GEO-GLAM Agricultural Mapping and Monitoring

Transcription

1 GEO-GLAM Agricultural Mapping and Monitoring Chris Justice Inbal Becker-Reshef and Matt Hansen University of Maryland, Department of Geographical Sciences June

2 The GEO Global Agricultural Monitoring (Structure for Ag 0703 >Restructuring to new work plan Ag 01 ) Task Co-Leads: Chris Justice, University of Maryland, (USA) * Olivier Leo, Joint Research Centre Ispra, (E.C,) Derrick Williams, USDA FAS, (USA) Wu Bingfang, IRSA, CAS, Beijing, (China) Task Executive Director: GEO Secretariat POC: Jai Singh Parihar, ISRO, (India) Where are our Russian agricultural leaders? Joao Soares, GEO Secretariat, (Brazil) JECAM Comp. Lead: PAY Comp. Lead: Cropland Mapping Lead: GEOGLAM Lead: CEOS GEO Ag POC: Ian Jarvis, Agriculture and Agri-Food Canada, Pierre Defourny, UCL (Belgium) Inbal Becker-Reshef, UMD, Meng Jihua, IRSA (China) Steffen Fritz, IIASA, (Austria) Pascal Kosuth (France) Prasad Thenkabail, USGS (USA) * NASA Applied Sciences supported (Brad Doorn, NASA POC)

3 The GEOGLAM Initiative: Project Elements 1. GLOBAL/ REGIONAL SYSTEM OF SYSTEMS Main producer countries, main crops 2. NATIONAL CAPACITY DEVELOPMENT for agricultural monitoring using Earth Observation 3. MONITORING COUNTRIES AT RISK Food security assessment 4. EO DATA COORDINATION (acquisition, availability, access) 5. METHOD IMPROVEMENT through R&D coordination 6. INFORMATION DISSEMINATION of Data and Products GEOGLAM is a coordination initiative, aiming at providing key information on Agricultural production using Earth Observations through: - supporting, strengthening and articulating existing efforts and - developing capacities and awareness at national, regional and global levels - providing coordinated input to the Agricultural Market Info. System (AMIS)

4 Countries producing over 80% of global crops

5 Percent of Population by Country with Insufficient Food UN FAO

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7 Cropping systems are inherently diverse which dictates the monitoring observations and methods

8 Need for higher frequency moderate resolution observations: Sentinel 2A and B LDCM Longitude: Latitude: The large number of blue colored bands (>41 accesses) indicate that the revisit interval over the majority of the region is on the order of 2 days. 8 The picture shows the number of times LDCM and the Sentinel 2 satellites accessed areas on the ground over an 80 day period of time. 21 accesses indicates a maximum revisit interval of ~3 days 19 hours 46 accesses indicates a minimum revisit interval of ~1 day 18 hours 8 Courtesy Brian Killough, NASA LARC

9 Proposed Data Initiative for Agricultural Landuse from Landsat and Sentinel Landsat (USGS) Sentinel-2a (ESA) Goal 1: Create consistent, merged Landsat and Sentinel-2 reflectance dataset - builds on MODIS, MERIS, and Landsat processing heritage - builds on previous data initiatives among NASA, ESA, and USGS - establishes consistent radiometric data set for land phenology Goal 2: Leverage new datasets for agricultural monitoring (e.g. GEOGLAM prototyping) Goal 3: Support transition to operational agencies - GEOGLAM, USDA FAS and EC JRC MARS programs - examples: UMD/USDA MODIS GLAM MODIS LANCE Four year effort ( ) - Phase 1: prototype with limited geographic scope (4-5 demonstrator countries); - Phase 2: expand to support global Ag monitoring with demonstration of success Data Ingest & Preprocessing Atmospheric Correction Bandpass Correction Regridding & formatting ~5-day 30m reflectances Agricultural Monitoring GEOGLAM prototyping Ag Land use research Crop type Crop vegetation status Ag land use change

10 JOINT EXPERIMENT FOR CROP ASSESSMENT AND MONTORING (JECAM): an initiative to evaluate and compare data and methods using satellite observations from different sensing systems

11 A network of distributed regional experiments on cropland pilot sites around the world representing a range of agricultural systems (new sites can be considered) Shared time-series datasets from a variety of earth observing satellites and in-situ data and assess them for agricultural assessment and monitoring Facilitated inter-comparison of monitoring and modeling methods, product accuracy assessments, data fusion and product integration, for agricultural monitoring Development of community standards and protocols for monitoring in the framework of GEOGLAM Sentinel 2 Symposium, JECAM Side Event

