Boschetti, Mirco

Busetto, Lorenzo

Mishra, B.

Nelson, A.D.

English

Rice is the most important staple crop in the Philippines. Like many other rice producing countries in Asia, it is cultivated in all regions of the country. A diversity of rice varieties, management practices and environments lead to a large variation in seasonality (when rice is grown) and cropping intensity (how often rice is grown per year in the same plot of land, once, twice or even three times per year). Seasonality and intensity also change over time depending on climatic, environmental and economic factors. Detecting where and when these changes occur can provides information on where and how rapidly practices have changed over time and why. Remote sensing offers a unique opportunity to detect these changes and trends with methods that can account for spatial and temporal variability. We applied the PhenoRice algorithm to 14 years of moderate resolution remote sensing (MODIS) data (utilizing 250m resolution 16 days composites from Terra and Aqua) over the Philippines to detect rice growing areas, Start and End dates (SoS and EoS) and Length (LoS) of each season. The detected intensity and seasonality were compared to published data – static provincial level crop calendars - to check that the PhenoRice detections were valid. Then, to generate statistically robust estimates of trends in seasonality we averaged the SoS and EoS of each detected rice season within a 10km hexagonal tessellation over the entire country and mapped the spatial patterns of trends in SoS, EoS and LoS. We observed a general trend towards shorter LoS in both wet and dry seasons, with seasons reducing by as much as one day per year over the 14 years period. The trends in SoS and EoS were much more variable over the country, with some regions showing trends of earlier SoS and EoS and others later. The strong spatial clustering of these trends suggests that they are influenced by local factors such as adoption of new management practices, changes in irrigation water release dates and weather. We discuss the spatial patterns in relation to these factors and describe how this approach could be scaled up to detect trends in rice seasonality and intensity across all of rice growing Asia

