A new method to estimate rice crop production and outlook using Earth Observation satellite data

Transcription

1 A new method to estimate rice crop production and outlook using Earth Observation satellite data Toshio Okumura, Shin-ich Sobue, Nobuhiro Tomiyama RESTEC Kei Ohyoshi JAXA 17 Feb Don Chan Palace, Vientiane Background Why do we research and develop agricultural remote sensing? 2

2 Background - Food security - The G20 Agriculture Ministers agreed on an Action Plan on food price volatility and agriculture in June The action plan was submitted at a Summit in November In order to improve crop production projections and weather forecasting, the use of modern tools was promoted, in particular remote Natural Oil price Speculation? Part of the G20 Head of States Declaration: 44. We commit to improve market information and transparency in order to make international markets for agricultural commodities more effective. To that end, we launched: The Agricultural Market Information System (AMIS) in Rome on September 15, 2011, to improve information on markets. It will enhance the quality, reliability, accuracy, timeliness and comparability of food market outlook information. As a first step, AMIS will focus its work on four major crops: wheat, maize, rice and soybeans. AMIS involves G20 countries and, at this stage, Egypt, Vietnam, Thailand, the Philippines, Nigeria, Ukraine and Kazakhstan. It will be managed by a secretariat located in FAO The Global Agricultural Geo-monitoring Initiative (GEO GLAM) in Geneva on September 22-23, This initiative will coordinate satellite monitoring observation systems in different regions of the world in order to enhance crop production projections and weather forecasting data. 3 Background - Remote sensing tec. for agriculture - The GEOGLAM serves as a useful input for the AMIS. (four type of commodity crops wheat, maize, rice, and soybeans) Since rice is the main commodity crop in Asia, JAXA proposes and leads the Asian Rice Crop Estimation & Monitoring project (Asia-RiCE) for GEOGLAM. Asia-RiCE is a collaborative effort between a number of Asian organizations. 4

3 Background - Remote sensing tec. for agriculture - RESTEC provides the technical and administrative support for JAXA s lead role in Asia-RiCE. RESTEC Developed a system to estimate rice crop acreage using GIS and space-based remote sensing data for the Ministry of Agriculture, Forestry, and Fisheries (MAFF), Developed software to estimate rice crop acreage and production using space-based Synthetic Aperture Radar (SAR), named INAHOR for JAXA, Additionally, implemented a satellite-based agricultural weather information system, named JASMIN for JAXA, And is supporting JAXA s activities related the food security. 5 Today s topics; Methodology to estimate rice crop acreage and production using space based microwave radar (SAR), which we developed Results obtained by using the method Rice crop outlooks using JASMIN, which is one of current activities 6

4 Methodology A new method to estimate rice crop acreage and production using space based microwave radar (SAR) 7 Methodology - Advantages of Earth Observation by Satellite - Earth Observation satellites can collect the information: Over a broad area, even if the area is difficult to access, Periodically, With high consistency, In near real-time, Cost-effectively. Approx km height from the Earth s surface 8

5 Methodology - Advantages of Earth Observation by Satellite - Space based remote sensing technology should be very powerful tool for agriculture monitoring in national and provincial level. Satellites/Sensors SAR Microwave Radiometer RADAR Optical Sensor (Global Imager) Optical Sensor (High Res.) Products from satellite data Paddy Field Mapping Flood Monitoring Crop Growth Agro-meteorological Monitoring Topography Agricultural Applications Agricultural Stat Early Warning Damage Assessment 3 Land Resource Management 9 Methodology - Advantage of synthetic aperture radar (SAR) - Optical sensor Sunlight Active radar SAR Microwave Ear of rice can penetrate the cloud. Observed simultaneously ALOS AVNIR-2 : Optical sensor can not observe the ground under the cloud. ALOS PALSAR : SAR can observe the ground under the cloud. 10

