To support seasonal monitoring of fallowed acreage, we developed new algorithms to complement the... more To support seasonal monitoring of fallowed acreage, we developed new algorithms to complement the annual fallow land class in the USDA Cropland Data Layer. Satellite data from Landsat TM, ETM+, and OLI were atmospherically corrected to surface reflectances using LEDAPS and L8SR and then used to calculate statewide composites of NDVI every 8 days. Gaps in the Landsat composites were filled using NDVI data calculated from MODIS MOD09 and MYD09 250m surface reflectance data. Data on field boundaries were compiled from County Agricultural Commissioners and used to extract timeseries of NDVI data for each of more than 220,000 agricultural fields in the state. Decision tree algorithms were trained against field observations for 1000 fields collected in 2012, and then applied to the NDVI timeseries in 2014 and 2015. Data was summarized each month and datasets, maps, and summary tables were provided to CDWR. Data was summarized for the winter (Jan 1 – May 31) and summer (June 1 – Sept 30) production seasons, as well as for the year-to-date (annual) conditions. Field Data Collection and Algorithm Validation To assess the accuracy of the monthly satellite-derived estimates of fallowed acreage provided to CDWR, the project team conducted monthly surveys of field conditions in the Central Valley from March through September 2014/2015. The field survey transects included 650 sites along eight east-west transects, and covered a mix of vegetable crops, winter grains, alfalfa, perennial crops including vineyards and orchards, and a number of sites that were fallow throughout the winter and/or summer growing seasons. The field data collected included information on crop presence or absence, crop type, crop height, visual estimates of canopy cover, soil condition, and observations of evidence of irrigation, weed control, or other field maintenance. Field data were summarized on a monthly and seasonal basis and compared against the satellite-derived data. Results from the accuracy assessment for 2015 are shown below. Accuracy results for 2014 were similar to those for 2015. Abstract We present data on monthly and seasonal land fallowing in 2015 in the California Central Valley during the ongoing drought. The datasets were produced using satellite observations as part of a joint effort by NASA, USDA and USGS in collaboration with the CA Department of Water Resources (CDWR) to provide timely and accurate assessments of land fallowing during drought events in California. This effort has used the CA Central Valley as a pilot region for the National Integrated Drought Information System (NIDIS) for the development and testing of an operational monitoring system. The ongoing drought in California substantially reduced surface water supplies for millions of acres of irrigated farmland in California's Central Valley. Rapid assessment of drought impacts on agricultural production can aid water managers in assessing mitigation options, and guide decision making with respect to mitigation of drought impacts. Satellite remote sensing offers an efficient and consistent way to provide quantitative assessments of drought impacts on agricultural production and increases in fallow acreage associated with reductions in surface water supplies. To provide quantitative measures of uncultivated agricultural acreage throughout the year, we developed a decision tree algorithm and applied it to timeseries data from Landsat TM, ETM+, OLI, and MODIS. Our effort has been focused on the development of indicators of drought impacts in the March – Sept. timeframe based on measures of crop development patterns relative to a reference period with average or above average rainfall. To assess the accuracy of the algorithms, monthly ground validation surveys were conducted across 650 fields from March-September in 2014 and 2015. We present the approach along with updated results from the accuracy assessment, and data and maps of land fallowing in the Central Valley in 2015 (Fig. 1). Conclusion The project team has demonstrated an approach for using data from NASA and USGS satellites (Landsat, Terra, Aqua) to provide timely and consistent information to water managers on land fallowing and reductions in planted acreage during droughts. The project team advance the availability of this information by >10 months. These datasets provide the basis for monthly county tabulations and maps that can be used by water managers to monitor fallowed land extent. This capability can provide early identification of changes in fallowed acreage due to water shortages during droughts, filling an important information gap and reducing ambiguity surrounding drought impact assessment and decision making for drought mitigation.
