Results from Lao People’s Democratic Republic, Philippines, Thailand, Viet Nam Earth Observation Technologies for Crop Monitoring: A Workshop to Promote Collabrations among JECAM/Asia-RiCE 2018 Lakshman Nagraj Rao Statistician Economic Research and Regional Cooperation Department Asian Development Bank

INTRODUCTION Rice is a staple food for the majority of population in Asia and the Pacific. Importance of timely and reliable crop production statistics Relevance to mitigating food security issues (SDGs) Planning government interventions in the agriculture sector Surveys, censuses are expensive to conduct and take longer to process. Administrative data are prone to measurement error. Alternative or supplemental data support system would be through remote sensing or the use of satellite data.

INTRODUCTION (2) With satellite data, But…. Rice area may be estimated several times a year instead of only during the survey period. Also open doors for forecasting. Land use can be studied better by comparing area maps taken in different times. But…. New technology may be difficult to adapt Statisticians have to be trained on the software Costs may be higher Small and varied rice plots (& cropping patterns) Mind set of policy makers and government planners has to be influenced to accept the new methodology This is where ADB comes in!

Innovative Data Collection Methods for Agricultural and Rural Statistics Source of Funds: Japan Fund for Poverty Reduction Technical Advisor: Japan Aerospace Exploration Agency Pilot Countries: Savannakhet (Lao PDR), Nueva Ecija (Philippines), Ang Thong (Thailand), and Thai Binh (Viet Nam) Implementation Period: June 2013 to October 2017 Objectives: Development of customized software applications and methodology to estimate paddy rice cultivation area and crop production using satellite data Training of counterpart staff in the four pilot countries, and Development of an online training program on the use of satellite data for agricultural and rural statistics.

Innovative Data Collection Methods for Agricultural and Rural Statistics Implementing Partners Center for Agricultural Statistics, Department of Planning and Cooperation, Ministry of Agriculture and Forestry, Lao People’s Democratic Republic Philippine Statistics Authority, Philippines Office of Agricultural Economics, Ministry of Agriculture and Cooperatives, Thailand Geo-Informatics and Space Technology Development Agency, Thailand Center for Informatics and Statistics, Ministry of Agriculture and Rural Development, Viet Nam

Innovative Data Collection Methods for Agricultural and Rural Statistics JAXA software application (INAHOR) used to transform satellite images into rice area maps, but modified and customized according to the needs of the country. Transformation undertaken by Remote Sensing Technology Center of Japan (RESTEC). INternational Asian Harvest mOnitoring system for Rice (and “INAHO” also means “rice year” in Japanese) The main functions : Providing a rice planted area map (including the growing stages classification) Providing a rice planted area and production Input satellite data : Time-series SAR data (ALOS-2 PALSAR-2 – 100m resolution, 350 km swath)

Stratified three-stage sample design Stratified random sample of 120 meshes (200m x 200m) selected for the pilot survey in each province Random sample of reserve meshes for possible replacement for each province Sample allocation concentrates sample in strata with more rice based on data collected during growing stage. First stage Selection of sample meshes within each stratum Second stage Random selection of 4 sample plots with rice within each sample mesh Third stage Random selection of one sub-plot within each sample rice plot for crop cutting Rainy season of 2015

FIELD SURVEYS (CROP CUTTING AND FARMER RECALL) Selection of 120 meshes Random selection of "plots" in the 200m x 200m meshes Tracking of plot boundaries Random selection of sub-plot Harvesting Moisture reading and weighing of samples Cleaning of samples Threshing Drying Weighing and moisture reading of dried samples

30% Rice 100% Rice About 120 points were surveyed 200m 200m 200m x 200m x 30% = 1200m2 200m x 200m x 100% = 40000m2 Sum of all surveyed mesh rice area = 1,842,640 m2 or 184.2 hectares Calculated result for each mesh with INAHOR = 1,777,800 m2 or 184.2 hectares The estimated % = INAHOR / Survey = 96% Meaning the estimated result is 4% under estimation

Comparison of Paddy Area Estimates (in ‘000 ha.) Stratum Country/Province Savannakhet LAO PDR Nueva Ecija PHILIPPINES Ang Thong THAILAND Thai Binh VIET NAM Official statistics 181.3 172.8 47.7 81.3 INAHOR-AD 258.0 221.9 40.9 78.6 Unmodified Tracks 270.0 207.5 29.2 47.4 Digitized Tracks 249.7 205.5 29.9 48.5 Re-digitized Rice Areas Inside the Mesh (ALIS Method) 260.7 202.8 39.8 73.3

R-CDTA 8369 RESEARCH WORK IMPLEMENTED (1) Land Measurement Bias: Comparisons from Global Positioning System, Self-Reports, and Satellite Data ASSOCIATED BLOG Land measurement bias revisited: Using Google Earth in agricultural land measurements as an alternative technique produces land area estimates close to Global Positioning System (GPS) estimates at reduced fieldwork costs. In addition to the three (3) main outputs, there will be four (4) economic working papers and one (1) Handbook on the Use of Remote Sensing for Paddy Area and Production Estimation. Two EWPs are now finalized and will be published within the year. One EWP has just been presented to ADB staff and will be finalized soon. While one EWP (e.g. country report???) and the handbook have been drafted and will be available by first quarter 2018.

