Soil temperature investigations using satellite acquired thermal-infrared data in semi-arid regions

Thermal-infrared data from the Heat Capacity Mapping Mission satellite were used to map the spatial distribution of diurnal surface temperatures and to estimate mean annual soil temperatures (MAST) and annual surface temperature amplitudes (AMP) in semi-arid east central Utah. Diurnal data with minimal snow and cloud cover were selected for five dates throughout a yearly period and geometrically co-registered. Rubber-sheet stretching was aided by the WARP program which allowed preview of image transformations. Daytime maximum and nighttime minimum temperatures were averaged to generation average daily temperature (ADT) data set for each of the five dates. Five ADT values for each pixel were used to fit a sine curve describing the theoretical annual surface temperature response as defined by a solution of a one-dimensinal heat flow equation. Linearization of the equation produced estimates of MAST and AMP plus associated confidence statistics. MAST values were grouped into classes and displayed on a color video screen. Diurnal surface temperatures and MAST were primarily correlated with elevation

A physics-constrained machine learning method for mapping gapless land surface temperature

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable physical constraints are incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model. Physical constraints (PCs) are coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the PC-LGBM model, which incorporates surface energy balance (SEB) constraints underlying the data in CLM-LST modeling within a biophysical framework. Compared with a pure physical method and pure ML methods, the PC-LGBM model improves the prediction accuracy and physical interpretability of LST. It also demonstrates a good extrapolation ability for the responses to extreme weather cases, suggesting that the PC-LGBM model enables not only empirical learning from data but also rationally derived from theory. The proposed method represents an innovative way to map accurate and physically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation

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

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 355)

This bibliography lists 147 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during October, 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Estimation of Gross Primary Productivity of Rice in Arkansas Using the Vegetation Photosynthesis Model

An estimate of the gross primary productivity (GPP) of rice fields can be instrumental to understand their harvest yield and to fulfill an array of agricultural monitoring needs. One of the most common satellite-based models to estimate GPP is the vegetation photosynthesis model (VPM). In this study, we use the VPM model for rice cropland in Arkansas and validate our findings against 16 site-years in-situ data (eddy covariance (EC)). At the site scale, results validated against 16 site-years have shown that the VPM with site information (R2 = 0.71, MAE = 2.90 g C m-2day- 1, and RMSE = 4.04 g C m-2day-1) outperforms VPM based on spatial information (R2 = 0.59, MAE = 4.9 g C m-2day-1, and RMSE = 3.48 g C m-2day-1). At the state scale, in the timeframe between 2008 to 2020, the mean photosynthetic carbon uptake of Arkansas rice fields was 1563.81± 129.09 g C m-2 season-1. The spatial distribution of GPP has shown that rice fields located between 33.5° N and 34.5° N have higher GPP values (1840.40 ± 8.34 g C m- 2 season-1) than other rice regions of Arkansas. At the county-scale, GPP has shown an R2 value of 0.07 against reported yield obtained from an agricultural survey. This GPP dataset will help to identify its underlying meteorological and soil factors, derive a relationship with yield, and investigate crop responses to a changing climate

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities’ increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics

Estimation of Gross Primary Productivity of Rice in Arkansas Using the Vegetation Photosynthesis Model

An estimate of the gross primary productivity (GPP) of rice fields can be instrumental to understand their harvest yield and to fulfill an array of agricultural monitoring needs. One of the most common satellite-based models to estimate GPP is the vegetation photosynthesis model (VPM). In this study, we use the VPM model for rice cropland in Arkansas and validate our findings against 16 site-years in-situ data (eddy covariance (EC)). At the site scale, results validated against 16 site-years have shown that the VPM with site information (R2 = 0.71, MAE = 2.90 g C m-2day- 1, and RMSE = 4.04 g C m-2day-1) outperforms VPM based on spatial information (R2 = 0.59, MAE = 4.9 g C m-2day-1, and RMSE = 3.48 g C m-2day-1). At the state scale, in the timeframe between 2008 to 2020, the mean photosynthetic carbon uptake of Arkansas rice fields was 1563.81± 129.09 g C m-2 season-1. The spatial distribution of GPP has shown that rice fields located between 33.5° N and 34.5° N have higher GPP values (1840.40 ± 8.34 g C m- 2 season-1) than other rice regions of Arkansas. At the county-scale, GPP has shown an R2 value of 0.07 against reported yield obtained from an agricultural survey. This GPP dataset will help to identify its underlying meteorological and soil factors, derive a relationship with yield, and investigate crop responses to a changing climate