ABSTRACT Accurate field scale observations of crop water use are necessary to maximize crop produ... more ABSTRACT Accurate field scale observations of crop water use are necessary to maximize crop productivity with limited water resources and to parameterize regional and continental satellite models to estimate near real-time crop water use. However, rapid, continuous observations of field-scale water use in California’s diverse cropping systems have been historically limited. Here we present an integrated framework to assess crop water use in a mature peach orchard in California’s San Joaquin Valley that combines micrometeorological, radiometric, and soil observations. We compared evapotranspiration (ET) measured with an Eddy Covariance tower to soil water balance observations (SM), precipitation (P), and irrigation (I) measurements. Data from our first year of observation indicates a relatively high irrigation efficiency (ET>85% of P+I+ΔSM). Crop coefficient (Kc) had a peak value (~1.2-1.3 of reference ET) that was similar, but more variable than reported for lysimeter-grown peaches in California and which reached an elevated level (Kc>1) earlier in the season. Transpiration (T) was >80% of ET during midday in the growing season. Our preliminary results highlight the need for better quantification of water extraction by mature peach trees from deeper soil layers. Additional observations in the upcoming year from the recently launched Landsat Data Continuity Mission should further enable additional quantification between orchard water use and remotely sensed observations.
Weather forecasting models have been shown to exhibit a strong sensitivity to land surface condit... more Weather forecasting models have been shown to exhibit a strong sensitivity to land surface conditions, particularly soil moisture. However, the lack of robust estimates of soil moisture at appropriate time and space scales has been a persistent problem. Terrestrial Observation and Prediction System (TOPS) integrates surface weather observations and satellite data with ecosystem simulation models to produce spatially and temporally
Eos, Transactions American Geophysical Union, 2011
... as well as global data sets of surface weather records, topog-raphy, soils, land cover, globa... more ... as well as global data sets of surface weather records, topog-raphy, soils, land cover, global climate sim-ulations, and ... Editors Anny Cazenave: Laboratoire d'Etudes en Géophysique et Océanographie Spatiales, Toulouse, France; anny.cazenave@cnes.fr Christina MS Cohen ...
ABSTRACT We address the growing need for accurate water temperature predictions in regulated rive... more ABSTRACT We address the growing need for accurate water temperature predictions in regulated rivers to inform decision support systems and protect aquatic habitats. Although many suitable river temperature models exist, few simultaneously model water temperature dynamics while considering uncertainty of predictions and assimilating observations. Here, we employ a stochastic dynamics approach to water temperature modeling that estimates both the water temperature state and its uncertainty by propagating error through a physically based dynamical system. This method involves converting the governing hydrodynamic and heat transport equations into a state space form and assimilating observations via the Kalman Filter. This model, called the River Assessment for Forecasting Temperature (RAFT), closes the heat budget by tracking heat movement using a robust semi-Lagrangian numerical scheme. RAFT considers key thermodynamic processes, including advection, longitudinal dispersion, atmospheric heat fluxes, lateral inflows, streambed heat exchange, and unsteady nonuniform flow. Inputs include gridded meteorological forecasts from a numerical weather prediction model, bathymetric cross-sectional geometry, and temperature and flow measurements at the upstream boundary and tributaries. We applied RAFT to an ˜100 km portion of the Sacramento River in California, downstream of Keswick Dam (a regulatory dam below Shasta Dam), at a spatial resolution of 2 km and a temporal resolution of 15 min. Model prediction error over a 6 month calibration period was on the order of 0.5°C. When temperature and flow gage data were assimilated, the mean prediction error was significantly less (0.25°C). The model accurately predicts the magnitude and timing of diel temperature fluctuations and can provide 72 h water temperature forecasts when linked with meteorological forecasts and real-time flow/temperature monitoring networks. RAFT is potentially scalable to model and forecast fine-grained one-dimensional temperature dynamics covering a broad extent in a variety of regulated rivers provided that adequate input data are available.