6 Methodology - Advantage of synthetic aperture radar (SAR) - For Agriculture monitoring, we need time series data constantly. Since however, in Asia, many crops mainly grow in rainy season, it is very difficult to monitor the crop situation only using optical sensors. Therefore, we mainly applied SAR data to estimate rice crop area and production in Asia. 11 Methodology - Basic approach to estimate paddy area using SAR - Specular Reflection Weak Backscatter Strong Backscatter Flooding Flooding stage Sowing/ Planting Transplanting stage Vegetative Mature stage n Tropics) Rice et al., 1997] dark little bright dark brighter We can estimate the paddy area by detecting the dark areas in flooding / planting stage and by detecting the bright areas in vegetative stage from SAR image data. 12

7 Methodology - Basic approach to estimate paddy area using SAR - SAR Image over Paddy Paddy area has Flooding and Vegetative stages. Backscatter Maximum Range (Max-Min) Minimum Flooding Planting Vegetative Phenological Stage (Flooding / Planting stage) (Vegetative stage) If (Minimum < Threshold1) and (Range > Threshold2) Paddy Area 13 Results The estimation result of rice crop acreage and/or production using the new method in Thailand 14

8 Results - JAXA & GISTDA Cooperation project - From 2011 to 2012, a collaborative project of ALOS series and THEOS series was conducted by JAXA and GeoInformatics and Space Technology Development Agency (GISTDA) of Thailand. We verified the new method with SAR data, in the rice crop working group of the project under contract to JAXA. Khon kaen Target area were Khon kaen province, Suphan buri province, and Thailand whole country. Suphan buri Target season is rainy season. 15 Results - In Khon kaen - Khon Kaen is located in the northeastern part of Thailand. The feature is that most of the paddy fields are rainfed. Khon kaen 16

9 Results - In Khon kaen - We conducted field survey for about 200 fields for validation. 17 Results - In Khon kaen - Automatic data collection by field router meteorological sensor Air Temperature, Soil Temperature, Precipitation (rain gauge), Radiation, Image sensor (CMOS camera) 5 Aug Apr May Jun Jul Aug Aug

10 Results - In Khon kaen - We periodically checked water situation and type of planting. 19 Results - In Khon kaen - We measured crop yield (production per area) by cutting rice plant in harvest stage. Cutting Drying in the sun Drying in the machine Tkk_2011- Yield [kg] more Threshing Measuring Acreage & Production 20

11 Results - In Khon kaen - Minimum Maximum image Detected image during during paddy flooding vegetation area / planting stage stage Results - In Khon kaen - Zoom T21 Estimated result Validation data from field survey Acreage [m 2 ] Yield [g/m 2 ] Production [ton] Estimation value 164, (203.96) * Validation value 166, Accuracy of estimation 98.58% % *1: The statistic information was used to estimate the production. 22

12 Results - In Khon kaen - The accuracy of acreage estimation was about 98%, because the flooding situations of paddy fields were gotten well from SAR image data during the planting stage. The accuracy of production estimation was about 82%, because the variation in the yield of direct seeding fields was large at the verification site, and varied substantially from the statistical information which was used to estimate the production. To estimate rice crop production with high accuracy, yield for each field type is require. Studies are underway to get yield from SAR data by using a correlation between biomass and the SAR backscatter. 23 Results - In Suphan buri - Suphan buri Suphan buri is located in the central part of Thailand. The feature is that most of the paddy fields are irrigated. An accuracy of acreage estimation was about 76%. Some sugarcane fields were misjudged as rice. To improve accuracy of estimation, studies are being undertaken to better distinguish rice from sugarcane. Minimum image during planting stage Maximum image during vegetation stage Deference image between planting and vegetation stage Estimated area of rice crop 24

13 Results - Wall-to-wall estimation of Thailand - We estimated wall-to-wall of the acreage and production of Thailand s rainy season rice crops. The satellite data used was ScanSAR from ALOS PALSAR provided by JAXA, which provides a 100m spatial resolution, and a 350km observation swath width. Although ScanSAR data is coarser, monthly updates for the entirety of Thailand can be achieved. And, it is useful to estimate rice crop area in provincial level. 300km 25 Results - Wall-to-wall estimation of Thailand - Imagery collected in 2009 was used, with the planting stage defined as April to August, and the vegetative stage was taken to be August to November. < RSP Path > RSP118 RSP121 RSP124 RSP127 RSP130 Planting stage Vegetative stage There were some data gaps. 26

