MAPPING RICE AREAS IN MINDANAO USING THE FIRST IMAGES FROM SENTINEL-1A: THE PRISM PROJECT EXPERIENCE

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1 MAPPING RICE AREAS IN MINDANAO USING THE FIRST IMAGES FROM SENTINEL-1A: THE PRISM PROJECT EXPERIENCE Jeny Raviz 1, Mary Rose Mabalay 2, Alice Laborte 1, Andrew Nelson 1, Francesco Holecz 3, Eduardo Jimmy Quilang 2, Massimo Barbieri 3, Jovino de Dios 2, Francesco Collivignarelli 3, Luca Gatti 3, Arnel Rala 1, Cornelia Garcia 1, Pristine Mabalot 2, Jean Rochielle Mirandilla 2, Marjie Doverte 2 1 International Rice Research Institute, Los Baños, Laguna, Philippines, J.Raviz@irri.org 2 Philippine Rice Research Institute, Muñoz, Nueva Ecija, Philippines, mro.mabalay@philrice.gov.ph 3 sarmap, Purasca, Switzerland fholecz@sarmap.ch KEYWORDS: rice mapping, Sentinel-1A, SAR, Philippines ABSTRACT: The Philippine Rice Information System (PRISM) project aims to develop a monitoring and information system for rice production in the Philippines using data from remote sensing, crop models and in-field crop surveys. PRISM predominantly uses multi-temporal Synthetic Aperture Radar (SAR) imagery to monitor rice growing regions in the Philippines. This study is a first attempt to use the first images of Sentinel-1A, a radar imaging satellite, for mapping rice areas. Sentinel-1A Interferometric Wide Swath mode (IWS) 20 m resolution images acquired from October 2014 to May 2015 which coincides with the dry season rice cropping in the island of Mindanao in southern Philippines were used for mapping. This C-band time series SAR data were processed using a dedicated semi-automated processing chain including a knowledge-based rice detection algorithm implemented in MAPscape-RICE, an image processing software developed specifically for rice. A total of 48 images from six footprints covering 98% of the land area in Mindanao were used for rice mapping. Over 381,000 hectares of rice area were mapped and the overall accuracy in one region based on 159 ground observations was 89% (kappa = 0.79). Planting months ranged from October to March with the peaks in either November or January depending on the region. The usefulness of Sentinel-1A and other SAR data sources, and the challenges in developing a SAR-based monitoring system at national scale are discussed. 1. INTRODUCTION Rice is an important food crop in Asia, where about 90% of the global rice output is produced and consumed (FAO, 2000). Like in most Asian countries, rice is a key crop for food security in the Philippines and accounts for nearly half of the caloric intake of Filipinos (DA, 2012). Thus, timely and accurate assessment of crop conditions such as planted area, growth status, yield and impact of adverse climatic conditions such as flood and drought can provide valuable information for the government, for planners and for decision makers in formulating policies and targeting interventions. The Philippine Rice Information System (PRISM) project aims to develop an operational online system that would consolidate and present timely and accurate information on the status of the rice crop in the country. PRISM is a four-year collaborative project among the Department of Agriculture (DA) Philippine Rice Research Institute (PhilRice), International Rice Research Institute (IRRI), DA-Bureau of Plant Industry (BPI) and DA Regional Field Offices (RFO s). PRISM is also collaborating with partners from developed countries: sarmap (Switzerland) and the University of Milan (Italy) for the development of software and mobile phone applications used in the project. PRISM relies on data from remote sensing, crop models, and in- field crop surveys to deliver timely and actionable information on rice crop seasonality, area, yield, crop health and damages. The main satellite imagery used in rice mapping is Synthetic Aperture Radar (SAR) which has been shown to effectively map rice areas in the tropics where cloud cover is pervasive particularly during the monsoon season (Le Toan et al., 1997; Bouvet et al., 2009; Nelson et al., 2014). PRISM also uses optical imagery to complement the SAR image acquisitions and provide information on the status of the rice crop and the risk of damage posed by tropical storms (Boschetti et al., 2015). In 2014, during its first year of operation, PRISM mapped and monitored sites in seven administrative regions namely: Cordillera Administrative Region (CAR), Regions III (Central Luzon), IVB (MIMAROPA), V (Bicol), VI (Western Visayas), VII (Central Visayas), and VIII (Eastern Visayas). X-band SAR Single Look Complex (SLC) data acquired from COSMO-SkyMed Stripmap 3 m resolution from the Italian Space Agency (ASI/e-GEOS) for