... Forrest Melton, Science Advisor Cindy Schmidt, Science Mentor Dr. JWSkiles, NASA Science Advi... more ... Forrest Melton, Science Advisor Cindy Schmidt, Science Mentor Dr. JWSkiles, NASA Science Advisor DEVELOP, NASA Ames Research Center MS 583-C, Moffett Field, CA 94035 Joseph.W.Skiles@nasa.gov INTRODUCTION ... 1997; Leblanc and Chen 2005). ...
The NASA Terrestrial Observation and Prediction System (TOPS) is a modeling framework that integr... more The NASA Terrestrial Observation and Prediction System (TOPS) is a modeling framework that integrates satellite observations, meteorological observations, and ancillary data to support monitoring and modeling of ecosystem and land surface conditions in near real-time. TOPS provides spatially continuous gridded estimates of a suite of measurements describing environmental conditions, and these data products are currently being applied to support the development of new models capable of forecasting estimated mosquito abundance and transmission risk for mosquito-borne diseases such as West Nile virus. We present results from the modeling analyses, describe their incorporation into the California Vectorborne Disease Surveillance System, and describe possible implications of projected climate and land use change for patterns in mosquito abundance and transmission risk for West Nile virus in California.
Although spatio-temporal patterns of influenza spread often suggest that environmental factors pl... more Although spatio-temporal patterns of influenza spread often suggest that environmental factors play a role, their effect on the geographical variation in the timing of annual epidemics has not been assessed. We examined the effect of solar radiation, dew point, temperature and geographical position on the city-specific timing of epidemics in the USA. Using paediatric in-patient data from hospitals in 35 cities for each influenza season in the study period 2000-2005, we determined 'epidemic timing' by identifying the week of peak influenza activity. For each city we calculated averages of daily climate measurements for 1 October to 31 December. Bayesian hierarchical models were used to assess the strength of association between each variable and epidemic timing. Of the climate variables only solar radiation was significantly related to epidemic timing (95% CI -0.027 to -0.0032). Future studies may elucidate biological mechanisms intrinsically linked to solar radiation that contribute to epidemic timing in temperate regions.
To support seasonal monitoring of fallowed acreage, we developed new algorithms to complement the... more To support seasonal monitoring of fallowed acreage, we developed new algorithms to complement the annual fallow land class in the USDA Cropland Data Layer. Satellite data from Landsat TM, ETM+, and OLI were atmospherically corrected to surface reflectances using LEDAPS and L8SR and then used to calculate statewide composites of NDVI every 8 days. Gaps in the Landsat composites were filled using NDVI data calculated from MODIS MOD09 and MYD09 250m surface reflectance data. Data on field boundaries were compiled from County Agricultural Commissioners and used to extract timeseries of NDVI data for each of more than 220,000 agricultural fields in the state. Decision tree algorithms were trained against field observations for 1000 fields collected in 2012, and then applied to the NDVI timeseries in 2014 and 2015. Data was summarized each month and datasets, maps, and summary tables were provided to CDWR. Data was summarized for the winter (Jan 1 – May 31) and summer (June 1 – Sept 30) production seasons, as well as for the year-to-date (annual) conditions. Field Data Collection and Algorithm Validation To assess the accuracy of the monthly satellite-derived estimates of fallowed acreage provided to CDWR, the project team conducted monthly surveys of field conditions in the Central Valley from March through September 2014/2015. The field survey transects included 650 sites along eight east-west transects, and covered a mix of vegetable crops, winter grains, alfalfa, perennial crops including vineyards and orchards, and a number of sites that were fallow throughout the winter and/or summer growing seasons. The field data collected included information on crop presence or absence, crop type, crop height, visual estimates of canopy cover, soil condition, and observations of evidence of irrigation, weed control, or other field maintenance. Field data were summarized on a monthly and seasonal basis and compared against the satellite-derived data. Results from the accuracy assessment for 2015 are shown below. Accuracy results for 2014 were similar to those for 2015. Abstract We present data on monthly and seasonal land fallowing in 2015 in the California Central Valley during the ongoing drought. The datasets were produced using satellite observations as part of a joint effort by NASA, USDA and USGS in collaboration