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Published on October 4, 2007

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A New Operational Tool Derived from Remotely-sensed Vegetation Metrics for Drought Monitoring and Yield Estimation :  A New Operational Tool Derived from Remotely-sensed Vegetation Metrics for Drought Monitoring and Yield Estimation Ping Zhang Department of Geography and Environment Dissertation Committee Bruce T. Anderson Ranga Myneni Mathew Barlow Nathan Phillips Curtis Woodcock Outline:  Outline Introduction Derivation of the Climate-Variability Impact Index (CVII) and its application in drought diagnosis and monitoring Modeling crop yield from CVII and its potential application in near real-time crop monitoring Application of the CVII for early crop yield forecasting in drought-stricken regions – Case studies in US Concluding remarks and future directions Backgrounds:  Backgrounds Climate control on vegetation Agriculture drought indices based on meteorological data Rainfall deficiency index Palmer’s Crop Moisture Index (CMI) (1968): U.S. Department of Agriculture Standardized Precipitation Index (SPI) (1993): National Drought Mitigation Center US drought monitor map (1999): US Drought Monitor Agriculture drought indices based on satellite data Vegetation indices from Landsat (1980s) Vegetation Condition Index (VCI) from AVHRR NDVI (1995) Standardized Vegetation Index from AVHRR NDVI (2002) Research questions Can we identify and quantify the climatic impact on vegetation at regional to global scales using remote sensing data? And can we construct an operational tool for real-time drought monitoring and early yield estimation using these data? Datasets and compilations:  Datasets and compilations Domains: Global vegetation pixels and crops in U.S. Scales: county, crop reporting district, state, country, and global Datasets: Satellite-based vegetation indices including AVHRR, MODIS LAI and NDVI Survey-based crop yield estimations from USDA NASS and FAO STAT Climate data including CMAP precipitation, NCEP temperature, and ISCCP radiation Drought indices based on meteorological records including SPI and US drought monitor maps GIS boundary maps from the National Atlas of the United States Outline:  Outline Introduction Derivation of the Climate-Variability Impact Index (CVII) and its application in drought diagnosis and monitoring Modeling crop yield from CVII and its potential application in near real-time crop monitoring Application of the CVII for early crop yield forecasting in drought-stricken regions – Case studies in US Concluding remarks and future directions Slide6:  Part One: Characteristics of MODIS vegetation indices Part One: Characteristics of MODIS vegetation indices:  Part One: Characteristics of MODIS vegetation indices Part One: Construction of a Climate Impact Index:  Part One: Construction of a Climate Impact Index Climate Impact Sensitivity Given estimates of the growing season length, as well as the annual growth, we can construct a climatological-based Climate Impact Index (CII). This index is designed to identify the monthly contribution of growing-season activity to annual production (normalized for a given land cover type), which in turn provides an estimate of the vulnerability of a particular region to climate variability during the growing season. Part One: Construction of a Climate Impact Index:  Part One: Construction of a Climate Impact Index Part One: Construction of a Climate Impact Index:  Part One: Construction of a Climate Impact Index Part One: Construction of a Climate-Variability Impact Index:  Part One: Construction of a Climate-Variability Impact Index Monitoring of Drought To see how the CII can be used for agricultural monitoring, we used AVHRR LAI to generate a Climate-Variability Impact Index (CVII). Here the CVII was designed to quantify the percentage of the climatological annual grid-point production either gained or lost due to climatic variability in a particular month. Part One: Application of CVII in drought monitoring:  Part One: Application of CVII in drought monitoring Part One: Summary:  Part One: Summary MODIS LAI can quantify spatial differences between productivity of ecosystems as well as seasonal variations within ecosystems. A Climatic Impact Index (CII) is derived to provide additional information regarding the potential sensitivity of vegetation to changes in climatic variables by accounting for the length of growing season. Major drought events are well-captured by the similarly derived Climate-Variability Impact Index (CVII). Zhang, P. et al., Climate related vegetation characteristics derived from MODIS LAI and NDVI, J. Geophys. Res., VOL. 109, 2004. Outline:  Outline Introduction Derivation of the Climate-Variability Impact Index (CVII) and its application in drought diagnosis and monitoring Modeling crop yield from CVII and its potential application in near real-time crop monitoring Application of the CVII for early crop yield forecasting in drought-stricken regions – Case studies in US Concluding remarks and future directions Part Two: the Relation between CVII and Yield Estimation:  Part Two: the Relation between CVII and Yield Estimation Correlation between CVII and Yield at Different Scales Local scales (county- and CRD-level) Representative regions: Illinois and North Dakota Representative