Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts

A.J.W. de Wit, C.A. van Diepen

Research output: Contribution to journalArticleAcademicpeer-review

168 Citations (Scopus)

Abstract

Uncertainty in spatial and temporal distribution of rainfall in regional crop yield simulations comprises a major fraction of the error on crop model simulation results. In this paper we used an Ensemble Kalman filter (EnKF) to assimilate coarse resolution satellite microwave sensor derived soil moisture estimates (SWI) for correcting errors in the water balance of the world food studies (WOFOST) crop model. Crop model simulations with the EnKF for winter wheat and grain maize were carried out for Spain, France, Italy and Germany for the period 1992¿2000. The results were evaluated on the basis of regression with known crop yield statistics at national and regional level. Moreover, the EnKF filter innovations were analysed to see if any systematic trends could be found that could indicate deficiencies in the WOFOST water balance. Our results demonstrate that the assimilation of SWI has clearly improved the relationship with crop yield statistics for winter wheat for the majority of regions (66%) where a relationship could be established. For grain maize the improvement is less evident because improved relationships could only be found for 56% of the regions. We suspect that partial crop irrigation could explain the relatively poor results for grain maize, because irrigation is not included in the model. Analyses of the filter innovations revealed spatial and temporal patterns, while the distribution of normalised innovations is not Gaussian and has a non-zero mean indicating that the EnKF performs suboptimal. The non-zero mean is caused by differences in the mean value of the forecasted and observed soil moisture climatology, while the excessive spread in the distribution of normalised innovations indicates that the error covariances of forecasts and observations have been underestimated. These results clearly indicate that additional sources of error need to be included in the simulations and observations.
Original languageEnglish
Pages (from-to)38-56
JournalAgricultural and Forest Meteorology
Volume146
Issue number1-2
DOIs
Publication statusPublished - 2007

Fingerprint

crop models
Kalman filter
data assimilation
crop yield
innovation
water balance
crop
winter wheat
corn
maize
statistics
soil water
irrigation
simulation
water budget
soil moisture
wheat
filter
food
winter

Keywords

  • remote-sensing data
  • soil-moisture
  • spatial variability
  • ers scatterometer
  • united-states
  • scales
  • parameters
  • wheat
  • reflectances
  • uncertainty

Cite this

@article{3d37e99d993b41b080ee5b45c5e61866,
title = "Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts",
abstract = "Uncertainty in spatial and temporal distribution of rainfall in regional crop yield simulations comprises a major fraction of the error on crop model simulation results. In this paper we used an Ensemble Kalman filter (EnKF) to assimilate coarse resolution satellite microwave sensor derived soil moisture estimates (SWI) for correcting errors in the water balance of the world food studies (WOFOST) crop model. Crop model simulations with the EnKF for winter wheat and grain maize were carried out for Spain, France, Italy and Germany for the period 1992¿2000. The results were evaluated on the basis of regression with known crop yield statistics at national and regional level. Moreover, the EnKF filter innovations were analysed to see if any systematic trends could be found that could indicate deficiencies in the WOFOST water balance. Our results demonstrate that the assimilation of SWI has clearly improved the relationship with crop yield statistics for winter wheat for the majority of regions (66{\%}) where a relationship could be established. For grain maize the improvement is less evident because improved relationships could only be found for 56{\%} of the regions. We suspect that partial crop irrigation could explain the relatively poor results for grain maize, because irrigation is not included in the model. Analyses of the filter innovations revealed spatial and temporal patterns, while the distribution of normalised innovations is not Gaussian and has a non-zero mean indicating that the EnKF performs suboptimal. The non-zero mean is caused by differences in the mean value of the forecasted and observed soil moisture climatology, while the excessive spread in the distribution of normalised innovations indicates that the error covariances of forecasts and observations have been underestimated. These results clearly indicate that additional sources of error need to be included in the simulations and observations.",
keywords = "remote-sensing data, soil-moisture, spatial variability, ers scatterometer, united-states, scales, parameters, wheat, reflectances, uncertainty",
author = "{de Wit}, A.J.W. and {van Diepen}, C.A.",
year = "2007",
doi = "10.1016/j.agrformet.2007.05.004",
language = "English",
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pages = "38--56",
journal = "Agricultural and Forest Meteorology",
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}

Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. / de Wit, A.J.W.; van Diepen, C.A.

