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Multisensor crop yield estimation with Machine Learning
Laura Martínez-Ferrer, Maria Piles, Gustau Camps-Valls
http://isp.uv.es @isp_uv_es [email protected]
Machine Learning for Earth System Modelling
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Motivation
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Population
Food
Adverse weather❏ Satellite (Optical & Microwave)
❏ Meteo
❏ Crop proportion
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Goals
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❏ Developing an automatic system for crop yield estimation and prediction
❏ Machine Learning approach to exploit synergies of satellite and meteorological data
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Data collection
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❏ Survey data: USDA (county-scale)❏ Crop yield ❏ Proportion of each crop planted (CP)
❏ Products:❏ EVI: MOD13C1, 0.05º, 16 days ❏ VOD, SM: SMAP, 9km, 3 days ❏ TMAX, P: DAYMET, 1km, monthly
❏ Study area: CONUS (35 states)❏ Years of data: 2015-2018 (growing season Apr-Oct)❏ Main Crops:
❏ Corn (1744 counties)❏ Soy (2060 counties)❏ Wheat (1036 counties)
Only satellite data from pure croplands pixels included in the experiment (following MODIS-IGBP land cover)
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Methodology
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❏ N...counties (~103)❏ T...observations (~103)❏ y...target variable (total yield)
Poorly conditioned matrices Problem
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Methodology❏ Machine Learning Methods:
❏ Linear Regression❏ Random Forest❏ Gaussian Processes
❏ Approaches:❏ Individual (each variable)❏ Global (combination of variables)
❏ Cross validation:
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LR
RF
GP
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Results from individual approach
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❏ Nonlinear methods (GP, RF) perform better: GP leads to the highest scores
❏ EVI, VOD, and SM are all capturing relevant information to estimate crop yield
❏ TMAX high relation to crop growth in corn/soy is confirmed (highest scores)
❏ SM leads the feature ranking while CP seems to have less relevance
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Global experiments
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1 2 3 4 5
EVI + VOD EVI + VOD + SM(SAT)
TMAX + P(METEO)
SAT + METEO SAT + METEO + CP
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Results
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❏ Best solution for all crops obtained with non-linear methods & all input features:
R (0.84-0.97) and RMSE (0.51-1.2 t/ha)
❏ CP makes a difference in the global experiments
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Estimation maps (Corn)
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Error (%)
Corn yield (t/ha) Corn yield estimation (t/ha)
❏ Most error is concentrated in only a few counties
❏ Overall, good results in terms of R, RMSE and relative error
2017
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Estimation maps (Soy)
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Error (%)
Soy yield (t/ha) Soy yield estimation (t/ha)
2017
❏ Most error is concentrated in only a few counties
❏ Overall, good results in terms of R, RMSE and relative error
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Estimation maps (Wheat)
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Error (%)
Wheat yield (t/ha) Wheat yield estimation (t/ha)
2017
❏ Higher geographic variability❏ Overall, good results in terms of R,
RMSE and relative error
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Conclusions❏ The proposed ML framework for crop yield estimation works:
❏ method: non-linear regression using GPs or RF❏ input features: time series of data summarized using PCA & proportion of planted crop CP
❏ Corn, soy and wheat estimated over CONUS R (0.84-0.97) and RMSE (0.51-1.2 t/ha)
❏ GPs allow ranking the input features: ❏ Satellite data (optical, microwaves) lead the ranking, followed by meteorological ❏ CP makes a difference in global experiments, while ranking is lowest in individual experiments
❏ Best results for Corn & Soy: higher number of observations available, concentrated in US Corn Belt
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References
❏ A. Mateo-Sanchis et al., Synergistic integration of optical and microwave satellite data for crop yield estimation, RSE (2019)
❏ D. Chaparro et al., L-band vegetation optical depth seasonal metrics for crop yield assessment, RSE (2018)
❏ Konings, A., Piles, M., et al., Vegetation optical depth and scattering albedo retrieval using time series of dualpolarized L-band radiometer observations, RSE (2016)
❏ G. Camps-Valls et al., A survey on gaussian processes for earth observation data analysis, IEEE GRS Magazine (2016)
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Thanks!
Laura Martínez-Ferrer, Maria Piles, Gustau Camps-Valls
http://isp.uv.es @isp_uv_es [email protected]
Machine Learning for Earth System Modelling
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