Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

1. Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). 2. We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. 3. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic‐related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi‐sensor/multi‐platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. 4. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Sources of Atmospheric Fine Particles and Adsorbed Polycyclic Aromatic Hydrocarbons in Syracuse, New York

Land surface temperature (LST) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor have been widely utilized across scientific disciplines for a variety of purposes. The goal of this dissertation was to utilize MODIS LST for three spatial modeling applications within the conterminous United States (CONUS). These topics broadly encompassed agriculture and human health. The first manuscript compared the performance of all methods previously used to interpolate missing values in 8-day MODIS LST images. At low cloud cover (\u3c30%), the Spline spatial method outperformed all of the temporal and spatiotemporal methods by a wide margin, with median absolute errors (MAEs) ranging from 0.2°C-0.6°C. However, the Weiss spatiotemporal method generally performed best at greater cloud cover, with MAEs ranging from 0.3°C-1.2°C. Considering the distribution of cloud contamination and difficulty of implementing Weiss, using Spline under all conditions for simplicity would be sufficient. The second manuscript compared the corn yield predictive capability across the US Corn Belt of a novel killing degree day metric (LST KDD), computed with daily MODIS LST, and a traditional air temperature-based metric (Tair KDD). LST KDD was capable of predicting annual corn yield with considerably less error than Tair KDD (R2 /RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). The superior performance can be attributed to LST’s ability to better reflect evaporative cooling and water stress. Moreover, these findings suggest that long-term yield projections based on Tair and precipitation alone will contain error, especially for years of extreme drought. Finally, the third manuscript assessed the extent to which daily maximum heat index (HI) across the CONUS can be estimated by MODIS multispectral imagery in conjunction with land cover, topographic, and locational factors. The derived model was capable of estimating HI in 2012 with an acceptable level of error (R 2 = 0.83, RMSE = 4.4°F). LST and water vapor (WV) were, by far, the most important variables for estimation. Expanding this analytical framework to a more extensive study area (both temporally and spatially) would further validate these findings. Moreover, identifying an appropriate interpolation and downscaling approach for daily MODIS imagery would substantially increase the utility of the corn yield and HI models

Satellite-based estimates of net radiation and modeling the role of topography and vegetation on inter-annual hydro-climatology

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 229-260).The Fourth Assessment Report of the Intergovernmental Panel on Climate Change acknowledged that the lack of relevant observations in various regions of the world is a crucial gap in understanding and modeling impacts of climate change related to hydrologic cycle. The Surface Radiation Budget (SRB) is an important component in the study of land surface processes. Existing SRB retrieval algorithms generally suffer from two major shortcomings: difficulty in dealing with cloudy sky conditions and reliance on study-site specific ancillary ground data. In this work, a framework of estimating net radiation from the MODerateresolution Imaging Spectroradiometer (MODIS) data is presented that is applicable under all-sky conditions, while solely relying on satellite data. The results from the proposed methodology are compared against several ground measurements within the United States for the entire 2006. Finally, monthly radiation maps for the Continental United States are produced. Modeling, similar to observations, is critical to the Earth Sciences and the second part of this work focuses on the impact of incorporating vegetation dynamics and topography in modeling hydro-climatology over large river basins. Land and atmosphere are coupled with each other through the exchange of heat, momentum and water at the boundary. This work involves coupling of a physically-based, fully distributed ecohydrology model with a numerical atmospheric model, using high performance computing. The ability of the ecohydrology model (in an offline mode) to accurately resolve hydro-climatic signatures and vegetation dynamics is first examined. The ecohydrology model is applied in a highly instrumented catchment, Walnut Gulch Experimental Watershed (WGEW) in Arizona, for a period of 11-years (1997-2007). The ecohydrology model is able to capture the behavior of several key hydrologic variables and vegetation dynamics within the WGEW. A series of three synthetic experiments are conducted with a coupled land-atmosphere model. The anomalies of various simulated quantities between the synthetic experiments are examined within the rainfall-soil moisture feedback hypothesis proposed by Elathir [1998]. The results from the experiments highlight the need to explicitly account for vegetation dynamics and topography within a numerical atmospheric model. The thesis concludes with a discussion of contributions, and future directions for this work.by Gautam Bisht.Ph.D

CIRA annual report FY 2015/2016

Reporting period April 1, 2015-March 31, 2016

2016 International Land Model Benchmarking (ILAMB) Workshop Report

As earth system models (ESMs) become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of terrestrial biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistryclimate feedbacks and ecosystem processes in these models are essential for reducing the acknowledged substantial uncertainties in 21st century climate change projections