with the CA Department of Water Resources (CDWR) to provide timely and accurate assessments of land fallowing during drought events in California. This effort has used the CA Central Valley as a pilot region for the National Integrated Drought Information System (NIDIS) for the development and testing of an operational monitoring system. The ongoing drought in California substantially reduced surface water supplies for millions of acres of irrigated farmland in California's Central Valley. Rapid assessment of drought impacts on agricultural production can aid water managers in assessing mitigation options, and guide decision making with respect to mitigation of drought impacts. Satellite remote sensing offers an efficient and consistent way to provide quantitative assessments of drought impacts on agricultural production and increases in fallow acreage associated with reductions in surface water supplies. To provide quantitative measures of uncultivated agricultural acreage throughout the year, we developed a decision tree algorithm and applied it to timeseries data from Landsat TM, ETM+, OLI, and MODIS. Our effort has been focused on the development of indicators of drought impacts in the March – Sept. timeframe based on measures of crop development patterns relative to a reference period with average or above average rainfall. To assess the accuracy of the algorithms, monthly ground validation surveys were conducted across 650 fields from March-September in 2014 and 2015. We present the approach along with updated results from the accuracy assessment, and data and maps of land fallowing in the Central Valley in 2015 (Fig. 1). Conclusion The project team has demonstrated an approach for using data from NASA and USGS satellites (Landsat, Terra, Aqua) to provide timely and consistent information to water managers on land fallowing and reductions in planted acreage during droughts. The project team advance the availability of this information by >10 months. These datasets provide the basis for monthly county tabulations and maps that can be used by water managers to monitor fallowed land extent. This capability can provide early identification of changes in fallowed acreage due to water shortages during droughts, filling an important information gap and reducing ambiguity surrounding drought impact assessment and decision making for drought mitigation.
ABSTRACT Accurate field scale observations of crop water use are necessary to maximize crop produ... more ABSTRACT Accurate field scale observations of crop water use are necessary to maximize crop productivity with limited water resources and to parameterize regional and continental satellite models to estimate near real-time crop water use. However, rapid, continuous observations of field-scale water use in California’s diverse cropping systems have been historically limited. Here we present an integrated framework to assess crop water use in a mature peach orchard in California’s San Joaquin Valley that combines micrometeorological, radiometric, and soil observations. We compared evapotranspiration (ET) measured with an Eddy Covariance tower to soil water balance observations (SM), precipitation (P), and irrigation (I) measurements. Data from our first year of observation indicates a relatively high irrigation efficiency (ET>85% of P+I+ΔSM). Crop coefficient (Kc) had a peak value (~1.2-1.3 of reference ET) that was similar, but more variable than reported for lysimeter-grown peaches in California and which reached an elevated level (Kc>1) earlier in the season. Transpiration (T) was >80% of ET during midday in the growing season. Our preliminary results highlight the need for better quantification of water extraction by mature peach trees from deeper soil layers. Additional observations in the upcoming year from the recently launched Landsat Data Continuity Mission should further enable additional quantification between orchard water use and remotely sensed observations.
Weather forecasting models have been shown to exhibit a strong sensitivity to land surface condit... more Weather forecasting models have been shown to exhibit a strong sensitivity to land surface conditions, particularly soil moisture. However, the lack of robust estimates of soil moisture at appropriate time and space scales has been a persistent problem. Terrestrial Observation and Prediction System (TOPS) integrates surface weather observations and satellite data with ecosystem simulation models to produce spatially and temporally
Eos, Transactions American Geophysical Union, 2011
... as well as global data sets of surface weather records, topog-raphy, soils, land cover, globa... more ... as well as global data sets of surface weather records, topog-raphy, soils, land cover, global climate sim-ulations, and ... Editors Anny Cazenave: Laboratoire d'Etudes en Géophysique et Océanographie Spatiales, Toulouse, France; anny.cazenave@cnes.fr Christina MS Cohen ...
