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NASA NTRS2018

Remote Sensing Based Yield Monitoring: Application to Winter Wheat in United States and Ukraine

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).

remote sensing↗

DOE OSTI2021

Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy

Tropical forests are one of the main carbon sinks on Earth, but the magnitude of CO 2 absorbed by tropical vegetation remains uncertain. Terrestrial biosphere models (TBMs) are commonly used to estimate the CO 2 absorbed by forests, but their performance is highly sensitive to the parameterization of processes that control leaf-level CO 2 exchange. Direct measurements of leaf respiratory and photosynthetic traits that determine vegetation CO 2 fluxes are critical, but traditional approaches are time-consuming. Reflectance spectroscopy can be a viable alternative for the estimation of these traits and, because data collection is markedly quicker than traditional gas exchange, the approach can enable the rapid assembly of large datasets. However, the application of spectroscopy to estimate photosynthetic traits across a wide range of tropical species, leaf ages and light environments has not been extensively studied. Here, we used leaf reflectance spectroscopy together with partial least-squares regression (PLSR) modeling to estimate leaf respiration ( R dark25 ), the maximum rate of carboxylation by the enzyme Rubisco ( V cmax25 ), the maximum rate of electron transport ( J max25 ), and the triose phosphate utilization rate ( T p25 ), all normalized to 25°C. We collected data from three tropical forest sites and included leaves from fifty-three species sampled at different leaf phenological stages and different leaf light environments. Our resulting spectra-trait models validated on randomly sampled data showed good predictive performance for V cmax25 , J max25 , T p25 and R dark25 (RMSE of 13, 20, 1.5 and 0.3 μmol m -2 s -1 , and R 2 of 0.74, 0.73, 0.64 and 0.58, respectively). The models showed similar performance when applied to leaves of species not included in the training dataset, illustrating that the approach is robust for capturing the main axes of trait variation in tropical species. We discuss the utility of the spectra-trait and traditional gas exchange approaches for enhancing tropical plant trait studies and improving the parameterization of TBMs.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2018

Remote Sensing Based Yield Monitoring: Application to Winter Wheat in United States and Ukraine

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100 percent of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (tonnes per hectare) (15-18 percent). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7 percent) while for Ukraine it is 0.27 t/ha (8.4 percent).

DVI↗

NASA NTRS2015

Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts

The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations, as well as quantifying the inherent predictability of temperature within each ensemble, shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.

Seasonal Forecasting↗

NASA NTRS1991

A model of regional primary production for use with coarse resolution satellite data

A model of crop primary production, which was originally developed to relate the amount of absorbed photosynthetically active radiation (APAR) to net production in field studies, is discussed in the context of coarse resolution regional remote sensing of primary production. The model depends on an approximately linear relationship between APAR and the normalized difference vegetation index. A more comprehensive form of the conventional model is shown to be necessary when different physiological types of plants or heterogeneous vegetation types occur within the study area. The predicted variable in the new model is total assimilation (net production plus respiration) rather than net production alone or harvest yield.

Prince, S. D.↗

NASA NTRS2009

Terrestrial Carbon Sinks in the Brazilian Amazon and Cerrado Region Predicted from MODIS Satellite Data and Ecosystem Modeling

A simulation model based on satellite observations of monthly vegetation cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate monthly carbon fluxes in terrestrial ecosystems of Brazilian Amazon and Cerrado regions over the period 2000-2004. Net ecosystem production (NEP) flux for atmospheric CO2 in the region for these years was estimated. Consistently high carbon sink fluxes in terrestrial ecosystems on a yearly basis were found in the western portions of the states of Acre and Rondonia and the northern portions of the state of Par a. These areas were not significantly impacted by the 2002-2003 El Nino event in terms of net annual carbon gains. Areas of the region that show periodically high carbon source fluxes from terrestrial ecosystems to the atmosphere on yearly basis were found throughout the state of Maranhao and the southern portions of the state of Amazonas. As demonstrated though tower site comparisons, NEP modeled with monthly MODIS Enhanced Vegetation Index (EVI) inputs closely resembles the measured seasonal carbon fluxes at the LBA Tapajos tower site. Modeling results suggest that the capacity for use of MODIS Enhanced Vegetation Index (EVI) data to predict seasonal uptake rates of CO2 in Amazon forests and Cerrado woodlands is strong.

