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GeoPlanning 
  Journal of Geomatics and Planning                                                                                                                   Vol. 8, No. 2, 2021     
 

Original Research 

The Spatial Model of Paddy Productivity Based 

on Environmental Vulnerability in Each Phase 

of Paddy Planting 

Rahmatia Susanti 1,2, Supriatna 1, Rokhmatuloh 1, Masita D. M. Mannesa 
1*, Aris Poniman 1, Yoniar H. Ramadhani 2 

1. Department of Geography, Faculty of Mathematics and Natural Sciences, Indonesia University, 

Indonesia 

2. Geospatial Information Agency, Indonesia 

DOI: 10.14710/geoplanning.8.2.127-136  

Abstract 

The national primary always growth and increase in line with the increase in population, such as the rise of rice consumption 

in Indonesia.  Paddy productivity influenced by the physical condition of the land and the declining of those factors can 

detected from the environmental vulnerability parameters. Purpose of this study was to compile a spatial model of paddy 

productivity based on environmental vulnerability in each planting phase using the remote sensing and GIS technology 

approaches. This spatial model is compiled based on the results of the application of two models, namely spatial model of 

paddy planting phase and paddy productivity. The spatial model of paddy planting phase obtained from the analysis of 

vegetation index from Sentinel-2A imagery using the random forest classification model. The variables for building the 

spatial model of the paddy planting phase are a combination of NDVI vegetation index, EVI, SAVI, NDWI, and time 

variables. The overall accuracy of the paddy planting phase model is 0.92 which divides the paddy planting phase into the 

initial phase of planting, vegetative phase, generative phase, and fallow phase. The paddy productivity model obtained from 

environmental vulnerability analysis with GIS using the linear regression method. The variables used are environmental 

vulnerability variables which consist of hazards from floods, droughts, landslides, and rainfall. Estimation of paddy 

productivity based on the influence of environmental vulnerability has the best accuracy done at the vegetative phase of 

0.63 and the generative phase of 0.61 while in the initial phase of planting cannot be used because it has a weak relationship 

with an accuracy of 0.35. 

Copyright © 2021 GJGP-Undip 

This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license 

1. Introduction 

Paddy is food crops that are the staple food for most people in Indonesia, so the amount of paddy 

production will significantly affect food needs in Indonesia. Physical characteristics of land will have an influence 

on paddy productivity in a region (Widiatmaka et al., 2016). Declining land physical factors can detected from 

environmental vulnerabilities that can cause changes in paddy productivity. Environmental vulnerability 

according to  Regulation of the Head of the National Disaster Management Agency Number 2 of 2012 compiled 

based on natural disaster hazard class or disaster-prone class factors with land use parameters. Compiled based 

on natural disaster hazard class or disaster-prone class factors with land use parameters. Bogor Regency is one 

area in Indonesia that has environmental vulnerability to natural disasters and a high paddy-producing.  

Bogor Regency is known as one of the largest paddy producers in West Java Province but based on BPS 

data it was detected a decrease in paddy productivity by 4 tons/hectare during 2016-2017 period. The decline in 

paddy productivity in Bogor Regency influenced by the location of paddy fields in areas prone to natural 

e-ISSN: 2355-6544 
 
Received: 14 January 2021;  
Accepted: 1 September 2021;  
Published: 30 December 2021. 
 
Keywords:  
Environmental Vulnerability, 
Paddy Productivity, Paddy 
Planting Phase, Random Forest 
Classification Model, Regression 
Model, Sentinel-2A 
 
*Corresponding author(s) email: 
manessa@ui.ac.id  
 
 

 

https://doi.org/10.14710/geoplanning.8.2.127-136
mailto:manessa@ui.ac.id


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128 

disasters, like floods, droughts, and landslides. This study focuses on aspects of environmental vulnerability 

based on natural hazard-prone conditions that can affect paddy productivity in each phase of paddy planting in 

Bogor Regency, West Java. Remote sensing technology and geographic information systems can detect the 

paddy planting phase quickly, accurately and analyze environmental vulnerabilities to natural disasters.  