LAND SIZE – PRODUCTIVITY RELATIONSHIP

R-CDTA 8369 RESEARCH WORK IMPLEMENTED (2) Measuring Rice Yield from Space: The Case of Thai Binh Province, Viet Nam ASSOCIATED BLOG Measuring rice yield from the sky: An innovative data fusion technology, which combines Landsat and MODIS data, thereby increasing spatial and temporal resolution is used to map paddy area and estimate yield in Thai Binh Province, Viet Nam. In addition to the three (3) main outputs, there will be four (4) economic working papers and one (1) Handbook on the Use of Remote Sensing for Paddy Area and Production Estimation. Two EWPs are now finalized and will be published within the year. One EWP has just been presented to ADB staff and will be finalized soon. While one EWP (e.g. country report???) and the handbook have been drafted and will be available by first quarter 2018.

Crop Yield Map for Thai Binh Check analysis

Innovative Data Collection Methods for Agricultural and Rural Statistics RESEARCH WORK IMPLEMENTED (3) Technological Innovation for Agricultural Statistics: Special Supplement to Key Indicators for Asia and the Pacific 2018 ASSOCIATED BLOG Technological Innovation is a game-changer for Agricultural Statistics: Both emerging and existing technologies should complement each other to bolster agricultural data quality. In addition to the three (3) main outputs, there will be four (4) economic working papers and one (1) Handbook on the Use of Remote Sensing for Paddy Area and Production Estimation. Two EWPs are now finalized and will be published within the year. One EWP has just been presented to ADB staff and will be finalized soon. While one EWP (e.g. country report???) and the handbook have been drafted and will be available by first quarter 2018.

Innovative Data Collection Methods for Agricultural and Rural Statistics Developed an online training con Estimating Rice Paddy Extent and Production with ALOS-2/PALSAR-2 and INAHOR-AD Promotional video for the course: https://youtu.be/SSwg000ooHc Link for the course: http://adbx.online/ Webpage for ADB’s Agricultural Statistics activities and training materials: http://cars.adbx.online/ INAHOR-AD has three major functions: (1) mapping of rice planted area, (2) calculation of rice planted area, and (3) calculation of rice production. The software application uses time series Synthetic Aperture Radar (SAR ) data in planting stage and well-growing stage and yield information. Between 14 to 25 ministry staff were trained and participated in the different training/workshop conducted under the TA. Important points from LNR: Can we be specific on SNAP QGIS and other software used during the final training? We must ask Chrys/Tin because I was only intermittently in and out. We may also want to break them down since ii has a lot of information. Suggestion: (II) CAPI using Survey Solutions (iii) Use of QGIS and SNAP using Sentinel 1 and 2 data (Iv) Crop Cutting and Farmer Recall Survey I think we can drop basic remote sensing since it is covered in point 2.

R-CDTA 8369 UPCOMING RESEARCH Area Sampling Frame for Paddy Rice Statistics (November 2018) Handbook on Estimating Rice Paddy Extent and Production with ALOS-2/PALSAR-2 and INAHOR-AD (November 2018) In addition to the three (3) main outputs, there will be four (4) economic working papers and one (1) Handbook on the Use of Remote Sensing for Paddy Area and Production Estimation. Two EWPs are now finalized and will be published within the year. One EWP has just been presented to ADB staff and will be finalized soon. While one EWP (e.g. country report???) and the handbook have been drafted and will be available by first quarter 2018.

LESSONS LEARNED Provision of satellite data with minimal fee (if not for free) is critical. JAXA has very kindly entered into partnership agreements to facilitate this work. Possibility of using cloud based platform for data access and analysis. Consider models for yield monitoring and not just area. Mixed/intercropping and/or crops beyond rice. Integrating surveying techniques into field mapping activities through a common map.

LESSONS LEARNED Policymakers are open to new techniques, but pushing this agenda still requires collaborations with IOs such as ADB to facilitate strategic partnerships. More systematic studies comparing administrative, survey, and remote sensing based estimates critical to the mainstreaming of such methods. This involves statisticians and remote sensing experts to work more closely together. How to get down to the plot/parcel level for heterogeneous smallholder plots?

FUTURE PLANS Project: Data For Development (being processed) Preparation of digitized field maps

THANK YOU!

PRESENTATION OUTLINE Introduction TA 8369: Innovative Data Collection Methods for Agricultural and Rural Statistics Sampling Strategy and Field Surveys Comparison of Paddy Rice Area Estimates from field methods, administrative data, and satellite data Land Measurement Bias Rice Yields from Space Technological Innovations for Agricultural Statistics Lessons Learned and Future Directions