We compared our MODIS-derived rice area to the official provincial rice areas from the Bureau of Agricultural Statistics (BAS). Results showed high correlations: Luzon=0.94, Visayas=0.70, and Mindanao=0.83, and 0.87 for the whole country (Table 1 and Figure 2). These were higher than the 0.77 correlation achieved by Xiao et al.

We collected 1,350 ground truth data points in rice (831 points) and nonrice (519 points) areas to assess the accuracy of the classification. The overall accuracy was high, 83% with a Kappa coefficient of 0.62 (Table 2).

A rice area map for 2009 was generated from the analysis (Fig. 3). Based on the two accuracy assessments above, we believe that the map is very accurate for Luzon, accurate for Mindanao, but could be improved in the Visayas where smaller rainfed rice systems – which are more difficult to detect – are more dominant.

Monthly rice area maps were also generated to show the transition in rice paddies from fallow to flooded to established rice crop throughout the year (Fig. 4).

An analysis of MODIS data for 2001-2009 demonstrates how flooding/transplanting and harvesting dates can be derived from the time series (Fig. 5).

Fig 1. Schematic diagram of our methodology for mapping rice areas.

Rice is the staple food for about 94 million Filipinos and is cultivated in almost all provinces. Information on the exact location, extent, and crop yield in those areas are important for developing location-specific technologies that address yield-limiting factors. At present, information on rice area location for the entire Philippines can only be derived from the 1980’s paper map of the Bureau of Soils and Water Management with a scale of 1:250,000.

Rice in the Philippines

The automated rice mapping system

Preliminary results

Automated mapping of rice paddies using multi-temporal MODIS images and R

“R” is a free software environment for statistical computing and graphics [3]. Automated processing of the 46 eight-day composite MODIS images for year

2009 is done using the software we developed for the “R” environment. “R” is a powerful tool for processing a large volume of satellite data. IRRI GIS Lab-developed R packages: RemoteSensing [4] and raster [5]. With RemoteSensing and raster, we can generate several rice-related maps:

rice area, cropping intensity, cropping pattern, and dates of flooding/transplanting and harvesting.

R software and IRRI’s packages

Sonia Asilo1, Andrew Nelson1, Jorrel Khalil Aunario1, Alice Laborte1, Yann Chemin2 and Robert Hijmans3

1 International Rice Research Institute, Los Baños, Laguna, Philippines; 2 IWMI, Colombo, Sri Lanka; 3 University of California, Davis, USA

Conclusions and further work The high temporal resolution of MODIS and the moderate spatial resolution make

it possible to map and monitor rice areas at high accuracy, even in tropical countries.

Information on cropping calendar, cropping pattern, and cropping intensity can be extracted from MODIS temporal signatures.

Local knowledge on rice areas, crop calendar, rice ecosystem, and cropping intensity is very important for calibrating and validating MODIS-derived maps.

Cloud cover is pervasive in the tropics and makes rice mapping challenging, especially in the wet season. Temporal interpolation techniques are required to fill these gaps.

Though the software we have developed and used for rice mapping is free, it is necessary to have a computer with high processing capability for the multi-temporal MODIS data.

We are continuously working on refining our algorithm to further improve the accuracy of the rice area map.

When fully validated, IRRI will make this automated rice mapping system accessible to national agricultural research and extension systems to further improve their rice-monitoring programs.

a Moderate Resolution Imaging Spectroradiometer (MODIS).b Normalized Difference Vegetation Index (NDVI): gives information on the presence or absence of vegetation.c Enhanced Vegetation Index (EVI): Enhancement of vegetation index to better account for soil and atmospheric aerosol effects. d Land Surface Water Index (LSWI): High values indicate high liquid water content either from soil or rice.

Acknowledgement: We thank NASA for the free MODIS images and PhilRice and BAS for the rice area statistics data.

We have developed an automated system using multi-temporal MODISa satellite images that improves upon the methodology developed by Xiao et al. [1] for mapping rice paddies (Fig. 1). Composite MODIS images are free, available in near real time every 8 days [2] and have a resolution of 25 ha.

Fig. 3. Rice area map for 2009 derived from the analysis of 46 MODIS satellite images.

Table 1. Physical rice area from BAS vs MODIS-derived rice areas (2009).

LocationPhysical rice area (000 ha)

MODIS rice area (000 ha)

Luzon 1,528 1,558

Visayas 644 530

Mindanao 622 450

Class

Ground truth (pixels)

Nonrice Rice Total

Nonrice 347 172 519

Rice 62 769 831

Total 409 941 1350

Table 2. Agreement between ground truth points and the MODIS classification.

References1. Xiao, X., Boles, S., Frolking, S., Li, C., Babu, J.Y., Salas, W., and Moore, B. III. 2006. Mapping paddy rice

agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environ. 100:95-113.

2. ftp://e4ftl01u.ecs.nasa.gov/MOLT/MOD09A1.005/.

3. http://cran.r-project.org/.

4. Chemin, Y., Laborte, A., Asilo, S. and Hijmans, R. 2010. RemoteSensing: Remote Sensing. R package version 0.3-3. https://r-forge.r-project.org/projects/remotesensing/.