The use of remote sensing technology is widely use in the analysis of agricultural land, especially for 

continuous monitoring of agricultural land. Remote sensing has great potential in monitoring paddy phenology 

for management and prediction of paddy production (He et al., 2018).  Utilization of Sentinel-2A imagery is 

appropriate for mapping large-scale paddy fields because it has high spatial and temporal resolution than other 

remote sensing images such as Landsat 8 which has a spatial resolution of 30 meters with 16 daily temporal 

resolution (Dong et al., 2016). Another advantage of this satellite image is that 3 (three) bands ‘red edge’ are 

suitable for identification of vegetation (Liu et al., 2018). 

The use of GIS technology can be used for spatial modeling in monitoring the paddy planting phase and 

estimation of paddy productivity based on environmental vulnerability factors. Spatial modeling with GIS can 

produce an algorithm that can be used to estimate paddy productivity in each area of paddy fields (Muslim et al., 

2015). The purpose of this study was the preparation of a spatial model estimation of paddy productivity based 

on the influence of environmental vulnerability in each phase of paddy planting. This spatial model is expected 

to be input in estimating the decline in paddy productivity due to environmental vulnerability in a region in each 

phase of paddy planting so that prevention or mitigation steps can be more quickly carried out. 

 

2. Data and Methods 

This research divided into several stages of work. In the first stage is the preparation stage to determine 

theme and choose the title of the research to be conducted, after getting the title of research then proceed with 

the study of literature by looking for a literature review related to this research. The next stage is to collect 

secondary data, as well as download Satellite Sentinel-2A imagery.  

The stages of initial data processing divided into two, namely spatial data processing which processes data 

on environmental vulnerability variables and processing vegetation indices using Sentinel-2A imagery. The 

initial data processing stage is to process environmental vulnerability variable data, which is analyzing secondary 

data consisting of drought-prone maps, flood hazard maps, and landslide-prone maps, and conducting spatial 

analysis of average monthly rainfall to obtain a monthly rainfall map. The next stage is the rasterization and 

classification of flood-prone maps, prone to drought, landslide-prone, and average monthly rainfall, according to 

the provisions of each theme. Classification of the average monthly rainfall maps using the Oldeman category. 

Classification of flood-prone based SNI 8197: 2015, classification prone to landslides based on SNI 8291: 2016, 

and classification of drought-prone uses Regulation of the Head of the National Disaster Management Agency 

Number 2 of 2012.  

The next initial data processing stage is processing remote sensing data in the form of an analysis of the 

vegetation index. The vegetation index used is NDVI, SAVI, EVI, and NDWI. NDVI analysis can highlight 

aspects of vegetation density  and use Near Infrared (NIR) and Red Bands (Tucker, 1986 in Danoedoro, 2012), 

as follows 

 

 

 

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129 

The EVI index is sensitive to changes in biomass and is resistant to canopy effects. The EVI index can 

reduce atmospheric influence because it uses the blue band in calculations that can correct aerosol interference 

in the red  and blue band (Huete et al., 2002), the equation is as follows 

 

This index emphasizes the background of the soil by minimizing the effect of soil brightness using soil 

correction factors (Huete, 1988). 

 

This index uses near-infrared (NIR) and shortwave infrared (SWIR) bands which can detect moisture 

content which is useful for vegetation studies (Gao, 1996). 

 

The results of the vegetation index analysis are used to estimate the paddy planting phase which idivided 

into four classifications, namely the initial planting phase (water dominance), vegetative phase, generative phase 

and fallow phase (LAPAN, 2015).  

The field survey phase was conducted to obtain data related to the paddy planting phase and productivity 

at each paddy planting period. This field sample was used as a training sample and a sample validation of the 

spatial model of paddy planting phase and spatial model the influence of environmental vulnerability on paddy 

productivity. Besides, a field survey was conducted to check the physical environmental conditions in terms of 

areas that have flood-prone areas, are prone to drought, and prone to landslides that will affect paddy 

productivity. 

The last stage is divided into two, namely making the spatial model of the paddy planting phase and spatial 

model the influence of environmental vulnerability on paddy productivity. Making a spatial phase of paddy 

planting model using the random forest classification model. Making a spatial model the influence of 

environmental vulnerability on paddy productivity using a linear regression model. The two results of the model 

will then be applied to Sentinel-2A remote sensing data to see the distribution of estimated paddy productivity 

in Jonggol, Cariu, Sukamakmur, and Tanjungsari sub-districts. 