2 Region VIII and TerraSAR-X (TSX) ScanSAR images 10 m resolution from InfoTerra GmbH for the other six regions were used to map rice areas during the 2014 wet season with an overall accuracy of 86% (Laborte et al., 2015). Starting in the 2015 wet season, PRISM expanded its coverage to include all rice growing regions of the country (16 in total). The main source of SAR data for all 16 regions is still TSX acquired every 11 days. In one crop season, 8 to 10 images are acquired to accurately map rice areas and estimate yield. However, this source of X-band SAR data is costly and careful planning is needed to make sure that the acquisitions cover the land preparation stage which is crucial for detecting rice areas and crop growth until the pre-flowering stage for more accurate mapping of rice areas and yield estimation. Also, because of the cost of acquiring the images, not all regions can be completely covered by SAR images. With the launch of the Sentinel-1A satellite by the European Space Agency in April 2014, there is a new source of SAR imagery that is freely available, allowing full regional coverage which makes this ideal for regular rice mapping and monitoring alongside images from commercial providers. This paper describes the methodology and initial rice area and start of season maps for Mindanao island for the dry season (DS). 2. DATA AND METHODS 2.1 Data Study area Mindanao, located in southern Philippines, is one of the three main island groups in the country (Fig.1). It is home to nearly 22 million people, almost a quarter of the total population of the Philippines (NSO, 2010). It consists of six administrative regions: Autonomous Region of Muslim Mindanao (ARMM), Region IX (Zamboanga Peninsula), Region X (Northern Mindanao), Region XI (Davao), Region XII (SOCCKSARGEN) and Region XIII (Caraga). One-third of Mindanao s land area is devoted to agriculture. It is considered the country s food basket, supplying over 40% of the country s food requirements and contributing more than 30% to national food trade (NEDA 2010). In 2015 DS, a total of 514,712 ha were planted to rice in Mindanao and 25% of total rice production in the country was produced in Mindanao with Region XII accounting for 6% (PSA, 2015). In addition to rice, Mindanao is also the country s major producer of rubber, pineapple, banana and coffee. Most of its regions are located outside the typhoon belt and significant crop damage from tropical storms is rare. Mindano has eight major river basins, namely: Agusan, Tagoloan, Cagayan de Oro, Tagum Libuganon, Davao, Buayan-Malungon, Agus and the Mindanao River all of which supply water for irrigation and other related needs (NEDA 2010). Satellite data Multi-temporal C-band SAR data were obtained from the European Space Agency (ESA): Sentinel-1A Copernicus data (2015). We used a total of 48 Sentinel-1A images which covered 98% of Mindanao s total land area acquired from October 2014 to May 2015 to cover the dry season rice from land preparation to maturity. Table 1 shows the details of the SAR imagery used in mapping and Figure 1 shows the footprint. Field data Field work was conducted on March 9-13, 2015, when the rice crop was between tillering stage and maturity in Region XII, to assess the accuracy of the rice maps. For this initial assessment, the whole island was not visited due to security considerations and time limitations. Validation points included were for those land cover where SAR signature changes within the season such as rice and other annual crops. Grasslands on low lying areas are also included in field validation because grasslands could be misclassified as rice. Locations were chosen such that the land cover was homogenous within the surrounding 1 ha area, and locations were at least 50 m away from roads, built up areas and other infrastructures. Aside from location, photos were also taken and the land cover in the area was described. A total of 159 validation points (86 rice and 73 nonrice) were collected in Region XII for use in accuracy assessment.

3 Figure 1. Footprints of Sentinel-1A images acquired over Mindanao island during the DS. Inset map shows location of Mindanao in the Philippines. The background used is a DEM from SRTM 90m wherein gray indicates flat areas and white for areas with high elevation. Table 1. Characteristics of Sentinel-1A data used in rice mapping. Characteristics Value Mode Interferometric Wide swath Product type Ground Range Detected (GRD) Processing level Level-1 Pass Ascending Polarization VV Swath width 250 km Resolution 20 m Incidence angle at scene center 40 Relative orbit No of frames Repeat cycle (days) Dates of acquisition 03-Oct 10-Oct 22-Nov 27-Oct 03-Nov 16-Dec 14-Dec 27-Nov 09-Jan 07-Jan 21-Dec 26-Feb 31-Jan 14-Jan 22-Mar 24-Feb 07-Feb 15-Apr 20-Mar 15-Mar 09-May 13-Apr 08-Apr 07-May 26-May Total number of images used