5. Hijmans, R., and van Etten, J. 2011. raster: Geographic analysis and modeling with raster data. R package version 1.7-46. http://CRAN.R-project.org/package=raster.

Fig. 4. Monthly transition of rice fields in 2009. Flooded fields in blue, established rice crop in green.

DEC

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

MO

DIS

-der

ived

Fig. 2. Correlation between MODIS-derived rice areas and provincial physical rice area from BAS.

Luzon

Mindanao

Visayas

Linear (Philippines)

MODIS 8-day composites of surface reflectance product (MOD09A1)MODIS 8-day composites of surface reflectance product (MOD09A1)

Vegetation & water index calculation

• calculate indices (NDVIb,EVIc,LSWId) that give information on land surface phenology

Cleaning• remove clouds, cloud shadows • remove permanent water• remove pixels with missing values

Accuracy assessmentGoogle EarthCompare with rice statistics

Rice detection• classify pixels as rice or non-rice based on thresholds derived from NDVI, EVI and LSWI• mask urban and high elevation/slope area

Final map of paddy rice field

Fig. 5. Different temporal signatures of rice derived from 2001-2009 average of EVI and LSWI. A is the temporal signature of nonrice-rice cropping pattern, B is rice-rice, and C is fallow-rice.

A

Nampicuan, Nueva Ecija (Long=120.67, Lat=15.70)

Alaminos, Pangasinan (Long=119.98,Lat= 16.09)

C

Burgos, Isabela (Long=121.72, Lat=16.95)

B

Temporal Signature ID1: 792 - Nampicuan Nueva Ecija, 324 – Burgos Isabela, 685 – Alaminos Pangasinan Advantage of MODIS over RADAR: it is free, we have an 10 year archive of data (2000 Feb- 2010), it has high temporal resolution You need 4 tiles to cover the whole Philippines With the 10-yr data archived, we will get a much better picture in the Visayas (which the least accurate in our map). We can remove some noise in the Visayas using this 10-yr data.Implications: We can use this for intensifying rice areas planted with rice only once per year. Since we can get information on cropping intensity, we can find areas that are just planted once a year and recommend those areas for improvement of irrigation.This rice area map cannot tell us potential areas for rice cultivation. It can only tell you where rice is planted and the current rice areas.Advantage of MODIS over RADAR: Processing of radar images is complicated compared with MODIS (simple).MODIS captures the past and recent events that occurred in rice areas with its 10-yr data.It can give you a picture of what happened in the past and what is happening now in rice areas.Kappa coefficient = it removes the agreement due to chance.Explanation of Graph: We compared rice area estimates derived from BAS and MODIS. Each point represents once province. You will see that there is high correspondence/correlation.

SCRIPT:

• Rice is a very important crop in the Philippines• About 94M Filipinos eat rice 2-3 times daily.• Information on the exact location of rice areas and how it is grown are very important in developing location specific technologies that address yield-limiting factors.•At present, we can only derive this information from a 1980’s rice-ecosystems map from BSWM. Some rice areas might have been converted into residential areas already.• To generate an updated rice area map, not only in the Philippines but also for the rest of ASIA, IRRI developed an automated rice mapping system using MODIS that improves upon the methodology developed by Dr. Xiao.•We are using free MODIS satellite images and an open source software called R for processing large volume of satellite data.•The GIS Laboratory at IRRI developed packages for R that could generate several rice maps and rice-related information such as cropping intensity, cropping pattern, and dates of flooding and harvesting.•We compared the rice areas derived from MODIS with the official provincial rice areas from BAS. Each point represents one province. We got high correlations for Luzon=0.94, Visayas=0.70, and Mindanao 0.83 and 0.87 for the whole country.•If you look at this graph, you will see high correlations. The red line indicates the linear regression line which is below the 1:1 line indicating a little bit of underestimation. You can verify this by lookingat the figures in this table.•We conducted another accuracy assessment. We collected points from the ground and from google earth. We got a high overall accuracy of 0.83 percent.

•TEMPORAL SIGNATURE:Figure 5A. NAMPICUAN, NUEVA ECIJA - Flooding/transplanting: DOY 216, from July 28-Aug 4

Reproductive(Flowering): DOY 264, from Sep 14 – 21Harvesting: DOY 304, from Oct 24 – Oct 31

Cropping pattern: nonrice-rice Cropping intensity: 1 per year planting of rice

Figure 5B. BURGOS, ISABELA - Flooding/transplanting: DOY 184, from June 26 – July 3 / DOY 344, from Dec 3 – 10

Reproductive(Flowering): DOY 48, from feb 12-19 / DOY 240, from August 21-28 Harvesting: DOY 80, from Mar 14-21 / DOY 264, from Sep 16-23 Cropping pattern: rice-rice Cropping intensity: 2 per year planting of rice

Figure 5C. ALAMINOS, PANGASINAN - Flooding/transplanting: DOY 192, from July 4 – July 13 Reproductive(flowering): DOY 248, from Aug 29 – Sep 5 Harvesting: DOY 296, from Oct 16-23 Cropping pattern: fallow-rice Cropping intensity: 1 per year planting of rice