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ESA UNCLASSIFIED - For Official UseSpacial Explicit Rice crop calendar database from 14 Years of MODIS Imagery: patterns and trend in rice seasonality preliminary resultsMishra B., Nelson A.Busetto L., Boschetti M.Framework• Why Rice• Importance of information on crop seasonal dynamicsBackground• Time series analysis• PhenoRice approach Use of MODIS data for rice monitoring• Mapping results• Multi-year analysis : first test in SenegalAn Asia wide Rice Calendar for Asia (RICA) database• Maps• Preliminary results of trend analysisPresentation outlineRice is the world’s most important staple crop. It is the second largest crop in terms of harvested area after wheat, but is by far the most important in terms of human consumption (FAOSTAT 2012). Why Rice ? FrameworkPrice controlSustainable developmentFood security Changes in rice production and availability can cause food crises and grain price variation  Meeting future increases in demand for rice is challenged by increasing climatic variability which limits yields especially in developing countries.  Sustainable production should meet GHG emission and water use taking into account local cultural importance20 countries in Asia produce90% (654Mt) of global production 87% (142Mha) of the areaWhy do we need rice crop calendars?  Sustainable and climate smart production requires the adoption of practices that are tuned to local conditions. Appropriate planting (or transplanting) and harvesting dates are part of these practices. Knowledge of current dates is required to establish a baseline of current practices and to propose improved practices such as earlier planting or the adoption short duration or stress tolerant varieties to reduce the risk of yield reduction or losses during the season.  Similarly, knowledge of these dates is important for crop monitoring and early warning systems that provide policy related information for national and regional food security.  Date information is fundamental for modelling the exposure of the rice crop to biotic and abiotic stresses and crop growth simulation modelling.FrameworkWhy do we need RS based calendars?46 4646182 182199Laborte et al. 2017RiceAtlasDry seasonWet seasonBusetto et al. 2019MODIS based SoSDryDryBusetto et al. 2018S1 based 2016 SoSDry seasonWet seasonSenegalRICA - RIce Calendar for Asia Exploiting available rice phenology algorithm (PhenoRice) and multi-year MODIS data to generate Planting (SoS) and Harvesting (EoS) date estimates for major rice growing countries of Asia Methodological solution to automatically handle pixel based and continuous season detection to generate a spatial explicit and “consistent” database (RICA) at regular spatial aggregation scale (e.g. hexagon grid) Validate the approach by analyzing mapping spatial patterns and comparing values with available reference data Investigate multi-year dynamics (preliminary results)ObjectiveBackgroundRice identification and dynamics: PhenoriceStarting from the work of Xiao et al. (2005,2006) , CNR-IREA and IRRI developed a tool for monitoring rice crop seasonal dynamics (Boschetti et al 2017)Phenorice 1) detects rice cultivated areas and 2) analyses their phenology and agro-practices. MethodsThe method exploits a rule based method which identifies a pixel as a rice crop when 1) a clear and unambiguous flood condition is detected2) a clear crop growth signal is recognized. NDFITemperateTropicalSingle season Double season Triple season 0 10 20 30 40 50 60 70-200020006000yearEVI+ +++ + +++++ ++Vietnam0 10 20 30 40 50 60 70-200020006000yearEVI++++Bangladesh10152025303540450 10 20 30 40 50 60 70-200020006000yearEVI++++ ++ ++ +Philippines0 10 20 30 40 50 60 70-200020006000yearEVI +++ +Benin0 10 20 30 40 50 60 70-200020006000yearEVI+++ +Turkey0 10 20 30 40 50 60 70-200020006000yearEVI++++SpainLat[°]0 10 20 30 40 50 60 70-200020006000yearEVI+++++0 10 20 30 40 50 60 70-200020006000yearEVI ++++ +China0 10 20 30 40 50 60 70-200020006000yearEVI+ ++++IndiaEgyptWinter crop and summer RiceLegendEVINDWIb2.b6NDFIb1.b5NDWIb1.b6NDFIb1.b7Cloud contamination (B3 >0.18)MOD35 Cloud (State flags MOD09A1)!( !(!(!(!( !(!(!(!(!(!(!