crops: corn and spring wheat Study period: 2000-2004 Regional scales (state-level) Representative regions: 3 regions in US Representative crops: corn, spring wheat, and winter wheat Study period: 1985-1989 National scales Representative regions: 4 European countries Representative crops: winter wheat Study period: 1982-1999 Part Two: the Relation between CVII and Yield Estimation :  Part Two: the Relation between CVII and Yield Estimation Part Two: the Relation between CVII and Yield Estimation:  Part Two: the Relation between CVII and Yield Estimation Prediction of Yield with CVII Homogeneous prediction Train the model based on 2000-2003 data in Illinois Predict the corn yield of 2004 in Illinois Heterogeneous prediction Train the model based on 1985-1989 data in winter wheat regions in US Predict the winter wheat yield of 1985-1989 in 4 European countries Predict the winter wheat yield of 1982-2000 in 4 European countries Part Two: the Relation between CVII and Yield Estimation:  Part Two: the Relation between CVII and Yield Estimation Part Two: the Relation between CVII and Yield Estimation:  Part Two: the Relation between CVII and Yield Estimation Operational Tool for Agriculture Monitoring To study the relation between monthly CVII and growing-season yield, we examine the timing of the relationship between CVII and crop yields. Here, we construct a statistical model for yield: This linear equation is examined four times with different regression selection procedures. Ideal model Cumulative model Chronological model Fixed-coefficient model Part Two: Operational Tool for Agriculture Monitoring:  Part Two: Operational Tool for Agriculture Monitoring Ideal Model Provides information about which months within the phenological cycle are most strongly related to the overall yield Ideal model contains no redundant predictor and provides the best correlation between CVII and yield. Important predictors are different for various crop types and study areas Cumulative Model CVII is progressively accumulated month by month over the growing season. At any point in the growing season, the cumulative CVII represents the variations in the total growth up to that point. Coefficient will change over the growing season. Part Two: Operational Tool for Agriculture Monitoring:  Part Two: Operational Tool for Agriculture Monitoring Chronological Model CVII series for each month of the growing season are added in the model chronologically. Coefficients will change with each subsequent CVII value added during the course of the growing season Fixed-coefficients Model Additional predictor variable is regressed with the residual of Y, instead of Y itself. Calculates partial F statistic to test whether the addition of one particular predictor variable adds significantly to the prediction of Y achieved using the pre-existing predictor variables. Part Two: Operational Tool for Agriculture Monitoring:  Part Two: Operational Tool for Agriculture Monitoring Evolution of the R-square of the models as a function of the number of predictors. Part Two: Operational Tool for Agriculture Monitoring:  Part Two: Operational Tool for Agriculture Monitoring Part Two: the Relation between CVII and Yield Estimation:  A quantitative index, Climate-Variability Impact Index, is introduced to study the relationship between the remotely-sensed data and crop yields. Integrated CVII is highly correlated with crop yields at different scales. Based on this correlation, models can be applied to produce homogeneous and heterogeneous yield forecasts. single-crop CVII-production relationship may be quasi-independent of location CVII-production relationship appears to be crop-independent for certain crop types CVII can be exploited for crop growth monitoring by investigating the timing of the relationship between the CVII and crop yields. Zhang, P. et al., Potential monitoring of crop yield using a satellite-based climate-variability impact index , Agricultural and Forest Meteorology,132,344-358, 2005. Part Two: the Relation between CVII and Yield Estimation Outline:  Outline Introduction Derivation of the Climate-Variability Impact Index (CVII) and its application in drought diagnosis and monitoring Modeling crop yield from CVII and its potential application in near real-time crop monitoring Application of the CVII for early crop yield forecasting in drought-stricken regions – Case studies in US Concluding remarks and future directions Part Three: Application of CVII at US :  Part Three: Application of CVII at US Extreme drought occurred in 2005 over Illinois the April-September rainfall ranked 10th lowest in the past 113 years [NCDC] US drought in 2006 centered in North and South Dakota the persistence of anomalous warmth made the summer the second warmest June-August period in the continental US in the past 110 years combined with the below-average precipitation, large parts of US were under drought conditions [NOAA] Slide27:  At CRD scale: Yield=0.023*CVII+1.011 Part Three: Case study in Illinois:  Part Three: Case study in Illinois Part Three: Case study in Illinois:  Part Three: Case study in Illinois Model predictions vs. USDA estimates of normalized yield in 102 Illinois counties. Yields of ‘1’ represent average conditions. Bold symbols represent the state-wide average; light symbols represent individual counties. The bold triangle represents