 

3. Result and Discussion  

3.1. Spatial Model of Paddy Planting Phase 

The compilation of the paddy planting phase spatial model uses the random forest classification method. 

The random forest classification method approach can used for paddy classification by extending the data 

dimensions in the spectral and temporal domains that influence the characteristics of paddy that has different 

climate and environmental characteristics (Park et al., 2018).  The training features used in this study were 3649 

sample points spread over 138 locations with 33 periods (March 2017 – May 2018). Modeling uses 5 (five) 

variables, namely the NDVI vegetation index, EVI, SAVI, and NDWI as well as the recording phase of the 

planting phase. The processing results of random forest classification obtained the percentage of interest from 

the variables used to build the spatial model. NDWI index has the highest percentage of interest of 26%, NDVI 

index of 23%, SAVI index of 23%, EVI index of 22% and variable that has the smallest percentage of interest is 

planting time recording variable which is only 6%.  

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130 

The application of random forest classification in mapping paddy fields based on the planting phase has 

outstanding accuracy of 0.96 using the NDVI vegetation index variable and time variable (Onojeghuo et al., 

2017). The overall accuracy results in this study were 0.92. The highest accuracy is found in the water phase of 

1.00, because in this phase water-dominated paddy fields have index values that are different from other planting 

phases, both in the NDVI index, EVI, SAVI, and NDWI. The vegetative phase has an accuracy of 0.90, the 

generative phase has an accuracy of 0.88, and the fallow phase has an accuracy of 0.91. 

 

 

Figure 1. Vegetation index graph in a paddy planting phase period 

(Data processing, 2019) 

 

Spatial modeling of paddy planting phase using four combinations of vegetation index and time variables 

can be simplified by using two combinations of vegetation indexes which have different vegetation index value 

patterns in the planting phase period (Figure 1). The vegetation index pattern in the planting phase period can 

seen that the NDVI vegetation index, EVI, and SAVI have almost the same pattern, while the NDWI vegetation 

index is different so that the combination that can be used is the NDWI vegetation index with other vegetation 

indices. The results of the comparison of accuracy in the paddy planting phase model using a combination of two 

vegetation indices are shown in Table 1. 

 

Table 1. Comparison of accuracy of models with a combination of vegetation index (Data processing, 2019) 

Vegetation Index 

Combination 

Accuracy 

Initial Phase Vegetative phase Generative Phase Fallow phase 

NDWI-NDVI-EVI-SAVI 1,00 0,90 0,88 0,91 

NDWI – NDVI 0,99 0,93 0,90 0,91 

NDWI – EVI 0,93 0,87 0,86 0,91 

NDWI - SAVI 1,00 0,92 0,90 0,94 

 

3.2. Paddy Productivity Model based on Environmental Vulnerability 

Variables of environmental vulnerability are made based on natural hazard-prone conditions that often 

occur in the study area. Dinas Tanaman Pangan dan Holtikultura Kabupaten Bogor in 2017 noted that the prone 

to natural disasters that occur on paddy fields in Bogor Regency is prone to droughts (Figure 2), floods (Figure 

3), and landslides (Figure 4).  

0

1

2

V
EG

ET
A

TI
O

N
 IN

D
EX

 V
A

LU
E 

PADDY PLANTING PHASE PERIOD (DAY)

Vegetation Index  in a Paddy Planting 
Phase Period

EVI SAVI NDWI NDVI

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131 

 

Figure 2. Drought-prone map of Jonggol, Cariu, Sukamakmur and Tanjungsari Subdistricts 

(National Disaster Management Agency, 2017) 

 

Figure 3. Flood-prone map of Jonggol, Cariu, Sukamakmur and Tanjungsari Subdistricts 

(Geospatial Information Agency, 2017) 

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Figure 4. Landslide-prone map of Jonggol, Cariu, Sukamakmur and Tanjungsari Subdistricts 

(Ministry of Energy and Mineral Resources, 2017) 