(MADBRAUSAVNMPHLBGDCHNBENEGYTURESP170°0'0"E170°0'0"E130°0'0"E130°0'0"E90°0'0"E90°0'0"E50°0'0"E50°0'0"E10°0'0"E10°0'0"E30°0'0"W30°0'0"W70°0'0"W70°0'0"W110°0'0"W110°0'0"W160°0'0"W160°0'0"W90°0'0"70°0'0"N50°0'0"N30°0'0"N10°0'0"N0°0'0"30°0'0"S30°0'0"S70°0'0"S70°0'0"S++++FloodingCrop establishmentEmergenceHeadingMaturityRice ecology challengeRice SoSSitesPhenorice results and performanceOverall: Y = 0.91 X + 8.8;   r^2 = 0.97; ME =  -2.6; MAE = 9.2Dry:       Y = 0.67 X + 24.1; r^2 = 0.63; ME =    2.4; MAE = 5.6Wet:      Y = 0.5   X + 105;  r^2 = 0.24; ME = -10.2; MAE = 14.9INDPHLPHLSNSNRICAInput DATAMODIS products (LP DAAC)• 250 m  16 day composites VI products (MOD13Q1 MYD13Q1) • 1km 8 days LST composite (MOD11A2 v.005)42 Tiles x 14 years = 27,048 imageReference dataRice Atlas (Laborte, 2017)MethodStudy area and input dataDownload MODIS timeseries [42 tiles × 14 years]Pixel level identification of rice SoS and EoS dates with PhenoRiceSpatial aggregation of SoSand EoS informationSeason identification per spatial unitFrequency histogram of SoS and EoS per hexagon*PhenoRicealgorithm and  cloud screeningGaussian fitting of number and timing of seasons  MODIS tiles (42)EVI smoothNDFISoS EmergenceCloud00.10.20.30.40.50.60.70.80.91-200 -100 0 100 200 300 400VI [-]DOYEVI rawPoS EoSFrom pixel detection to RICA databaseMethodFrom pixel detection to RICA databaseProblem: - How to aggregate single pixel detection?- How to define «seasons»?- How many?- When start end?Mixtools analysisAutomated statistical technique to identify “main modes” of the distribution of SoSand EoS Rice SesonsRule 1: agronomic criteria minimum time between two consecutive rice crops (> 80 days)Rule 2: > 2% of the total number of rice detections within the spatial unitRule 3: number of models for SoSmust equal the number for EoSNOOKOKMethodSeason identification and validationMixtoolmodel resultsCHN_HeilongjiangCHN_GuangdongIDN_Nusa_Tenggara_TimurIND_Gujarat_AnandVNM_South_East_Binh_ThuanIDN_Irian_Jaya_BaratResultsPlanting datesDetection between: - 35 to 60 doyDetection between: 61 to 150 doyPlanting datesDetection between: 151 to  240 doyPlanting datesDetection between: 241 to 330 doyPlanting datesSeason 4Season 2Season 1Season 3Harvesting datesProduct assessmentComparison of the planting and harvesting dates between RICA and RiceAtlas.Each circle indicates a single (sub) region; the area of the circle is the log10 value of the rice area in that (sub) region corresponding to RiceAtlas.SoS EoSMAE = 28.8R^2 = 0.80MAE = 35.2R^2 = 0.81Multi annual dinamycs: preliminaryWork in progressAssign each detection to RICA season using Bayes Decision Boundaries methodTimeseries MODIS dataPhenoRice ProcessingTimeseries SoS, EoSSpatial ExtensionHexagonal tessellation  Average seasons for each unit defined by RICACompute annual statistics of SoS and EoS for each season using a threshold in all hexagonsAssumption:• Number of rice pixels in 14 years should be GT 200 in a hexagon to compute SoS and EoS.• Difference between the SoS of two seasons should be more than 80 days.• At least seven estimates in the timeseries of SoS(EoS) is necessary to compute the trend for each season in a hexagon.Data processing work flowTrend  and p-value computation using linear regression methodPreliminary resultsPHL – Nueva Ecija (delay in sowing?) Orissa (ongoing changes due to monsoon rainfall anomalies ?)Preliminary resultsTrendSign.RICARiceAtlas< -2> 3Gummaet al 2015Asia wide rice crop calendar We process 2003 – 2016 MODIS timeseries with PhenoRicealgorithm to generate pixel level SoS and EoS maps Appropriate data handling was developed to generate regular hexagon crop calendar by spatially and temporally averaging multi-year pixel level planting (SoS) and harvesting (EoS) date estimates RICA V0 First Asia wide HR rice crop calendar  reflects local spatial variations Provides multi season information for each unit @administrative level is in agreement with Rice Atlas  The dataset is under analysis in collaboration with IRRI to perform expert base assessment to validate it at local scale identify potential artefacts Perform further refinement (mask and specific region rules)Conclusions and Future WorkMulti-year analysis 2003 -2016 hexagon level estimates can be exploit for multi-year analysis A preliminary methodology was implemented to attribute each detection to the more plausible crop season (need to trend analysis)  The dataset generated can be of help to identify rice dynamics in relation to land use changes (crop intensification, expansion…) consequence of agro-practices (e.g. varietal changes)  effect of climate/enviromental process (e.g. rainfeed monsoon) Hot spot of anomalous behaviour  Assessment/validation undergoing Exploitation of expert knowledge (collaboration with IRRI) Investigation of publication and grey litteratureConclusions and Future WorkThanks for your attention !

Analysing Trends in Rice Cropping Intensity and Seasonality Across the Philippines using 14 Years of MODIS Imagery:powerpoint

https://ris.utwente.nl/ws/files/120016158/Boschetti_LPS_2019_V5_H.pdf

Analysing Trends in Rice Cropping Intensity and Seasonality Across the Philippines using 14 Years of MODIS Imagery:powerpoint

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