the state-wide estimate made by USDA released in August (dark color) and September (red color) 2005. Part Three: Case study in North and South Dakota:  Part Three: Case study in North and South Dakota Model predictions vs. USDA estimates of normalized yield in North and South Dakota. Yields of ‘1’ represent average conditions. Open symbols represent the USDA estimates and filled symbols represent our model predictions. Bold symbols represent the state-wide average; light symbols represent individual counties. Part Three: Case study in North and South Dakota:  Part Three: Case study in North and South Dakota USDA monitors the crop conditions and yields via monthly-conducted Objective Yield Surveys from August to November. Measurement of stalks, ears, kernel row length, and ear diameter are obtained monthly according to the development stage NASS then make yield forecasts at state-level by applying corn models (blue bars). The CVII model predictions (red bars) are based upon the CVII values at the end of July, August, and September. Part Three: Case study in North and South Dakota:  Part Three: Case study in North and South Dakota The average (absolute) percentage difference of CVII model predictions and NASS estimates to the actual corn yield in South Dakota over the 2000-2006 period. Part Three: Operational Product Development and Dissemination :  Part Three: Operational Product Development and Dissemination http://ecrop.bu.edu/home.html Part Three: Operational Product Development and Dissemination :  Part Three: Operational Product Development and Dissemination Part Three: Application of CVII in US:  Part Three: Application of CVII in US Drought-monitoring indices based upon meteorological data alone can both overestimate (2005) and underestimate (2002) vegetation variations in drought-stricken regions. The need for monitoring vegetation growth directly when estimating yield. CVII model can provide significant predictability (less than 10% error) at the state-average level at least 1-2 months prior to the start of the harvest. Satellites can provide a secondary, independent estimate that can pinpoint regions where agricultural failure is greatest. The cost effectiveness and repetitive, near-global view of earth’s surface suggest this LAI-based CVII may significantly improve crop monitoring and yield estimation at regional scales. Zhang et al., 2006. Monitoring 2005 corn belt yields from space. EOS, Transactions, AGU, 87(15), p150. Zhang et al., 2007. Application of a satellite-based climate-variability impact index for crop yield forecasting in drought-stricken regions, Agricultural and Forest Meteorology, submitted. Outline:  Outline Introduction Derivation of the Climate-Variability Impact Index (CVII) and its application in drought diagnosis and monitoring Modeling crop yield from CVII and its potential application in near real-time crop monitoring Application of the CVII for early crop yield forecasting in drought-stricken regions – Case studies in US Concluding remarks and future directions Concluding remarks and future directions:  Concluding remarks and future directions Conclusion The findings of this dissertation indicate that the new remotely-sensed vegetation indices can be used to identify sensitive regions which are particularly susceptible to agricultural failure arising from month-to-month climate variations; provide both fine-scale and aggregated information on yield for various crops; determine when the in-season predictive value plateaus and which months provide the greatest predictive capacity; perform near real-time drought monitoring and famine prediction at regional and global scale; provide earlier yield forecasts for crop production before harvest and pinpoint regions where agricultural failure is greatest. Future directions:  Future directions Validation of the CVII-production relationship Utilization of fine temporal scale data with longer coverage Validation of the CVII model using in situ data Validation of the CVII model with more crops and locations Interactions between growing-season CVII and climatic variations in semi-arid regions The impacts of three local driving factors including precipitation, temperature, and solar radiation The impacts of remote climate variables including sea surface temperature, surface pressure, stream function, and vertical velocity Contributions of the Complete Research:  Contributions of the Complete Research Development of Climate Impact Index (CII) to provide information regarding the potential sensitivity of vegetation to changes in climatic variables by accounting for the length of growing season. Development of Climate-Variability Impact Index (CVII) to quantify the percentage of the climatological production either gained or lost due to climatic variability during the growing season. Explain more than 50% of the variance in crop yields at different scales. Train linear model using historical crop production data and satellite data Apply CVII-models to produce homogeneous and heterogeneous yield forecasts. Apply CVII-models to provide early yield estimation at drought-stricken regions before the end of the growing season. Examination of the effect of local- and large-scale climate variability on vegetation productivity and hence yield in specific regions. Thank you!:  Thank you!

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