ABSTRACT We address the growing need for accurate water temperature predictions in regulated rive... more ABSTRACT We address the growing need for accurate water temperature predictions in regulated rivers to inform decision support systems and protect aquatic habitats. Although many suitable river temperature models exist, few simultaneously model water temperature dynamics while considering uncertainty of predictions and assimilating observations. Here, we employ a stochastic dynamics approach to water temperature modeling that estimates both the water temperature state and its uncertainty by propagating error through a physically based dynamical system. This method involves converting the governing hydrodynamic and heat transport equations into a state space form and assimilating observations via the Kalman Filter. This model, called the River Assessment for Forecasting Temperature (RAFT), closes the heat budget by tracking heat movement using a robust semi-Lagrangian numerical scheme. RAFT considers key thermodynamic processes, including advection, longitudinal dispersion, atmospheric heat fluxes, lateral inflows, streambed heat exchange, and unsteady nonuniform flow. Inputs include gridded meteorological forecasts from a numerical weather prediction model, bathymetric cross-sectional geometry, and temperature and flow measurements at the upstream boundary and tributaries. We applied RAFT to an ˜100 km portion of the Sacramento River in California, downstream of Keswick Dam (a regulatory dam below Shasta Dam), at a spatial resolution of 2 km and a temporal resolution of 15 min. Model prediction error over a 6 month calibration period was on the order of 0.5°C. When temperature and flow gage data were assimilated, the mean prediction error was significantly less (0.25°C). The model accurately predicts the magnitude and timing of diel temperature fluctuations and can provide 72 h water temperature forecasts when linked with meteorological forecasts and real-time flow/temperature monitoring networks. RAFT is potentially scalable to model and forecast fine-grained one-dimensional temperature dynamics covering a broad extent in a variety of regulated rivers provided that adequate input data are available.
... Forrest Melton, Science Advisor Cindy Schmidt, Science Mentor Dr. JWSkiles, NASA Science Advi... more ... Forrest Melton, Science Advisor Cindy Schmidt, Science Mentor Dr. JWSkiles, NASA Science Advisor DEVELOP, NASA Ames Research Center MS 583-C, Moffett Field, CA 94035 Joseph.W.Skiles@nasa.gov INTRODUCTION ... 1997; Leblanc and Chen 2005). ...
The NASA Terrestrial Observation and Prediction System (TOPS) is a modeling framework that integr... more The NASA Terrestrial Observation and Prediction System (TOPS) is a modeling framework that integrates satellite observations, meteorological observations, and ancillary data to support monitoring and modeling of ecosystem and land surface conditions in near real-time. TOPS provides spatially continuous gridded estimates of a suite of measurements describing environmental conditions, and these data products are currently being applied to support the development of new models capable of forecasting estimated mosquito abundance and transmission risk for mosquito-borne diseases such as West Nile virus. We present results from the modeling analyses, describe their incorporation into the California Vectorborne Disease Surveillance System, and describe possible implications of projected climate and land use change for patterns in mosquito abundance and transmission risk for West Nile virus in California.
Although spatio-temporal patterns of influenza spread often suggest that environmental factors pl... more Although spatio-temporal patterns of influenza spread often suggest that environmental factors play a role, their effect on the geographical variation in the timing of annual epidemics has not been assessed. We examined the effect of solar radiation, dew point, temperature and geographical position on the city-specific timing of epidemics in the USA. Using paediatric in-patient data from hospitals in 35 cities for each influenza season in the study period 2000-2005, we determined 'epidemic timing' by identifying the week of peak influenza activity. For each city we calculated averages of daily climate measurements for 1 October to 31 December. Bayesian hierarchical models were used to assess the strength of association between each variable and epidemic timing. Of the climate variables only solar radiation was significantly related to epidemic timing (95% CI -0.027 to -0.0032). Future studies may elucidate biological mechanisms intrinsically linked to solar radiation that contribute to epidemic timing in temperate regions.
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