Potter, C.↗

DOE OSTI2022

Consistent responses of vegetation gas exchange to elevated atmospheric CO 2 emerge from heuristic and optimization models

Elevated atmospheric CO 2 concentration is expected to increase leaf CO 2 assimilation rates, thus promoting plant growth and increasing leaf area. It also decreases stomatal conductance, allowing water savings, which have been hypothesized to drive large-scale greening, in particular in arid and semiarid climates. However, the increase in leaf area could reduce the benefits of elevated CO 2 concentration through soil water depletion. The net effect of elevated CO 2 on leaf- and canopy-level gas exchange remains uncertain. To address this question, we compare the outcomes of a heuristic model based on the Partitioning of Equilibrium Transpiration and Assimilation (PETA) hypothesis and three model variants based on stomatal optimization theory. Predicted relative changes in leaf- and canopy-level gas exchange rates are used as a metric of plant responses to changes in atmospheric CO 2 concentration. Both model approaches predict reductions in leaf-level transpiration rate due to decreased stomatal conductance under elevated CO 2 , but negligible (PETA) or no (optimization) changes in canopy-level transpiration due to the compensatory effect of increased leaf area. Leaf- and canopy-level CO 2 assimilation is predicted to increase, with an amplification of the CO 2 fertilization effect at the canopy level due to the enhanced leaf area. The expected increase in vapour pressure deficit (VPD) under warmer conditions is generally predicted to decrease the sensitivity of gas exchange to atmospheric CO 2 concentration in both models. The consistent predictions by different models that canopy-level transpiration varies little under elevated CO 2 due to combined stomatal conductance reduction and leaf area increase highlight the coordination of physiological and morphological characteristics in vegetation to maximize resource use (here water) under altered climatic conditions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1989

Physically-based parameterization of spatially variable soil and vegetation using satellite multispectral data

A stochastic-geometric landsurface reflectance model is formulated and tested for the parameterization of spatially variable vegetation and soil at subpixel scales using satellite multispectral images without ground truth. Landscapes are conceptualized as 3-D Lambertian reflecting surfaces consisting of plant canopies, represented by solid geometric figures, superposed on a flat soil background. A computer simulation program is developed to investigate image characteristics at various spatial aggregations representative of satellite observational scales, or pixels. The evolution of the shape and structure of the red-infrared space, or scattergram, of typical semivegetated scenes is investigated by sequentially introducing model variables into the simulation. The analytical moments of the total pixel reflectance, including the mean, variance, spatial covariance, and cross-spectral covariance, are derived in terms of the moments of the individual fractional cover and reflectance components. The moments are applied to the solution of the inverse problem: The estimation of subpixel landscape properties on a pixel-by-pixel basis, given only one multispectral image and limited assumptions on the structure of the landscape. The landsurface reflectance model and inversion technique are tested using actual aerial radiometric data collected over regularly spaced pecan trees, and using both aerial and LANDSAT Thematic Mapper data obtained over discontinuous, randomly spaced conifer canopies in a natural forested watershed. Different amounts of solar backscattered diffuse radiation are assumed and the sensitivity of the estimated landsurface parameters to those amounts is examined.

Jasinski, Michael F.↗

NASA NTRS1985

Sea breeze-induced mesoscale systems and severe weather

The relationship between thunderstorm activity during the summer months along coastal regions of the Atlantic and Gulf coasts and the dry sea breeze circulation was investigated. Satellite composites of thunderstorm activity for synoptically undisturbed conditions have been obtained for south Florida for a series of days in the summer of 1983. These data were catalogued into different low level synoptic flow regimes. Five synoptic flow regimes were found from the data. A three-dimensional mesoscale numerical model was used for each sysnoptic flow regime to quantitatively predict the location of enhanced thunderstorm activity. This model includes a parameterization of vegetation and soil moisture feedbacks as well as a sophisticated planetary boundary layer representation. Using the results of the satellite image composites, spatial and temporal characteristics of deep convective cloud patterns and their variation with synoptic flow are described. The results from the numerical model have provided explanations for the observed patterns.

Pielke, R. A.↗

NASA NTRS1984

Scattering models in the microwave regime

Results of first and second year research efforts are summarized in a series of complete articles and abstracts. The goal of the first year efforts was to calculate scattering from an inhomogeneous layer with irregular boundaries to model natural terrains. The model was applied to interpret measurements from vegetation, snow, and sea ice. The goal of the second year was to extend the scattering model to handle disc shaped scatterers which are comparable to incident wavelength and to use the model to investigate the relative merits between active versus passive sensing of soil moisture over vegetated terrain.

