In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.
Quantitative remote sensing inversion is ill-posed.The Moderate Resolution Imaging Spectroradiometer at 250 m resolution(MODIS_250m) contains two bands.To deal with this ill-posed inversion of MODIS_250m data,we propose a framework,the Multi-scale,Multi-stage,Sample-direction Dependent,Target-decisions(Multi-scale MSDT) inversion method,based on spa-tial knowledge.First,MODIS images(1 km,500 m,250 m) are used to extract multi-scale spatial knowledge.The inversion accuracy of MODIS_1km data is improved by reducing the impact of spatial heterogeneity.Then,coarse-scale inversion is taken as prior knowledge for the fine scale,again by inversion.The prior knowledge is updated after each inversion step.At each scale,MODIS_1km to MODIS_250m,the inversion is directed by the Uncertainty and Sensitivity Matrix(USM),and the most uncertain parameters are inversed by the most sensitive data.All remote sensing data are involved in the inversion,during which multi-scale spatial knowledge is introduced,to reduce the impact of spatial heterogeneity.The USM analysis is used to implement a reasonable allocation of limited remote sensing data in the model space.In the entire multi-scale inversion process,field data,spatial knowledge and multi-scale remote sensing data are all involved.As the multi-scale,multi-stage inversion is gradually refined,initial expectations of parameters become more reasonable and their uncertainty range is effectively reduced,so that the inversion becomes increasingly targeted.Finally,the method is tested by retrieving the Leaf Area Index(LAI) of the crop canopy in the Heihe River Basin.The results show that the proposed method is reliable.
Ecosystem service is an emerging concept that grows to be a hot research area in ecology.Spatially explicit ecosystem service values are important for ecosystem service management.However,it is difficult to quantify ecosystem services.Remote sensing provides images covering Earth surface,which by nature are spatially explicit.Thus,remote sensing can be useful for quantitative assessment of ecosystem services.This paper reviews spatially explicit ecosystem service studies conducted in ecology and remote sensing in order to find out how remote sensing can be used for ecosystem service assessment.Several important areas considered include land cover,biodiversity,and carbon,water and soil related ecosystem services.We found that remote sensing can be used for ecosystem service assessment in three different ways:direct monitoring,indirect monitoring,and combined use with ecosystem models.Some plant and water related ecosystem services can be directly monitored by remote sensing.Most commonly,remote sensing can provide surrogate information on plant and soil characteristics in an ecosystem.For ecosystem process related ecosystem services,remote sensing can help measure spatially explicit parameters.We conclude that acquiring good in-situ measurements and selecting appropriate remote sensor data in terms of resolution are critical for accurate assessment of ecosystem services.
