Improving Simulated Soil Moisture Fields Through Assimilation of AMSR-E Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint

Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed

Development and validation of the global surface type data product from S-NPP VIIRS

Accurate representation of actual terrestrial surface types at regional to global scales is an important element for many applications. Based on National Aeronautics and Space Administration Moderate Resolution Imaging Spectroradiometer land cover algorithms, a global surface-type product from observations of the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership, provides consistent global land cover classification map for various studies, such as land surface modelling for numerical weather predictions, land management, biodiversity and hydrological modelling, and carbon and ecosystem studies. This letter introduces the development and validation of the VIIRS global surface-type product using the land cover classification scheme of the International Geosphere- Biosphere Programme. Surface reflectance data from VIIRS were composited into monthly data and then into annual metrics. The C5.0 decision tree classifier was used to determine the surface type for each pixel in a 1 km grid. To quantitatively evaluate accuracies of the new surface type product, a visual interpretation-based validation was performed in which high-resolution satellite images and other ancillary data were used as the reference. The validation result based on the large validation data set indicated that (78.64 ± 0.57)% overall classification accuracy was achieved

Development and validation of the global surface type data product from S-NPP VIIRS

Accurate representation of actual terrestrial surface types at regional to global scales is an important element for many applications. Based on National Aeronautics and Space Administration Moderate Resolution Imaging Spectroradiometer land cover algorithms, a global surface-type product from observations of the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership, provides consistent global land cover classification map for various studies, such as land surface modelling for numerical weather predictions, land management, biodiversity and hydrological modelling, and carbon and ecosystem studies. This letter introduces the development and validation of the VIIRS global surface-type product using the land cover classification scheme of the International Geosphere- Biosphere Programme. Surface reflectance data from VIIRS were composited into monthly data and then into annual metrics. The C5.0 decision tree classifier was used to determine the surface type for each pixel in a 1 km grid. To quantitatively evaluate accuracies of the new surface type product, a visual interpretation-based validation was performed in which high-resolution satellite images and other ancillary data were used as the reference. The validation result based on the large validation data set indicated that (78.64 ± 0.57)% overall classification accuracy was achieved

A Method for Objectively Integrating Soil Moisture Satellite Observations and Model Simulations Toward a Blended Drought Index

With satellite soil moisture (SM) retrievals becoming widely and continuously available, we aim to develop a method to objectively integrate the drought indices into one that is more accurate and consistently reliable. The datasets used in this paper include the Noah land surface modelbased SM estimations, AtmosphereLandExchangeInverse modelbased Evaporative Stress Index, and the satellite SM products from the Advanced Scatterometer, WindSat, Soil Moisture and Ocean Salinity, and Soil Moisture Operational Product System. Using the Triple Collocation Error Model (TCEM) to quantify the uncertainties of these data, we developed an optically blended drought index (BDI_b) that objectively integrates drought estimations with the lowest TCEMderived rootmeansquareerrors in this paper. With respect to the reported drought records and the drought monitoring benchmarks including the U.S. Drought Monitor, the Palmer Drought Severity Index and the standardized precipitation evapotranspiration index products, the BDI_b was compared with the sample average blending drought index (BDI_s) and the RMSEweighted average blending drought indices (BDI_w). Relative to the BDI_s and the BDI_w, the BDI_b performs more consistently with the drought monitoring benchmarks. With respect to the official drought records, the developed BDI_b shows the best performance on tracking drought development in terms of time evolution and spatial patterns of 2010Russia, 2011USA, 2013New Zealand droughts and other reported agricultural drought occurrences over the 20092014 period. These results suggest that model simulations and remotely sensed observations of SM can be objectively translated into useful information for drought monitoring and early warning, in turn can reduce drought risk and impacts

Development OF A Multi-Scale Framework for Mapping Global Evapotranspiration

As the worlds water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use (e.g, ET) are becoming increasingly important, especially in areas of significant water and food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in mid-morning land surface temperature to estimate the partitioning of sensible and latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring

Future Opportunities and Challenges in Remote Sensing of Drought

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation. Other indices such as the Vegetation Health Index (VHI) were also developed during this time period, and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers such as AVHRR could provide for operational drought monitoring through their near-daily, global observations of Earth's land surface. However, the advancement of satellite remote sensing of drought was limited by the relatively few spectral bands of operational global sensors such as AVHRR, along with a relatively short period of observational record. Remote sensing advancements are of paramount importance given the increasing demand for tools that can provide accurate, timely, and integrated information on drought conditions to facilitate proactive decision making (NIDIS, 2007). Satellite-based approaches are key to addressing significant gaps in the spatial and temporal coverage of current surface station instrument networks providing key moisture observations (e.g., rainfall, snow, soil moisture, ground water, and ET) over the United States and globally (NIDIS, 2007). Improved monitoring capabilities will be particularly important given increases in spatial extent, intensity, and duration of drought events observed in some regions of the world, as reported in the International Panel on Climate Change (IPCC) report (IPCC, 2007). The risk of drought is anticipated to further increase in some regions in response to climatic changes in the hydrologic cycle related to evaporation, precipitation, air temperature, and snow cover (Burke et al., 2006; IPCC, 2007; USGCRP, 2009). Numerous national, regional, and global efforts such as the Famine and Early Warning System (FEWS), National Integrated Drought Information System (NIDIS), and Group on Earth Observations (GEO), as well as the establishment of regional drought centers (e.g., European Drought Observatory) and geospatial visualization and monitoring systems (e.g, NASA SERVIR) have been undertaken to improve drought monitoring and early warning systems throughout the world. The suite of innovative remote sensing tools that have recently emerged will be looked upon to fill important data and knowledge gaps (NIDIS, 2007; NRC, 2007) to address a wide range of drought-related issues including food security, water scarcity, and human health

Stable heterologous expression of biologically active terpenoids in green plant cells

Plants biosynthesize a great diversity of biologically active small molecules of interest for fragrances, flavours, and pharmaceuticals. Among specialized metabolites, terpenoids represent the greatest molecular diversity. Many terpenoids are very complex, and total chemical synthesis often requires many steps and difficult chemical reactions, resulting in a low final yield or incorrect stereochemistry. Several drug candidates with terpene skeletons are difficult to obtain by chemical synthesis due to their large number of chiral centres. Thus, biological production remains the preferred method for industrial production for many of these compounds. However because these chemicals are often found in low abundance in the native plant, or are produced in plants which are difficult to cultivate, there is great interest in engineering increased production or expression of the biosynthetic pathways in heterologous hosts. Although there are many examples of successful engineering of microbes such as yeast or bacteria to produce these compounds, this often requires extensive changes to the host organism's metabolism. Optimization of plant gene expression, post-translational protein modifications, subcellular localization, and other factors often present challenges. To address the future demand for natural products used as drugs, new platforms are being established that are better suited for heterologous production of plant metabolites. Specifically, direct metabolic engineering of plants can provide effective heterologous expression for production of valuable plant-derived natural products. In this review, our primary focus is on small terpenoids and we discuss the benefits of plant expression platforms and provide several successful examples of stable production of small terpenoids in plants

Future Opportunities and Challenges in Remote Sensing of Drought

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation, as summarized by Anyamba and Tucker (2012, Chapter 2). Other indices such as the Vegetation Health Index (VHI) (Kogan, 1995) were also developed during this time period and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers like AVHRR could provide for operational drought monitoring through near-daily, synoptic observations of earth’s land surface. However, the advancement of satellite remote sensing for drought monitoring was limited by the relatively few spectral bands on operational global sensors such as AVHRR, along with a relatively short observational record

Development of a Global Evaporative Stress Index Based on TIR and MW LST

No abstract availabl

Mapping Drought with Satellite Remote Sensing and Land Surface Modeling

No abstract availabl