Fung, A. K.↗

DOE OSTI2024

Simulations of flaming combustion and flaming-to-smoldering transition in wildland fire spread at flame scale

Our objective in the present study is to provide basic insights into the coupling between external-gas and solid biomass vegetation processes that control the dynamics of flame spread in wildland fire problems. Here we focus on a modeling approach that resolves processes occurring at vegetation and flame scales, i.e., the formation of flammable vapors due to the thermal degradation of the solid biomass, the subsequent combustion in ambient air, the thermal feedback to the biomass through radiative and convective heat transfer, and the possible transition from flaming combustion (taking place outside of the solid biomass) to smoldering combustion (taking place inside the solid biomass). The capability uses a multiphase combustion framework and treats external-gas processes through a Large Eddy Simulation solver and solid biomass processes through a discrete particle model. The discrete particle model adopts a one-dimensional porous medium formulation, includes descriptions of drying, thermal pyrolysis, oxidative pyrolysis, and char oxidation, as well as a description of the external-gas-to-solid-biomass diffusion of oxygen mass; the discrete particle model thereby provides a treatment of in-depth oxidative processes and allows the simulation of smoldering combustion. The modeling capability is applied to the simulation of fire spread across a surrogate biomass vegetation bed corresponding to a discrete array of cylindrical-shaped, vertically-oriented, pine wood sticks, characterized by a monomodal size distribution, in horizontal flat terrain and under wind-aided conditions. The numerical results demonstrate that the model can simulate successful flaming-to-smoldering transition followed by complete biomass consumption.

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS2021

Simulation of Solar-Induced Chlorophyll Fluorescence from 3D Canopies with the Dart Model

The potential of solar-induced chlorophyll fluorescence (SIF) to monitor photosynthesis and plant stress has attracted considerable interest in SIF remote sensing (RS). However, canopy SIF and RS observations are impacted by topography, vegetation three dimension (3D) structure, leaf orientation, non foliar elements (e.g., tree woody skeleton), ... Physically based downscaling of canopy SIF RS data to leaf-level (i.e., to leaf photosynthesis) requires 3D radiative transfer (RT) models simulating canopy SIF and its observation. These models are necessary to better exploit the potential of SIF, by linking leaf SIF and SIF in RS observations as a function of canopy 3D architecture and experimental configurations (sun and viewing directions, etc.). The Discrete Anisotropic Radiative Transfer (DART) model is a comprehensive 3D radiative transfer (RT) model for urban and natural landscapes. This paper presents its SIF modeling for vegetation simulated with facets, its validation with the SCOPE/mSCOPE 1D models, and its recent extension to SIF modelling for landscapes simulated with 3D turbid medium.

radiative transfer↗

NASA NTRS2021

Simulation of Solar-Induced Chlorophyll Fluorescence from 3D Canopies with the Dart Model

Radiative transfer↗

DOE OSTI2021

Topographical Controls on Hillslope-Scale Hydrology Drive Shrub Distributions on the Seward Peninsula, Alaska

Observations indicate shrubs are expanding across the Arctic tundra, mainly on hillslopes and primarily in response to climate warming. However, the impact topography exerts on hydrology, nutrient dynamics, and plant growth can make untangling the mechanisms behind shrub expansion difficult. We examined the role topography plays in determining shrub expansion by applying a coupled transect version of a mechanistic ecosystem model (ecosys) in a tundra hillslope site in the Seward Peninsula, Alaska. Modeled biomass of the dominant plant functional types agreed well with field measurements (R 2 = 0.89) and accurately represented shrub expansion over the past 30 years inferred from satellite observations. In the well-drained crest position, canopy water potential and plant nitrogen (N) uptake was modeled to be low from plant and microbial water stress. Intermediate soil water content in the mid-slope position enhanced mineralization and plant N uptake, increasing shrub biomass. The deciduous shrub growth in the mid-slope position was further enhanced by symbiotic N 2 fixation primed by increased root carbon allocation. The gentle slope in the poorly drained lower-slope position resulted in saturated soil conditions that reduced soil O 2 concentrations, leading to lower root O 2 uptake and lower nutrient uptake and plant biomass. Additionally, a simulation that removed topographical interconnectivity between grid cells resulted in (1) a 28% underestimate of mean shrub biomass and (2) over or underestimated shrub productivity at the various hillslope positions. Our results indicate that land models need to account for hillslope-scale coupled surface and subsurface hydrology to accurately predict current plant distributions and future trajectories in Arctic ecosystems.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1985

Evaluation of a canopy reflectance model for LAI estimation through its inversion