Rainfall variables are also used in the preparation of the paddy productivity model because linear 

regression models are formed based on temporal data, while secondary data in the form of natural disaster-prone 

maps issued by data custodians only at maximum prone or only one map in one year. Spatial analysis of rainfall 

is used to determine the distribution of rainfall distribution in each month so that it can determine the effect of 

rainfall with the planting phase time and paddy productivity. The growth of paddy plants is very dependent on 

water sources, one of which comes from rainfall that is used to meet the needs of plants for water and good water 

management is needed for irrigating paddy farms (Thakur et al., 2013). Rainfall analysis based on Oldeman 

classification shows that Jonggol, Tanjungsari, Cariu, and Sukamakmur sub-districts only have dry months and 

humid months (Figure 5). Dry months are from May to October, at that time the condition of paddy fields in the 

condition of the final generative phase is at the time of cooking until the condition of the fallow phase or not 

during the paddy planting period. Humid months are in the period from November to April, at that time is the 

initial phase of planting in November and March. In the humid months it can be produced two times the paddy 

planting period.  

 

Figure 5. Average monthly rainfall patterns in Jonggol, Cariu, Sukamakmur and Tanjungsari Subdistricts 

(Data processing, 2019) 

0

50

100

150

A
V

ER
A

G
E 

M
O

N
TH

LY
 R

A
IN

FA
LL

 
(M

M
)

MONTH

Cariu Jonggol Sukamakmur Tanjungsari

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The method used in making a model of paddy productivity is a linear regression model with the OLS 

method or the least-squares method. The OLS method will produce an estimator that is unbiased, linear and has 

a minimum variance (best linear unbiased estimators - BLUE) (Frost, 2018). The results of the linear regression 

model in the form of an algorithm that can be used to estimate paddy productivity based on the conditions of the 

paddy planting phase. The results of the spatial model in the form of an effective algorithm, one of which is paddy 

rice planting index (PRPI), which is used to map rice paddies nationally in China (Song et al., 2018). The 

algorithm produced in this study is as follows.  

a. Paddy productivity in the initial phase of planting = 66.40 – (1.033 * rainfall) – (0.204 * prone to 

landslides) – (0.430 * prone to drought) – (0.486 * prone to floods) 

b. Paddy productivity in the vegetative phase =  69.46 – (1.59 * rainfall) – (0.35 * prone to landslides) – (1.33 

* prone to drought) – (0.50 * prone to floods) 

c. Paddy productivity in the generative phase =  68.51 + (0.73 * rainfall) – (0.69 * prone to landslides) – 

(2.15 * prone to drought) – (0.62 * prone to floods) 

The influence of environmental vulnerability and rainfall in the vegetative phase has an inverse 

relationship, which decreases the level of flood-prone, prone to landslides, prone to drought and rainfall will 

result in higher paddy productivity. The increase in minimum temperature, rainfall, and relative humidity has a 

positive impact on paddy productivity even though it is not significant (Grover & Upadhya, 2014). In the 

vegetative and generative phase, drought-prone variables have a large influence on the estimation of paddy 

productivity, so that if there is an increase in prone drought it will cause a decrease in paddy productivity. Rainfall 

variables also have a large influence on productivity predictions, but in the vegetative phase there is no need for 

high rainfall or in the dry to moist months.  The comparison of the relationship of environtmental vulnerability 

to paddy productivity in each paddy planting phase can be seen in Table 2. 

Table 2. Comparison of the relationship of environmental vulnerability to paddy productivity in each paddy 

planting phase (Data processing, 2019) 

No Planting Phase Variable Relationship to Paddy Productivity Adjusted r2 

1 Initial Planting Phase 

Rainfall Negative 

0.35 
Prone to flooding Negative 

Drought-prone Negative 
Prone to landslides Negative 

2 Vegetative phase 

Rainfall Negative 

0.63 
Prone to flooding Negative 

Drought-prone Negative 
Prone to landslides Negative 

3 Generative Phase 

Rainfall Negative 

0.61 
Prone to flooding Positive 
Drought-prone Negative 

Prone to landslides Negative 

 

The adjusted r2 results in the vegetative phase and the generative phase has a strong enough relationship 

to predict paddy productivity using the paddy planting phase classification model (McLean et al., 1980), while 

the initial phase of planting has a weak or cannot be used to predict paddy productivity. 