In: Agricultural and Forest Meteorology, Vol. 146, No. 1-2, 2007, p. 38-56.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts

AU - de Wit, A.J.W.

AU - van Diepen, C.A.

PY - 2007

Y1 - 2007

N2 - Uncertainty in spatial and temporal distribution of rainfall in regional crop yield simulations comprises a major fraction of the error on crop model simulation results. In this paper we used an Ensemble Kalman filter (EnKF) to assimilate coarse resolution satellite microwave sensor derived soil moisture estimates (SWI) for correcting errors in the water balance of the world food studies (WOFOST) crop model. Crop model simulations with the EnKF for winter wheat and grain maize were carried out for Spain, France, Italy and Germany for the period 1992¿2000. The results were evaluated on the basis of regression with known crop yield statistics at national and regional level. Moreover, the EnKF filter innovations were analysed to see if any systematic trends could be found that could indicate deficiencies in the WOFOST water balance. Our results demonstrate that the assimilation of SWI has clearly improved the relationship with crop yield statistics for winter wheat for the majority of regions (66%) where a relationship could be established. For grain maize the improvement is less evident because improved relationships could only be found for 56% of the regions. We suspect that partial crop irrigation could explain the relatively poor results for grain maize, because irrigation is not included in the model. Analyses of the filter innovations revealed spatial and temporal patterns, while the distribution of normalised innovations is not Gaussian and has a non-zero mean indicating that the EnKF performs suboptimal. The non-zero mean is caused by differences in the mean value of the forecasted and observed soil moisture climatology, while the excessive spread in the distribution of normalised innovations indicates that the error covariances of forecasts and observations have been underestimated. These results clearly indicate that additional sources of error need to be included in the simulations and observations.

AB - Uncertainty in spatial and temporal distribution of rainfall in regional crop yield simulations comprises a major fraction of the error on crop model simulation results. In this paper we used an Ensemble Kalman filter (EnKF) to assimilate coarse resolution satellite microwave sensor derived soil moisture estimates (SWI) for correcting errors in the water balance of the world food studies (WOFOST) crop model. Crop model simulations with the EnKF for winter wheat and grain maize were carried out for Spain, France, Italy and Germany for the period 1992¿2000. The results were evaluated on the basis of regression with known crop yield statistics at national and regional level. Moreover, the EnKF filter innovations were analysed to see if any systematic trends could be found that could indicate deficiencies in the WOFOST water balance. Our results demonstrate that the assimilation of SWI has clearly improved the relationship with crop yield statistics for winter wheat for the majority of regions (66%) where a relationship could be established. For grain maize the improvement is less evident because improved relationships could only be found for 56% of the regions. We suspect that partial crop irrigation could explain the relatively poor results for grain maize, because irrigation is not included in the model. Analyses of the filter innovations revealed spatial and temporal patterns, while the distribution of normalised innovations is not Gaussian and has a non-zero mean indicating that the EnKF performs suboptimal. The non-zero mean is caused by differences in the mean value of the forecasted and observed soil moisture climatology, while the excessive spread in the distribution of normalised innovations indicates that the error covariances of forecasts and observations have been underestimated. These results clearly indicate that additional sources of error need to be included in the simulations and observations.

KW - remote-sensing data

KW - soil-moisture

KW - spatial variability

KW - ers scatterometer

KW - united-states

KW - scales

KW - parameters

KW - wheat

KW - reflectances

KW - uncertainty

U2 - 10.1016/j.agrformet.2007.05.004

DO - 10.1016/j.agrformet.2007.05.004

M3 - Article

VL - 146

SP - 38

EP - 56

JO - Agricultural and Forest Meteorology

JF - Agricultural and Forest Meteorology

SN - 0168-1923

IS - 1-2

ER -