A technique for estimating leaf area indices (LAIs) based on bidirectional canopy reflectance (CR) data is applied to three plant canopies: a naturally growing healthy soybean canopy; and a clumped and tufted orchardgrass canopy, respectively. The CR data were collected using a PARABOLA instrument which is capable of acquiring complete sky-and-ground looking hemispheres in 11 seconds. The model fit and LAI estimates were good for the soybean and clumped orchardgrass canopies, but poor for the tufted orchardgrass canopy when the maximum zenith angle was less than 50 percent. It is shown that the biophysical parameter estimation based on CR measurements applied well to homogeneous herbaceous vegetation types, while better CR models are needed to adequately represent discontinuous plant canopies.

Goel, N. S.↗

NASA NTRS1982

Scattering from a random layer of leaves in the physical optics limit

Backscatter of electromagnetic radiation from a layer of vegetation over flat lossy ground has been studied in collaborative research at the George Washingnton University and the Goddard Space Flight Center. In this work the vegetation is composed of leaves which are modeled by a random collection of lossy dielectric disks. Backscattering coefficients for the vegetation layer have been calculated in the case of disks whose diameter is large compared to wavelength. These backscattering coefficients are obtained in terms of the scattering amplitude of an individual disk by employing the distorted Born procedure. The scattering amplitude for a disk which is large compared to wavelength is then found by physical optic techniques. Computed results are interpreted in terms of dominant reflected and transmitted contributions from the disks and ground.

Lang, R. H.↗

NASA NTRS1999

Satellite Derived Land Surface Temperature for Model Assimilation

Studies have shown that land surface temperature (LST) tendencies are sensitive to the surface moisture availability which is a function of soil moisture and vegetation. The assimilation of satellite derived LST tendencies into the surface energy budget of mesoscale models has shown promise in improving the representation of the complex effects of both soil moisture and vegetation within the models for short term simulations. LST derived from geostationary satellites has the potential of providing the temporal and spatial resolution needed for an LST assimilation process. This paper presents an analysis comparing the LST derived from GOES-8 infrared measurements with LST calculated by the MM5 numerical model. The satellite derived LSTs are calculated using a physical split window approach using channels 4 and 5 of GOES-8. The differences in the LST data sets, especially the tendencies, are presented and examined. Quantifying the differences between the data sets provide insight of possible weaknesses in the model parameterizations affecting the surface energy budget calculations and an indication of the potential effectiveness o f assimilating LST into the models.

Suggs, Ronnie J.↗

NASA NTRS2012

Using Remotely Sensed Data and Watershed and Hydrodynamic Models to Evaluate the Effects of Land Cover Land Use Change on Aquatic Ecosystems in Mobile Bay, AL

Alabama coastal systems have been subjected to increasing pressure from a variety of activities including urban and rural development, shoreline modifications, industrial activities, and dredging of shipping and navigation channels. The impacts on coastal ecosystems are often observed through the use of indicator species. One such indicator species for aquatic ecosystem health is submerged aquatic vegetation (SAV). Watershed and hydrodynamic modeling has been performed to evaluate the impact of land cover land use (LCLU) change in the two counties surrounding Mobile Bay (Mobile and Baldwin) on SAV stressors and controlling factors (temperature, salinity, and sediment) in the Mobile Bay estuary. Watershed modeling using the Loading Simulation Package in C++ (LSPC) was performed for all watersheds contiguous to Mobile Bay for LCLU scenarios in 1948, 1992, 2001, and 2030. Remotely sensed Landsat-derived National Land Cover Data (NLCD) were used in the 1992 and 2001 simulations after having been reclassified to a common classification scheme. The Prescott Spatial Growth Model was used to project the 2030 LCLU scenario based on current trends. The LSPC model simulations provided output on changes in flow, temperature, and sediment for 22 discharge points into the estuary. These results were inputted in the Environmental Fluid Dynamics Computer Code (EFDC) hydrodynamic model to generate data on changes in temperature, salinity, and sediment on a grid throughout Mobile Bay and adjacent estuaries. The changes in the aquatic ecosystem were used to perform an ecological analysis to evaluate the impact on SAV habitat suitability. This is the key product benefiting the Mobile Bay coastal environmental managers that integrates the influences of temperature, salinity, and sediment due to LCLU driven flow changes with the restoration potential of SAVs. Data products and results are being integrated into NOAA s EcoWatch and Gulf of Mexico Data Atlas online systems for dissemination to coastal resource managers and stakeholders.

Al-Hamdan, Mohammad Z.↗