3.3. Spatial Model of Paddy Productivity based on Environmental Vulnerability in Each Paddy Planting 
Phase 

The Spatial Model of Paddy Productivity based on Environmental Vulnerability in Each Paddy Planting 

Phase is a combination of paddy planting phase classification models and linear regression models of paddy 

productivity so that it can be used to estimate paddy productivity spatially. The application of the two models 

was carried out on Sentinel 2A images with Recording Date 31 in 2017. The results of the paddy planting phase 

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prediction produced according to the planting calendar issued by UPT Jonggol and UPT Cariu. Planting phase 

based on cropping calendar shows that paddy plants at the end of December 2017 are in the vegetative phase 

condition for Jonggol, Sukamakmur, Cariu and Tanjungsari sub-districts. The results of the spatial modeling of 

the paddy planting phase on December 31, 2017, were dominated by the vegetative phase in all sub-districts 

(Figure 6).  

 

Figure 6. Planting phase distribution map December 31, 2017  

(Data processing, 2019) 

The prediction results of the distribution of the paddy planting phase based on the random forest 

classification model need to be validated again to determine the product accuracy of the paddy planting phase 

distribution map on December 31, 2017. Validation is done by testing the accuracy using the Confusion Matrix 

method. The data used in the accuracy test is the planting phase data from the prediction of the random forest 

classification model which is used as primary data which then compared with the planting phase data resulting 

from field data processing. The results of the test using the Confession Matrix method are of the overall accuracy 

of 87.5% in Sentinel-2A processing images on December 31, 2017. The distribution map of the paddy planting 

phase on December 31, 2017 is acceptable because the overall accuracy is greater than 85%, which according to 

the United States Geological Survey (USGS) that the level of accuracy of classification or minimum 

interpretation using remote sensing data is 85%.  

The results of the distribution of the paddy planting phase then calculated for estimating the paddy 

productivity algorithm. The highest estimated productivity of paddy or greater productivity of 6.17 tons/ha 

found in Mekarwangi Village, Cariu District. The results of predictions of paddy productivity from both spatial 

models illustrate that paddy productivity in a region is very influential on the geographical and climate 

conditions of a region. The geographical conditions of research with varied topography provide different 

environmental vulnerabilities. This environmental vulnerability illustrates environmental degradation that will 

affect paddy productivity.  

Appropriate use of paddy plantations and not in areas prone to natural disasters will provide higher paddy 

productivity. Jonggol and Cariu sub-districts which have relatively flat topography and have a lot of river flow, 

making the potential of irrigation in these two sub-districts easier. Whereas in Tanjungsari and Sukamakmur 

Subdistricts faced with limiting factors such as the relatively high topography conditions, the slope is relatively 

steep so that it has relatively high landslide vulnerability and poor agricultural irrigation factors which are highly 

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dependent on rainfall which causes lower paddy productivity. Beside geographical conditions, climate conditions 

also widely affect paddy productivity, especially in paddy fields, whose primary source of irrigation comes from 

rainfall. This can be seen from the results of the paddy productivity model (Figure 7) that produced that the 

variables prone to drought and rainfall are the variables that have the highest influence on the estimation of 

paddy productivity and have a direct impact on paddy crops.  

 

 

Figure 7. Map of estimated productivity of paddy in December 2017  

(Data processing, 2019) 

4. Conclusion 

The spatial model of the paddy planting phase with a beneficial random forest classification method is used 

to see the distribution of the paddy planting phase in an area. The factors used to estimate the planting phase, 

namely the combination of vegetation indexes consisting of NDVI, EVI, SAVI, and NDWI are mutually 

temporal with overall accuracy of the model of 92 %. The spatial model of the influence of environmental 

vulnerability on paddy productivity which consists of variables prone to flooding, prone to drought, prone to 

landslides and rainfall in each phase of paddy planting can use as a tool to estimate paddy productivity.  The 

difference in the influence of environmental vulnerability in productivity estimation occurs in each phase of paddy 

planting. Estimation of paddy productivity by spatial modeling influence of environmental vulnerability can be 

detected in the vegetative and generative phases because it produces a relatively stable accuracy of 0.63 in the 

vegetative phase and 0.61 in the generative phase. Whereas the initial phase of planting cannot use in estimating 

paddy productivity because it has a weak accuracy of 0.35. The highest environmental vulnerability variables 

that affect paddy productivity are prone to drought and rainfall. 

 

5. Acknowledgements 

This research was funded by Universitas Indonesia under research grant PUTI Q3 2020 with grant 

contact number NKB-4492/UN2.RST/HKP.05.00/2020 

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