Incorporation of a soil water modifier into MODIS predictions of temperate Douglas-fir gross primary productivity: Initial model development
- 作者：Nicholas C. Coops ; Rachhpal S. Jassal ; Ray Leuning ; Andy T. Black ; Kai Morgenstern
- 关键词：Gross primary productivity ; MODIS ; Eddy covariance ; Soil water ; Radiation use efficiency ; fAPAR
- 刊名：Agricultural and Forest Meteorology
- 出版年：2007
- 期刊代码：2_01681923
- 类别：abs
- 出版时间：10 December 2007
- 卷：147
- 期：3-4
- 页码：99-109
- 文件大小：275 K
摘要
The moderate resolution imaging spectroradiometer (MODIS) is being used to monitor gross primary production (GPP), both spatially and temporally, routinely from space. However, estimates of GPP at various flux stations indicate that the MODIS algorithm may (i) over-predict GPP at sites where limitation to growth by low-soil water content is not adequately captured by the reduction in stomatal conductance by vapor pressure and (ii) under-predict GPP in highly productive, evergreen, needle leaf forests, due to a reduced radiation-use-efficiency term. The objective of this paper is to determine if any systematic bias exists in the MODIS algorithm relative to eddy covariance (EC) estimates of GPP made over an evergreen, needle leaf temperate rainforest on Vancouver Island, Canada, which is routinely water-stressed in summer months. Results indicate that 8-day GPP as predicted by the standard MODIS algorithm, with appropriate parameters for evergreen needle leaf forest, was highly correlated to EC-measured GPP (r2 = 0.89, p < 0.001, S.E. = 0.9 g C m−2 day−1), however with significant bias, under predicting GPP by as much as 30%. Increasing the radiation-use-efficiency term 
max (g C MJ−1) from the MODIS lookup value to the maximum observed at the site resulted in a reduced bias in the predicted GPP, however estimates were 8%higher than EC measurements. To account for soil water stress on plant growth, we implemented a soil water modifier initially proposed by Leuning et al. [Leuning, R., Cleugh, H., Zegelin, S., Hughes, D., 2005. Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet/dry savanna in Australia: measurements and comparison with MODIS remote sensing estimates. Agric. For. Meteorol. 129, 151–173] that accounts for rainfall and potential evaporation in the antecedent 3 months, a surrogate for soil water availability. Results confirm that field observations of relative available soil water content in the 0–60 cm layer matched the proposed soil water modifier closely with the relationship between the modified MODIS algorithm GPP and the EC-measured GPP remaining highly significant (r2 = 0.91, p < 0.001, S.E. = 1.1 g C m−2 day−1) with no significant bias. Whilst broad scale implementation of such a soil water modifier into the MODIS algorithm is still limited due to lack of rainfall data, at least in the short-term, the modifier does provide an alternative for researchers and land mangers, interested in applying the MODIS GPP products over regional areas, but who may have, or are observing, over-estimated production estimates due to the lack of inclusion of soil water modification to growth.
max (g C MJ−1) from the MODIS lookup value to the maximum observed at the site resulted in a reduced bias in the predicted GPP, however estimates were 8%higher than EC measurements. To account for soil water stress on plant growth, we implemented a soil water modifier initially proposed by Leuning et al. [Leuning, R., Cleugh, H., Zegelin, S., Hughes, D., 2005. Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet/dry savanna in Australia: measurements and comparison with MODIS remote sensing estimates. Agric. For. Meteorol. 129, 151–173] that accounts for rainfall and potential evaporation in the antecedent 3 months, a surrogate for soil water availability. Results confirm that field observations of relative available soil water content in the 0–60 cm layer matched the proposed soil water modifier closely with the relationship between the modified MODIS algorithm GPP and the EC-measured GPP remaining highly significant (r2 = 0.91, p < 0.001, S.E. = 1.1 g C m−2 day−1) with no significant bias. Whilst broad scale implementation of such a soil water modifier into the MODIS algorithm is still limited due to lack of rainfall data, at least in the short-term, the modifier does provide an alternative for researchers and land mangers, interested in applying the MODIS GPP products over regional areas, but who may have, or are observing, over-estimated production estimates due to the lack of inclusion of soil water modification to growth.