination, atmospheric

attenuation, surface directional reflectance, and

view and illumination geometry were studied and

proofed that can be reduced by the composite of

multi-dates. For many decades Maximum Value

Composite (MVC) procedure has been

implemented by increasing the number of

acquisitions into a single composite image for

reducing those factors. Since the upcoming of

Sentinel-2 (S2A and S2B), the high spatial

resolution and its revisit frequency of 3 -5 days, the

data can be used to monitoring agriculture at a farm

level. Sentinel-2 for Agriculture Project (Sen2-

Agri) Bontemps et al., (2015) provides the

international community for finding the best

practices to process Sentinel-2 data in an

operational manner into relevant earth observation

(EO) agricultural products and recommended 7 to

10 day basis for cloud-free surface reflectance

composite.

Due to Thailand located in the tropical zone, an

annual average rainfall reports 116 mm based on

1981-2010 period (Thai Meteorological

Department, 2015), the test of data composite

shows a minimum of 15 day is suitable for the

northeast of Thailand to implement the cloud-free

reflectance (Figure 5).

Northeast of Thailand mostly done by the labors

which three parties are involved in relation to

cutting interval, sugar mill owners, cane farmers,

and truck operators. During the peak season,

supply is higher than the capacity of the mills. At

that time hundreds of trucks can be seen queuing in

front of the mills, waiting to unload sugarcane

(Chetthamrongchai, 2001). This condition making

the crushing interval by 4-5 months (December to

April). The composite image of cutting activity is

invert of the traditional composite by using the

Figure 5: The cloud masked image interval 15 days of

S2A and S2B composition of SAVI.

Minimum Value Composite (MinVC) for the cutting

detection. Figure 6 (a)-(e) show the time-related cutting

activity over the farm parcel. In this study, 15 day period

with MinVC is used to produce a pre-processing cloud-

free data for the next step.

(a)

(b)

Figure 6: SAVI images of (a) 03 Dec 2018, (b) 13 Dec

2018, (c) 18 Dec 2018, (d) 23 Dec 2018, and (e) 28 Dec

2018.

Sentinel-2 based Remote Evaluation System for a Harvest Monitoring of Sugarcane Area in the Northeast Thailand Contract Farming

237

(c)

(d)

(e)

Figure 6: SAVI images of (a) 03 Dec 2018, (b) 13 Dec

2018, (c) 18 Dec 2018, (d) 23 Dec 2018, and (e) 28 Dec

2018 (cont.).

5.4 Geostatistical of Parcels

From the farm contract of sugarcane in Thailand, an

area or a parcel of land is known from field

estimation using mobile GNSS. Thus, statistical of

pixels can determine using that GIS information.

The vector polygon is translated into pixels mask for

extracting the SAVI data which locates inside the

parcel.

Pixels inside a farm parcel are extracted and

calculated in the term of descriptive statistics, min,

max, mean, median, standard deviation, and

percentile. Figure 7 demonstrates the geostatistical

calculation on the five parcels which have three

types of activities, (T1) “no cut yet”, (T2) “in

progress”, and (T3) “finish”. From the calculation,

mean and median can be used to indicate "in

progress" which close to "finish", but cannot

distinguish the difference between "in progress" and

"no cut yet". The values such as min, max, and

percentile are an indicator which can distinct "no cut

yet" and "in progress". From this inspection, the

cutting in a certain part of a parcel does not make a

significant change on mean and median, but min

value is significantly changed until finish the cutting

the values, max, median, and mean are less than the

threshold value of bare soil (0.4 or 4,000 in this

study).

(a)

(b)

Figure 7: (a) SAVI at 18 Dec 2018 and five farm parcels,

(b) boxplot of the farm parcels.

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5.5 Thresholds for Cutting Activity

The required statuses of the cut activity for the mills'

utilization are "no cut yet", "in progress", and

"finish" as described in the previous section. The

status of each parcel performs in 15 days period by

composited SAVI. The suitable threshold for this

study is as follows.

Table 1: Thresholds of the three types of the cutting

activities.

Status

Min

Mean

Percentile(75)

no cut

yet

> 4000

> 5000

progress

< 4000

> 3000

> 4000

finish

< 4000

In Table 1, the statistical key features which are

used to indicate the status “no cut yet” and “finish”,

are Min and Percentile (75). When grower starts

their cutting sugarcane a min value is decreased to

the value less than 3000 until percentile (75)

decreasing to lower 4000, the status then will be

changed to “finish”.

