Exploring the potential of crop specific green area index time series to improve yield estimation at regional scale

G. Duveiller, A.J.W. de Wit, K.E. Kouadio, B. Djaby, Y. Curnel, B. Tychon, P. Defourny

Research output: Contribution to conferenceConference paperAcademic

Abstract

Crop status, such as the Green Area Index (GAI), can be retrieved from satellite observations by modelling and inverting the radiative transfer within the canopy. Providing such information along the growing season can potentially improve crop growth modelling and yield estimation. However, such approaches have proven difficult to apply on coarse resolution satellite data due to the fragmented land cover in many parts of the World. Advances in operational crop mapping will sooner or later allow the production of crop maps relatively early in the crop growth season, thereby providing an opportunity to sample pixels from medium/coarse spatial resolution data with relatively high cover fraction of a particular crop type to derive crop specific GAI time series. This research explores how to use such time series derived from MODIS to produce indicators of crop yield using two approaches over part of Belgium. The first method consists in looking at metrics of the decreasing part of the GAI curves when senescence occurs. Such metrics, like the position of the inflexion point, have been shown to be significantly correlated to yield. The second approach is to optimize the WOFOST model used in the European Crop Growth Monitoring System (CGMS) based on the GAI time series. Results show that, although the optimized model shows considerably better performance than the model running on the default parameter, the model is sometimes outperformed by the simpler metric approach. In all cases, indicators including remote sensing information provide better estimates that the average yield of previous years.
Original languageEnglish
Publication statusPublished - 2010
EventRAQRSIII -
Duration: 27 Sep 20101 Oct 2010

Conference

ConferenceRAQRSIII
Period27/09/101/10/10

Fingerprint

time series
crop
growth modeling
index
senescence
crop yield
monitoring system
MODIS
radiative transfer
satellite data
pixel
land cover
spatial resolution
growing season
canopy
remote sensing
modeling
indicator

Cite this

Duveiller, G., de Wit, A. J. W., Kouadio, K. E., Djaby, B., Curnel, Y., Tychon, B., & Defourny, P. (2010). Exploring the potential of crop specific green area index time series to improve yield estimation at regional scale. Paper presented at RAQRSIII, .
Duveiller, G. ; de Wit, A.J.W. ; Kouadio, K.E. ; Djaby, B. ; Curnel, Y. ; Tychon, B. ; Defourny, P. / Exploring the potential of crop specific green area index time series to improve yield estimation at regional scale. Paper presented at RAQRSIII, .
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abstract = "Crop status, such as the Green Area Index (GAI), can be retrieved from satellite observations by modelling and inverting the radiative transfer within the canopy. Providing such information along the growing season can potentially improve crop growth modelling and yield estimation. However, such approaches have proven difficult to apply on coarse resolution satellite data due to the fragmented land cover in many parts of the World. Advances in operational crop mapping will sooner or later allow the production of crop maps relatively early in the crop growth season, thereby providing an opportunity to sample pixels from medium/coarse spatial resolution data with relatively high cover fraction of a particular crop type to derive crop specific GAI time series. This research explores how to use such time series derived from MODIS to produce indicators of crop yield using two approaches over part of Belgium. The first method consists in looking at metrics of the decreasing part of the GAI curves when senescence occurs. Such metrics, like the position of the inflexion point, have been shown to be significantly correlated to yield. The second approach is to optimize the WOFOST model used in the European Crop Growth Monitoring System (CGMS) based on the GAI time series. Results show that, although the optimized model shows considerably better performance than the model running on the default parameter, the model is sometimes outperformed by the simpler metric approach. In all cases, indicators including remote sensing information provide better estimates that the average yield of previous years.",
author = "G. Duveiller and {de Wit}, A.J.W. and K.E. Kouadio and B. Djaby and Y. Curnel and B. Tychon and P. Defourny",
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Duveiller, G, de Wit, AJW, Kouadio, KE, Djaby, B, Curnel, Y, Tychon, B & Defourny, P 2010, 'Exploring the potential of crop specific green area index time series to improve yield estimation at regional scale' Paper presented at RAQRSIII, 27/09/10 - 1/10/10, .

Exploring the potential of crop specific green area index time series to improve yield estimation at regional scale. / Duveiller, G.; de Wit, A.J.W.; Kouadio, K.E.; Djaby, B.; Curnel, Y.; Tychon, B.; Defourny, P.

2010. Paper presented at RAQRSIII, .

Research output: Contribution to conferenceConference paperAcademic

TY - CONF

T1 - Exploring the potential of crop specific green area index time series to improve yield estimation at regional scale

AU - Duveiller, G.

AU - de Wit, A.J.W.

AU - Kouadio, K.E.

AU - Djaby, B.

AU - Curnel, Y.

AU - Tychon, B.

AU - Defourny, P.

PY - 2010

Y1 - 2010

N2 - Crop status, such as the Green Area Index (GAI), can be retrieved from satellite observations by modelling and inverting the radiative transfer within the canopy. Providing such information along the growing season can potentially improve crop growth modelling and yield estimation. However, such approaches have proven difficult to apply on coarse resolution satellite data due to the fragmented land cover in many parts of the World. Advances in operational crop mapping will sooner or later allow the production of crop maps relatively early in the crop growth season, thereby providing an opportunity to sample pixels from medium/coarse spatial resolution data with relatively high cover fraction of a particular crop type to derive crop specific GAI time series. This research explores how to use such time series derived from MODIS to produce indicators of crop yield using two approaches over part of Belgium. The first method consists in looking at metrics of the decreasing part of the GAI curves when senescence occurs. Such metrics, like the position of the inflexion point, have been shown to be significantly correlated to yield. The second approach is to optimize the WOFOST model used in the European Crop Growth Monitoring System (CGMS) based on the GAI time series. Results show that, although the optimized model shows considerably better performance than the model running on the default parameter, the model is sometimes outperformed by the simpler metric approach. In all cases, indicators including remote sensing information provide better estimates that the average yield of previous years.

AB - Crop status, such as the Green Area Index (GAI), can be retrieved from satellite observations by modelling and inverting the radiative transfer within the canopy. Providing such information along the growing season can potentially improve crop growth modelling and yield estimation. However, such approaches have proven difficult to apply on coarse resolution satellite data due to the fragmented land cover in many parts of the World. Advances in operational crop mapping will sooner or later allow the production of crop maps relatively early in the crop growth season, thereby providing an opportunity to sample pixels from medium/coarse spatial resolution data with relatively high cover fraction of a particular crop type to derive crop specific GAI time series. This research explores how to use such time series derived from MODIS to produce indicators of crop yield using two approaches over part of Belgium. The first method consists in looking at metrics of the decreasing part of the GAI curves when senescence occurs. Such metrics, like the position of the inflexion point, have been shown to be significantly correlated to yield. The second approach is to optimize the WOFOST model used in the European Crop Growth Monitoring System (CGMS) based on the GAI time series. Results show that, although the optimized model shows considerably better performance than the model running on the default parameter, the model is sometimes outperformed by the simpler metric approach. In all cases, indicators including remote sensing information provide better estimates that the average yield of previous years.

M3 - Conference paper

ER -

Duveiller G, de Wit AJW, Kouadio KE, Djaby B, Curnel Y, Tychon B et al. Exploring the potential of crop specific green area index time series to improve yield estimation at regional scale. 2010. Paper presented at RAQRSIII, .