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

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ESA UNCLASSIFIED - For Official Use

Spacial Explicit Rice crop calendar database from 14 Years of MODIS Imagery: patterns and trend in rice

seasonality preliminary results

Mishra B., Nelson A.

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Framework • Why Rice

• Importance of information on crop seasonal dynamics Background

• Time series analysis

• PhenoRice approach

Use of MODIS data for rice monitoring • Mapping results

• Multi-year analysis : first test in Senegal

An Asia wide Rice Calendar for Asia (RICA) database • Maps

• Preliminary results of trend analysis

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Rice 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 ?

Framework

Price control Sustainable development Food 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

importance

20 countries in Asia produce 90% (654Mt) of global production 87% (142Mha) of the area

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Why 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.

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Why do we need RS

based calendars?

46 46 46 182 182 199 Laborte et al. 2017 RiceAtlas Dry season Wet season Busetto et al. 2019 MODIS based SoS Dry Dry Busetto et al. 2018 S1 based 2016 SoS Dry season Wet season Senegal

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RICA - 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)

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Rice identification and dynamics: Phenorice

Starting 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.

Methods

The method exploits a rule

based method which identifies a pixel as a rice crop when

1) a clear and unambiguous flood condition is detected

2) a clear crop growth signal is

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Temperate

Tropical

Single season Double season Triple season

0 10 20 30 40 50 60 70 -2 0 0 0 2000 6000 year EVI + ₊ + ++ ₊+ ++ + ₊ + Vietnam 0 10 20 30 40 50 60 70 -2 0 0 0 2000 6000 year EVI + ++ + Bangladesh 10 15 20 25 30 35 40 45 0 10 20 30 40 50 60 70 -2 0 0 0 2000 6000 year EVI + + + ++ ++ + + Philippines 0 10 20 30 40 50 60 70 -2 0 0 0 2000 6000 year EVI + ++ + Benin 0 10 20 30 40 50 60 70 -2 0 0 0 2000 6000 year EVI + ++ + Turkey 0 10 20 30 40 50 60 70 -2 0 0 0 2000 6000 year EVI + ++ + Spain Lat [° ] 0 10 20 30 40 50 60 70 -2 0 0 0 2000 6000 year EVI + ₊ ++ + 0 10 20 30 40 50 60 70 -2 0 0 0 2000 6000 year EVI + + ++ + China 0 10 20 30 40 50 60 70 -2 0 0 0 2000 6000 year EVI + ₊ ++ + India Egypt Winter crop and summer Rice Legend EVI NDWIb2.b6 NDFIb1.b5 NDWIb1.b6 NDFIb1.b7 Cloud contamination (B3 >0.18) MOD35 Cloud (State flags MOD09A1)

! ( !( ! ( ! ( ! ( !( ! ( ! ( ! ( ! ( ! ( ! ( MAD BRA USA VNMPHL BGD CHN BEN EGY TUR ESP 170°0'0"E 170°0'0"E 130°0'0"E 130°0'0"E 90°0'0"E 90°0'0"E 50°0'0"E 50°0'0"E 10°0'0"E 10°0'0"E 30°0'0"W 30°0'0"W 70°0'0"W 70°0'0"W 110°0'0"W 110°0'0"W 160°0'0"W 160°0'0"W 9 0 °0 '0 " 7 0 °0 '0 "N 5 0 °0 '0 "N 3 0 °0 '0 "N 1 0 °0 '0 "N 0 °0 '0 " 3 0 °0 '0 "S 3 0 °0 '0 "S 7 0 °0 '0 "S 7 0 °0 '0 "S + + + + Flooding Crop establishment Emergence Heading Maturity

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Rice SoS Sites

Phenorice results and

performance

Overall: Y = 0.91 X + 8.8; r^2 = 0.97; ME = -2.6; MAE = 9.2 Dry: Y = 0.67 X + 24.1; r^2 = 0.63; ME = 2.4; MAE = 5.6 Wet: Y = 0.5 X + 105; r^2 = 0.24; ME = -10.2; MAE = 14.9 IND PHL PHL SN SN

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Input DATA

MODIS 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 image

Reference data Rice Atlas

(Laborte, 2017)

Method

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Download MODIS timeseries [42 tiles × 14 years]

Pixel level identification of rice SoS and EoS dates with

PhenoRice

Spatial aggregation of SoS

and EoS information Season identification per spatial unit

Frequency histogram of SoS and EoS per

hexagon* PhenoRice algorithm and cloud screening Gaussian fitting of number and timing of seasons MODIS tiles (42) EVI smooth NDFI SoS Emergence Cloud 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -200 -100 0 100 200 300 400 V I [ -] DOY EVI raw PoS EoS

From pixel detection to RICA database

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From pixel detection to RICA database

Problem:

- How to aggregate single pixel detection?

- How to define «seasons»? - How many?

- When start end?

Mixtools analysis

Automated statistical technique to identify “main modes” of the distribution of SoS

and EoS  Rice Sesons

Rule 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 unit

Rule 3: number of models for SoS

must equal the number for EoS

NO OK OK

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Season identification and validation

Mix tool model resu lts CHN_Heilongjiang CHN_Guangdong IDN_Nusa_Tenggara_Timur IND_Gujarat_Anand VNM_South_East_Binh_Thuan IDN_Irian_Jaya_Barat

Results

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Planting dates

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Detection between: 61 to 150 doy

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Season 4 Season 2 Season 1

Season 3

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Product assessment

Comparison 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 EoS

MAE = 28.8 R^2 = 0.80

MAE = 35.2 R^2 = 0.81

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Work in progress

Assign each detection to RICA season using Bayes Decision

Boundaries method Timeseries MODIS data

PhenoRice Processing Timeseries SoS, EoS

Spatial Extension Hexagonal tessellation

Average seasons for each unit defined by RICA

Compute annual statistics of SoS and EoS for each season using a threshold

in all hexagons

Assumption:

• 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 flow

Trend and p-value computation using linear

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Preliminary results

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Orissa (ongoing changes due to monsoon rainfall anomalies ?)

Preliminary results

Trend Sign. RICA RiceAtlas < -2 > 3 Gumma et al 2015

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Asia wide rice crop calendar

 We process 2003 – 2016 MODIS timeseries with PhenoRice

algorithm 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 Work

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Multi-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 litterature

Conclusions and

Future Work

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