Results from the Land & Environment Crop & Drought. projects in Dragon-3. Chaired by S. Pignatti & F. Jinlong

Transcription

1 Results from the Land & Environment Crop & Drought projects in Dragon-3 Chaired by S. Pignatti & F. Jinlong

2 Project 10367: Desertification Monitoring and Assessment Committed products optimal indicators for desertification assessment through comparisons among vegetation fraction trend analysis, Net Primary Production (NPP) scaling, Rain Use Efficiency (RUE) and its advanced versions (2dRUE) an operational desertification assessment system at the national and regional scales Geographic scope China drylands: Potential Extent of Desertification in China (dry subhumid, semiarid and arid zones) Resolutions: 4 km, 1 month Target user: Focal Point of China in UNCCD progress New version of NPP time-series CASA (ESA Envisat-MERIS images), sandy lands Climatic, hierarchical regionalization of China (9 ecozones) Benchmark 2dRUE application to dry lands of Northern China, for each domain 2dRUE will be fitted for modelling 2dRUE -> Assessment of land states: legend system IFRIT-EEZA joint field campaign in Inner Mongolia and Gansú (04-11 June 2015) FAO-UNEP aridity index

3 Project 10367: Desertification Monitoring and Assessment Fact and findings from last year s work Potential Extent of Desertification in China almost doubled from to , mainly through extension of semi-arid class Reworked results show 8.1 % of China s drylands to be actively degrading along time. A large proportion compared to other cases. Forthcoming activities within Dragon 3 Consolidate and document previous findings on monitoring (land trends) Refine the assessment settings (land states) in view of benchmark results and apply to whole Chine Validate the assessment using available Soil Organic Carbon data Develop a quantitative metrics targeting at land degradation level for the assessment Consolidate climatic regionalization as a project byproduct Map of land trends Basic figures: Degrading: 536,288 km 2 (8.1%) Fluctuating: 1,052,800 km 2 (15.9%) Increasing: 476,400 km 2 (7.2%) Static: 4,550,992 km 2 (68.8%)

4 ID Crop Production Estimation Food security: a big issue for current and the future CropWatch: an operational system to monitor global crop condition and forecast the main crops production. It aims at improving food information availability, quality and transparency The second version of CropWatch: the new hierarchical approach for global crop monitoring were proposed. Since Nov 2013, the quarterly bulletin had been released in English and Chinese simultaneously. Crop Watch Hierarchical Approach New algorithms: Cropped Arable Land Fraction (CALF), maximum Vegetation Condition Index (VCI), NDVI cluster analysis, crop yield model

5 New research on crop mapping and crop area estimation Crop NDVI time series were used to build the CALF (Cropped Arable Lands Fraction) algorithm for cropped land identification. CALF is based on a NDVI threshold method combined with a decision tree to identify whether an agricultural pixel is cropped or uncropped

6 ew research on crop mapping and crop area estimation for hina, Canada, Australia, Egypt, and US In China, HJ 1A/B CCD, GF 1, Envisat and TerraSar data were used for cropped land and crop type identification. Multi-frequency Radar data effectively improve the crop classification accuracy, better than that using multi-temporal data. Field border can be easily identified by integration of Radar data and optical data and a objected based classification method. HJ data, SVM Merged data, SVM Merged data, MLC Merged data, NET HJ CCD+ Envisat wheat cotton House & road tree Objected classification- Multi-times Segment method

7 Project ID Crop Monitoring Study areas North China Plain (China) Northern France and Belgium(Europe) Satellite Data Processing FY3-MERSI (FY-3A/3B/3C Daily) China Since 2009 Europe Since 2013 ENVISAT-MERIS 7d China SPOT/VEGETION Daily and tenday China PROBA-V VITO PDF FY3A MERSI 250m FY3C MERSI 250m Global Dataset FY3A MERSI 250m

8 Winter Wheat Mapping in North China Plain with FY MERSI 250m data (a)bagging (b)rbf (c)mlc

9 Medium resolution Satellite data comparison

10 Project 10448: Farmland Drought Test areas Xiaotanshang, Beijing (China) Yangling, Shaanxi, (China) 2014 wheat season data Satellite data (Radarsat-2; HJ-1) Ground data (Crop, Soil, Climate) advances analysis of quad-pol SAR data to retrieve biomass development of index to estimate Canopy Cover and Biomass by combining SAR and Optical data exploitation of Aquacrop and SAFY crop models to relate winter wheat yield to draught models sensitivity analysis for the individuation of influential parameters development of techniques to take into account RS variables into crop models i) Optimization (Particle Swarm Optimization; Simplex based Parameters Optimization ); ii) Ensemble Kalman Filter assimilation techniques Yield 2014 Yield and WUE mapping in Yangling as derived by Aquacrop biomass as the state variable in assimilation Water Use Efficiency

11 DRAGON 4 points Missing research theme Dragon4 should enable the possibility to submit proposals on transversal themes like natural resources management and Global Changes EO data Have a more easy procedure to access/order new data (possibility to have a Data Acquisition Request - DAR) for the Chinese space segment. Have the possibility to request EO data from both Agencies on Chinese and not Chinese (worldwide) test sites Sentinels synergy with future TPM (ENMAP, PRISMA, CSK etc) is missing

12 Young scientist program Young researchers exchange has to be fully supported. o Both Agencies should support a full PhD position in China and in EU for each project. Support PhD visiting period both in Europe and China o Support short term mobility of the researchers (e.g. an extra journey a year) to facilitate the progress of the activities carried on by the Chinese and the EU partners Project dimension Project dimension should be medium-small as well as for Dragon3