12 General JECAM Site Characteristics: JECAM sites are relatively small, well defined geographic areas Experiments span 3-5 years (allowing for repeat testing of methods) JECAM usually leverages existing research initiatives within countries. Due to ecosystem differences the experiments will test a variety of techniques incorporating a wide range of optical and radar data sets In-situ data and satellite data can be shared between experimental sites for collaborative research JECAM sites are looking at a common range of monitoring needs over a very diverse range of landscape conditions and cropping systems. Including: Crop identification and acreage estimation Yield prediction Near Real Time Crop condition \ Crop stress Land management Soil moisture Sentinel 2 Symposium, JECAM Side Event

13 JECAM activities are being undertaken at a series of study sites which represent the world s main cropping systems and agricultural practices. 15 sites currently exist ( 1 in development). Additional sites will be added to meet science objectives and ensure all major crop systems are addressed. Mali site in development

14 CEOS-JECAM Satellite Data Acquisition and Access Optical Data Aqua Terra Landsat 5 Landsat 7 MNP EO-1 ResourceSat 1 ResourceSat 2 THEOS DMCii QuickBird Worldview-1 Worldview-2 Radar Data Radarsat 1 Radarsat 2 TerraSARX Envisat ERS-2 Passive Radar Aqua SMOS

15 Current Status of GEOGLAM Refining the GEOGLAM work plan Expanding national participation G20 Focus (Russia?) Fund raising for GEOGLAM activities and developing commitments e.g. France, EU FP7, US, Canada Working with CEOS on satellite requirements/acquisition Developing partnerships and activities Looking for initial national capacity building demonstrations for GEOGLAM focus Would welcome a connection to the Russian Ministry of Agriculture Would welcome Russian technical partners JECAM Agricultural Test sites in Russia

16 UMD Agricultural Monitoring Activities GLAM- Satellite data provision and support to USDA FAS and the international community NRT, MODIS>VIIRS Crop condition monitoring Agricultural Drought Monitoring Crop Area and change monitoring Yield forecasting Capacity building (Pakistan) Co-leading G20 international initiative - GEOGLAM Partners: NASA, USDA, Pakistan, FAO, GEO Partners

17 GLAM System Web Interface for Querying and Analyzing MODIS VI Time Series

18 MODIS NDVI Departure from Average April 30 - May 7, 2012 Favorable early-spring conditions in Rostov and Volgograd (circled) were expected to compensate for poor conditions in Krasnodar and Stavropol directly to the south. Source: USDA/NASA/UMD GLAM Project

19 MODIS NDVI Departure from Average May 8-15, 2012 The NDVI from mid-may begin to show the early effects of excessive April heat and three consecutive weeks of dryness. Source: USDA/NASA/UMD GLAM Project

20 MODIS NDVI Departure from Average May 16-23, 2012 Winter-grain conditions deteriorated as the heat and dryness continued in the Southern and Central Districts. Source: USDA/NASA/UMD GLAM Project

21 MODIS NDVI Departure from Average May 24-31, 2012 The Southern District received much-needed rainfall during the second half of May. Source: USDA/NASA/UMD GLAM Project

22 Apr 6 Apr 14 Apr 22 Apr 30 May 8 May 16 May 24 Jun 1 Jun 9 Jun 17 Jun 25 Jul 3 Jul 11 Jul 19 Jul 27 NDVI 85 Volgograd: MODIS NDVI Source: USDA/NASA/UMD GLAM Project

23 Mar 13 Mar 21 Mar 29 Apr 6 Apr 14 Apr 22 Apr 30 May 8 May 16 May 24 Jun 1 Jun 9 Jun 17 Jun 25 Jul 3 Jul 11 Jul 19 Jul 27 NDVI 90 Stavropol: MODIS NDVI Source: USDA/NASA/UMD GLAM Project

24 Strong correlation between NDVI Peak and yield Example of Daily Normalized Difference Vegetation Index (NDVI from MODIS) , Versus Crop Yields (Blue numbers are Yield (MT/Ha) ) in Harper County Kansas Regression-based model developed as a function of: a seasonal maximum, background adjusted, NDVI Pixel percent of wheat Winter Wheat emergence NDVI peak Winter Wheat seasonal NDVI peak Year

25 Wheat Development Spring Wheat Development Stages (Zadoks) & approximate time between growth stages image from University of Minnesota Extension Daily NDVI of Winter Wheat in Harper County Day 14 Day 55