14 Results - Wall-to-wall estimation of Thailand - Images during planting stage Images during vegetation stage Minimum pixel values image during planting stage Maximum pixel values image during vegetation stage Deference image between planting and vegetation stage Estimated area of rice crop 27 Results - Wall-to-wall estimation of Thailand - Considerations By comparing with statistical information provided by Thailand s Office of Agricultural Economics (OAE), an accuracy of more than 70% was confirmed in 37 provinces in 76 provinces. Error factors estimation are as follows; The some data gaps might be origin. The provinces which main crop was rice were good. The other crops might be misjudged as rice. The crop calendar might be different with our assumed. We need rural information such as crop calendar and so on. In the future, it is suggested that the methodology be improved by conducting field surveys in areas that the accuracy of less than 70% was confirmed. 28

15 Results - Wall-to-wall estimation of Thailand - To improve accuracy of estimation, studies are being undertaken to better distinguish rice from other crops by analysing biomass in the well-grown stage, analysing the period from planting to harvest, paying attention to the characteristics of cross polarization, and analysing complex images with full polarizations. 29 Rice crop outlooks Rice crop outlooks for GEO GLAM & AMIS 30

16 Rice crop outlooks - JASMIN - Asia-RiCE, led by JAXA, has also started to provide rice crop outlooks in Thailand, Vietnam and Indonesia for FAO AMIS by using JASMIN. RESTEC developed JASMIN to provide satellite weather information to statistical experts. JASMIN displays information in maps and graphs, and the information includes information on current conditions and anomalies (deviations from past normal years). Data is updated twice a month. 31 Rice crop outlooks - JASMIN - JASMIN provides 6 parameters. Parameters Interval Spatial Resolution Data Period (anomaly calc.) Satellite Data Source Precipitation Cumulative (15-day) 10 km ( ) GSMaP (GCOM-W1, TRMM, MTSAT etc.) Solar Radiation 15-day Average 5 km ( ) MODIS Land Surface Temperature 15-day Average 5 km ( ) MODIS Soil Moisture 15-day Average 50 km ( ) AMSR-E, WindSat Drought Index 15th /31[30]th day of month 10 km ( ) GSMaP, MTSAT Vegetation Index 15th /31[30]th day of month 5 km ( ) MODIS 32

17 Rice crop outlooks - Work-flow - Provide satellite derived information on the WWW Develop Rice Outlook by AFSIS and Agricultural Statistician in each country Review and Post Asia-RiCE Outlook Develop Crop Monitor report for AMIS Market Monitor Publish Market Monitor (Monthly) 33 Rice crop outlooks - Current status - Rice crop outlooks using the satellite weather information system JASMIN were started from last October in three countries, Indonesia, Vietnam and Thailand. It is expected that more knowledge will be accumulated. In the future, JASMIN will be expanded to support FAO AMIS outlook reporting for other ASEAN countries in cooperation with AFSIS. 34

18 Conclusions 35 Conclusions RESTEC developed INAHOR and JASMIN under contract to JAXA. INAHOR is software that is used to estimate rice crop acreage and production using SAR data. JASMIN is a web-based system that provides satellite agricultural weather information to statistical experts for the purpose of making crop outlooks. JAXA and RESTEC are working to verify their methods of rice crop monitoring using EO satellite data for GEO GLAM/Asia-RiCE. In addition, we also preparing to apply new SAR sources such as ALOS-2 and Sentinel-1. 36

19 Thank you for your kind attention 37 Appendix 38

20 RESTEC overview Remote Sensing Technology Center of Japan 39 RESTEC overview - Main business - Earth Observation : Reception, Processing and Provision of data acquired both by domestic and foreign satellites. Development and Operations of ground stations. Research and Development : Conducting calibration and validation of remote sensing data, development of algorithm and software in remote sensing. Developing processor and observation platform. Capacity Building : Providing both domestic and international personnel with remote sensing training. Capacity building for developing countries including technology transfer. Think tank/consulting : Conducting consulting and research works related to the earth observation and remote sensing. Assessment and analysis of remote sensing needs in emerging and developing countries. Solution Businesses : Offering value added services including consultation in remote sensing technologies. Monitoring agricultural crop production related to food security. Monitoring natural disasters. More Details : 40