6 PROCESSING SYSTEM

Most agriculture parcels in Thailand are mostly

smallholder farms that area is between 2 – 3 ha and

frequently change their plant type responding to a

unit cost, product price, weather, and the policy of

government subsidy and so on. Cause of this behave

of Thai's agriculture and thus requires the time

related potential technique for observations spread

over time. For supporting this requirement of

agriculture remote sensing, Harvest Monitoring

through Remote Sensing (HMRS) was designed and

developed to aim the investigate the harvest of

sugarcane farmland from analyzing Sentinel-2

dataset, using fully open source or free software,

library, and programming language i.e., custom

Python script, Linux shell scripts, GDAL

(Geospatial Data Abstraction Library), OGR

(OpenGIS Simple Features Reference

Implementation), Numpy, Matplotlib, SEN2COR.

The workflow scripts (Figure 7) are explained as

follows.

Figure 8: Harvest Monitoring through Remote Sensing

(HMRS) workflow.

6.1 Query and Downloads S2 Datasets

Copernicus Open Access Hub provides OData

API for accessing Sentinel datasets over core

protocols i.e., HTTP and REST with an ability to

handle a large set of client tools for performing a

query and retrieving the Sentinel data

(European Space Agency). The python script is

executed every day for querying dataset using

$filter function with a grid name and date in order

to retrieve an observation data as following

example.

https://scihub.copernicus.eu/dhus/odata/v1/

Products?$filter=substringof('T47QQV',Name)

+and+ substringof('_MSIL1C_YYYYMMDD',Name)

6.2 Create Scene Classification and

Atmospheric Correction

SEN2COR toolbox is executed the command

L2A_Process for producing the scene

classification image and performing atmospheric

correction into the 20m resolution image.

From the lack existing of suitable experimented

parameters for Thai regional area for processing

L2A, thus the default parameters are used in this

system as follows.

Query and Downloads S2 Datasets

Create Scene Classification and

Atmospheric Correction

Create Vegetation Index

15 Days Composite

Geostatistical Calculation

Assign Status

L1C

L2A

SAVI

Cloud-free SAVI

Cloud mask

Stat.

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239

Table 2: L2A_Process parameters.

Processing Parameter

Value

Nr_Process

AUTO

Aerosol_Type

RURAL

Mid_Latitude

SUMMER

Ozone_Content

331

WV_Correction

WV_Watermask

Cirrus_Correction

TRUE

BRDF_Correction

BRDF_Lower_Bound

0.22

Smooth_WV_Map

100

WV_Threshold_Cirrus

0.25

6.3 Create Vegetation Index

A little quite improve here is a conversion of data

type from float SAVI to integer SAVI, in which

putting into the equation for a smaller GeoTiff and

the coordinate reference system is still used UTM

WGS84 as same as the source Sentinel-2.

The naming of SAVI product that should explain

here is the design on the support of manual selection

by the researcher that the name supports the sort in

which area are grouped and date-time can be sorted

ascending or descending. This naming aims to build

and initiate the compact system in order to have an

ability for the improvement in the future for a new

analysis model. The naming is as following

structure.

TZZGGG_YYYYMMDDTHHMMSS_VVVV.tif

ZZ = zone number

GGG = grid characters

YYYY = datatake sensing year

MM = datatake sensing month

DD = datatake sensing date

HH = start hour of sensing

MM = start minute of sensing

SS = start second of sensing

VVVV = index name

In order to build a cloud-free image for the next

composite procedure, Scene Classification (SCL) is

used here as the mask array in order to filter only

vegetation and bare soil pixel by using pixel class

number 4 and 5 respectively.

Figure 9: Filter vetgetation or bare soil from source SAVI.

6.4 15 Days Composite

The 15-day images are investigated in order to select

the minimum SAVI each pixel series for building

the 15 days composite image. If no SAVI found the

pixel is set to no data value.

Figure 10: SAVI composite for harvest detection.

6.5 Geostatistical Calculation

First, OGR is used to read the features from land

parcels of sugarcane then each parcel is selected to

create a raster mask array, thus SAVIs from a

composite image in which the same location of the

mask is extracted from the source into the

calculation procedure as demonstrated in Figure 10.

Numpy, average, mean, median, min, max, and

percentile are the functions which are used to

calculate the geostatistic for the status determination

Figure 11: Pixels masked using farm polygon for

geostatistical calculation.

Cloud-free. SAVI

…

SAVI

SCL

Comp. SAVI

…

Datetime SAVI (15 days interval)

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6.6 Assign Status

The threshold parameters i.e., min, mean, and

percentile (75) (Table 1) are used to assign a status,

"no cut yet", "in progress", and "finish" in the final

step. Then, status information is sending to the GI

System for utilization in the final step.

7 CONCLUSIONS

This paper presents a proof of concept of using

Sentinel-2 data in the harvest monitoring of

sugarcane fields. SAVI can be used as a cutting

indicator in harvest period of farm plants. In rainfed

agriculture such as Thailand, recommended the

minimum of 15 days composite for cloud-free image

analysis. Geostatistics is a powerful tool for

interpretation in various ways to identify the human

activities which perform on farmland as

demonstrated in this paper.

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