4 2.2 Methods Rice area mapping Rice has distinctive temporal features (and a distinctive temporal signature if images are acquired frequently enough) and we rely on the relationship between the backscatter (σ 0 ) or intensity of the signal reflected back to the SAR sensor and how this changes over time as the basis for monitoring and mapping rice. In C-band, the backscatter during land preparation is low because the soils are flooded with water. The radar backscatter during transplanting increases because of the presence of young rice crop. As the crop grows, there is an increase in the density, height, and biomass, so the backscatter also increases resulting in a stronger backscatter signal. During the reproductive phase when no significant change in biomass, height, and density occurs, the backscatter remains stable. At the end of the ripening phase, a slight decrease in the backscatter occurs due to wilting of some plant parts (Inoue et. al., 2002). Figure 2 shows an example of backscatter derived from C-band SAR, VV polarization Sentinel-1A data, extracted from known land covers. The backscatter increases only during the vegetative stage and becomes quite stable at the reproductive stage consistent with the findings of Le Toan et al. (1997) and Oh et al. (2009). Sentinel-1A SAR images were processed using MAPscape-RICE, software developed by sarmap. It is a dedicated processing chain that enables the mapping of rice areas, detection of start of season, and monitoring of growth on a seasonal basis. The software can use different SAR and optical images for rice mapping and the thresholds used for each type of satellite image can be specifically tailored to suit rice environments and management practices in a particular area. We applied a rule-based rice detection algorithm implemented in MAPscape-RICE, following the procedure described in detail in Nelson et al. (2014). The rules for rice detection are based on the well-studied temporal signature of rice from SAR backscatter and its relationship with crop stages. Start of season (SoS) is assigned to the date when the lowest backscatter signal was observed prior to a rapid increase in backscatter. It is very closely correlated with the date of agronomic flooding prior to transplanting or direct seeding. (a) Rice (b) Other land cover/use Figure 2. Example temporal signatures showing the detection of rice and its start of season (a) and other land covers/uses (b) from Sentinel-1A C-band SAR data VV polarization. Accuracy assessment The validation points were classified into two land cover types: rice and non-rice. A one hectare area buffer around each validation point was created and zonal majority statistic for this region was compared with the actual land use/cover (see Figure 3). The overall accuracy of the rice and non-rice classification and the kappa value were calculated using a standard confusion matrix table. To further assess the quality of the rice maps derived from Sentinel-1A data, we calculated the rice area at regional level and compared the PRISM estimates against the official statistics released by the Philippine Statistics Authority (PSA). 3. RESULTS AND DISCUSSION 3.1 Rice area Figure 3. Example of validation point classed as rice. We were able to delineate more than 381,000 ha of rice during the DS in the study area (Figure 4, Table 2).

5 Region XII had the highest area planted to rice consistent with official statistics (Table 2). The over-all accuracy for Region XII was 89% and kappa value was 0.79 based on 159 ground observations. There was a 26% difference in rice area estimate across the island; this gap could be attributed to the temporal frequency of the image acquisitions (24 days). It is possible that we missed detecting agronomic flooding that occurred in between image acquisitions. The change in the backscatter from very low during the agronomic flooding (land preparation) to the rapid increase as the crop grows is critical in our rice detection algorithm. Aside from the wide gap in between acquisitions, we also had several cancellations which further increased the number days in between some acquisitions. For example, the acquisitions of November 20 for Orbit 142 and February 2 for Orbit 171 were cancelled causing a 48-day gap in between acquisitions. These most likely explain the lower rice area detected in affected regions. The 24-day repeat cycle need to be shortened to make sure that the critical land preparation stage is covered by an image acquisition. Sentinel-1A has been designed to have a 12-day repeat cycle (ESA). But for some areas, currently the repeat cycle is 24 days. Figure 4 shows a large variation in SoS detected across regions. SoS was detected from October to March in Mindanao. The eastern portion of the island is generally planted later (January) than the western portion where planting started as early as October (Figure 5). The peak of planting is November in ARMM, Regions IX and XII, and January for Region XIII (Figure 5). Two peak planting months were observed in Regions X and XI. Figure 4. Rice area map generated over Mindanao, Philippines, DS. SAR imagery from ESA: Sentinel-1A Copernicus data (2015). Sentinel-1A rice area maps processed using MAPscape-RICE. The background used is a DEM from SRTM 90m wherein gray indicates flat areas and white for areas with high elevation. Table 2. Comparison of rice area estimates between PSA and PRISM DS Rice area estimate, ha % difference Region Name PRISM PSA IX - Zamboanga Peninsula 50,833 67, X - Northern Mindanao 52,608 72, XI - Davao 33,715 49, XII - SOCCKSARGEN 149, , XIII - Caraga 42,610 96, Autonomous Region in Muslim -45 Mindanao (ARMM) 52,402 95,920 All 381, ,712-26