26 NDVI 16-day Composite Data for Wheat site in Harper County Day of Year

27 NDVI Daily NDVI for Wheat Site in Harper County Day of Year Day of Year

28 NDVI 16-day Vs. Daily NDVI for Wheat in Harper County Site Day of Year

29 An Approach to Wheat Yield Forecasting Timely crop specific information on extent/ distribution is a key input for monitoring crop conditions and for forecasting yields This type of information is critical and not readily available A viable and practical alternative is to spatially aggregate a single year crop mask as percent wheat and apply to multiple years Tradeoff in % error is between 1.2% relative to control Temporal res is critical!! 16-day composite often miss peak

30 Spatial Resolution: Approach to mitigate effects of crop rotations Hypothesis: if a year specific wheat map is aggregated to coarser resolution as a percent wheat mask, the per grid cell percent wheat will become stable at a coarser resolution. Assumption: proportions stay the same Becker- Reshef )

31 Crop Type Mapping and Area Mapping using multiple resolutions of Earth Observations for pre-harvest assessment of crop area Hansen et al

32 Global Cultivated Area 250m Iowa, USA Syria Pittman, K.W., Hansen, M.C., Becker-Reshef, I., Potapov, P.V., & Justice, C.O. (2010). Estimating global cropland extent with multiyear MODIS data. Remote Sensing Journal

33 MODIS for crop type identification Wardlow et al. 2007

34 Example of Crop Type Classification used to develop a regression estimator of crop area Integrating multiple resolution data from MODIS, AWiFS and Landsat Example: 2007 Midwest Corn Hansen et al >40% Corn Cover Per Pixel Hansen et al.

35 Crop Classification Zoom: Example from Illinois Classified Image: Corn- Red Soybeans- Yellow Hansen et al

36 Method MODIS used for generalized models to generate to indicate within growing-season soybean cultivation based on sub-pixel percent cover training data MODIS soybean indicator maps are used : to develop the stratified sampling strategy of higher resolution data (landsat and rapid eye) performing regression estimators of high-res sample blocks Landsat samples used to map per sample block soybean cultivated area RapidEye allows for per country/region calibration of Landsat area estimates The Landsat sample blocks are then analyzed to quantify national-scale crop type area Hansen et al

37 MODIS Landsat RapidEye 5km x 4.7km near Stuttgart, Arkansas

38 MODIS Landsat RapidEye 5km x 4.7km near Stuttgart, Arkansas

39 MODIS based soybean strata: High, medium and low Red=high (>19.8%), orange=medium ( %), yellow=low ( %)

40 Stratified Random Sample Landsat blocks (each block 40km X 40km) Red=high (>19.8%), orange=medium ( %), yellow=low ( %)

41 RapidEye blocks Red=high (>19.8%), orange=medium ( %), yellow=low ( %)

42 Multi-resolution crop area estimation The combined use of MODIS Landsat and Rapid Eye enable an efficient means for accurately monitoring crop area over large areas MODIS enables to follow new cultivation areas from year to year, providing a dynamic stratifier for high res sample blocks Hansen et al

43 Winter Wheat Yield Forecasting Overall Objective: develop a practical and robust approach to forecast wheat yields at regional/national scales using multitemporal and spatial resolution earth observations Reference: Becker-Reshef I, Vermote E, Lindeman M, Justice C In Remote Sensing of Environment, 114,

44 Kansas Results: Kansas Model Estimates vs. USDA NASS Crop Statistics Model Estimates are within 7%, 6 weeks prior to harvest Becker-Reshef I, Vermote E, Lindeman M, Justice C In Remote Sensing of Environment, 114,

45 Model Extendibility to Ukraine

46 Developing a Wheat Mask for Ukraine using a Classification Tree Method

47 Model Results in Ukraine: Model estimated production vs. Ukrainian State Statistical Committee Crop Statistics RMSE= 10% R= 0.94 Y=0.9934X The model forecast 6 weeks prior to beginning of harvest was within 10% of final reported production Becker-Reshef et al

48 Initial model Results for Four Southern Oblasts in Russia, and Western Australia ( )

49 Conclusion/Implications for Wheat Yield Forecasting An empirical RS based wheat forecasting approach that is generalized and transferable between wheat growing countries can be implemented to forecast yields at the national scale Limited data requirements, utilizing freely available coarse resolution data Errors relative to final yields, 6 weeks prior to harvest between 7% and 10% Promising results for implementing model for primary wheat producing countries where data are limited