21 RESTEC overview - Main customer - Main customer is JAXA (Japan Aerospace exploration Agency) JAXA develops and researches the space technologies and its utilization. More than 70% of our business is from JAXA. Human Space Activities Aerospace exploration Planetary exploration Space Transportation Systems Satellites & Spacecraft Aeronautical Technology Research 41 Methodology - 42

22 Framework for crop yield estimation 43 Flow chart of INAHOR software Manual Automatic Open satellite image data Noise reduction NG Select images for planting stage and well-growing stage Set two threshold parameters to detect flooding and well-growing area Check the result of rice crop area mapping OK Detect flooding area Detect well-growing area Mask the other than farmland (if LULC from optical sensor data is available) Mapping rice crop area Rice crop area Finally, the rice crop production is calculated by result of estimated rice crop area and yield per unit from statistic information or field survey. 44

23 Operation of INAHOR INAHOR runs on Linux OS Ubuntu. INAHOR is developed by open sources. (Qt, PostgreSQL, PostGIS, Shapefile C Library, LibGeoTIFF, PROJ.4, GEOS,GDAL/OGR, netcdf, HDF, etc. Ubuntu is also open source.) 45 Current Activities Monitoring rice crop situation and Outlook of rice crop in Asia for GEO GLAM / Asia-RiCE 46

24 Current Activities Activity for ALOS-2 PALSAR-2 - For ALOS-2 PALSAR-2, a study to estimate biomass using SAR data is going in Indonesia. The classification to four stages of paddy field has done. The verify of result is going. Vegetated 1 Vegetated 2 Planting Harvesting 47 Current Activities - GEO GLAM/Asia-RiCE activity- Asia-RiCE was launched by JAXA after the agricultural ministers 2011 G20 meeting which it was decided to launch AMIS and GEOGLAM. Technical demonstration sites (TDS) were selected in each country. These sites are being used to verify the rice crop monitoring methodology using statistical information, ground (in-situ) observations, computer modelling, SAR, and other space-based observation data Technical Demonstration Sites for Asia-RiCE Japan India China Taiwan Thailand Laos Vietnam Philippines Malaysia Indonesia Phase 1A: Jun Nov 2014 Phase 1B: Apr Mar

25 Current Activities - Asia-RiCE activity in Japan- RESTEC supports that JAXA is developing ground (in-situ) observation devise and analyzing method to estimate biomass, crop calendar and so on, at Japanese Technical demonstration site where is Tsuruoka, Yamagata Prefecture, with Professor from Tsuruoka college of technology and Aizu university. The studies are going to start April, next crop season, by combining the ground observation data and satellite data. 49 Current Activities - JASMIN - Current Condition Anomaly Precipitation : Few precipitation can causes drought and too much precipitation can causes flooding. Much Much Less Less Solar radiation : Solar radiation is one of the key factors for rice growth. High solar radiation means there is few cloud and a lot of solar radiation comes to land surface. Current Condition Clear Cloudy Anomaly Clear Cloudy Soil moisture : Available water in the soil is a significant factor for rice growth. High soil moisture means available water in the soil is enough. Low soil moisture means at the risk of drought. Current Condition Wet Dry Anomaly Wet Dry 50

26 Current Activities - JASMIN - Current Condition Anomaly Drought index : Drought index shows the degree of drought. High index means that there are few available water (drought). Dry Dry Wet Wet Vegetation index: NDVI is not agro-meteorological parameter, but the index to indicate the amount of leaves. High NDVI means much vegetative and less NDVI means less vegetative. Current Condition Much Less Anomaly Much Less 51 Conclusions RESTEC is determined to contribute to capacity building for remote sensing utilization in the world. work with agencies and institutions for better utilization of remote sensing technologies. provide technical services of remote sensing to organizations engaged in resource management, disaster control and others. 52