6 Area planted to rice by month, '000 ha Figure 5. Start of season map derived from Sentinel-1A imagery over Mindanao, Philippines, DS. SAR imagery from ESA: Sentinel-1A Copernicus data (2015). Sentinel-1A rice area maps processed using MAPscape-RICE. The background used is a DEM from SRTM 90m wherein gray indicates flat areas and white for areas with high elevation Oct Nov Dec Jan Feb Mar 0 ARMM Region IX Region X Region XI Region XII Region XIII Region Figure 6. Temporal distributions of rice start of season in Mindanao, Philippines, DS.

7 4. CONCLUSIONS We presented results from PRISM s first attempt to assess the usefulness of the new freely available Sentinel-1A imagery. The first results for mapping rice areas and seasonality in Mindanao are promising and the relatively high accuracy of the rice classification in one region demonstrates the potential of the imagery and methodology for rice detection. We note however that the 24 days revisit cycle need to be shortened for more accurate detection of rice and start of season. Planting dates are variable in the island ranging from October to March and even within one region. This requires several and frequently acquired images to be able to detect this variability. In some areas, Sentinel-1A has been acquiring images more frequently (every 12 days). This kind of frequency is needed in rice monitoring. For the current cropping season, 42 municipalities in the different regions across the country have been regularly monitored and a more thorough ground validation will be done before the end of the season. This will give us more data and wider coverage for use in accuracy assessment. The ESA Sentinel program is essential in the future of remote sensing based applications for rice (Nelson et al., 2015). The continuous acquisition of images and their availability at no cost makes this ideal for rice crop monitoring in developing countries. From 2015 onwards, Sentinel-1A will become one of the major sources of SAR imagery for the PRISM project. We presented here the activities and results of the rice mapping component of the project. In this project, we are also exploring the use of Sentinel-1A data in drought detection and mapping, and we are also conducting crop health assessments. ACKNOWLEDGEMENTS PRISM, a collaborative project among the Department of Agriculture (DA)- PhilRice, IRRI, DA-Bureau of Plant Industry (BPI) and DA- RFOs in support to DA's Food Staples Sufficiency Program, is funded by the DA National Rice Program through the DA-Bureau of Agricultural Research. We also received funding from DA-RFOs in 16 administrative regions in In addition, we acknowledge the support of the Global Rice Science Partnership (GRiSP) research program of the CGIAR. SAR imagery from ESA: Sentinel-1A Copernicus data (2015). We thank Ms. Jesiree Elena Ann D. Bibar for coordinating the field validation work in Region XII. We also thank DA-RFO and the Local Government Units in Region XII for facilitating the field visits. REFERENCES Boschetti, M., Nelson, A., Nutini, F., Manfron G., Busetto, L., Barbieri et al., Rapid assessment of crop status: An application of MODIS and SAR data to rice areas in Leyte, Philippines affected Typhoon Haiyan. Remote Sens. 7, Bouvet, A., Le Toan, T., Lam-Dao, N., Monitoring of the rice cropping system in the Mekong delta using ENVISAT/ASAR dual polarization data. IEEE Trans. Geosci. Remote Sens., 47, DA (Department of Agriculture), Food staples sufficiency program: Enhancing agricultural productivity and global competitiveness DA, Quezon City, Philippines. ESA website FAO (Food and Agriculture Organization of the United Nations), Rice production in the Asia Pacific region: Issues and Perspectives. Available online at: Inoue, Y. Kurosu, T. Maeno, H. Uratsuka, S. Kozu, T. Dabrowska-Zielinska, K.and Qi, J., "Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables", Remote Sensing of Environment, vol 81(2-3), p.p

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