Data driven estimation of soil and vegetation attributes using airborne remote sensing

Airborne remote sensing using imaging spectroscopy and LiDAR (Light Detection and Ranging) measurements enable us to quantify ecosystem and land surface attributes. In this study we use high resolution airborne remote sensing to characterize soil attributes and the structure of vegetation canopy. Soil texture, organic matter, and chemical constituents are critical to ecosystem functioning, plant growth, and food security. However, most of the soil data available globally are of coarse resolutions at scales of 1:5 million and lack quantitative information for modeling and land management decisions at field or catchment scales. Thus the need for a spatially contiguous quantitative soil information is of immense scientific merit which can be obtained using airborne and space-borne imaging spectroscopy. Towards this goal we systematically explore the feasibility of characterizing soil properties from imaging spectroscopy using data driven modeling approaches. We have developed a modeling framework for quantitative prediction of different soil attributes using airborne imaging spectroscopy and limited field soil grab sample datasets. The results of our analysis using fine resolution (7.6m) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data collected over midwestern United States immediately after the large 2011 Mississippi River flood indicate the feasibility of using the developed models for quantitative spatial prediction of soil attributes over large areas (> 700 sq. km) of the landscape. The quantitative predictions reveal coherent spatial correlations of the difference in constituent concentrations with legacy landscape features, and immediate disturbances on the landscape due to extreme events. Further for model validation using independent test data, we demonstrate that the results are better represented as a probability density function compared to a single validation subset. We have simulated up-scaled datasets at multiple spatial resolutions ranging from 10m to 90m from the AVIRIS data, including future space based Hyperspectral Infrared Imager (HyspIRI) like observations. These datasets are used to investigate the applicability of the developed modeling framework over increasing spatial resolutions on the characterization of soil constituents. We have outlined an evaluation framework with a set of metrics that considers the point-scale model performance as well as the consistency of cross-scale spatial predictions. The results indicate that the ensemble quantification method is scalable over the entire range of airborne to space-borne spatial resolutions and establishes the feasibility of quantification of soil constituents from space- based observations. Further, we develop a retrieval framework from satellites, which combines the developed modeling framework and spectral similarity measures for global scale characterization of soils using a weighted constrained optimization framework. The retrieval algorithm takes advantage of the potential of repeat temporal satellite measurements to evolve a dynamic spectral library and improve soil characterization. Finally, we demonstrate that in addition to soil constituents, hyperspectral data can add value to characterizations of leaf area density (LAD) estimations for dense overlapping canopies. We develop a method for the estimation of the vertical distribution of foliage or LAD using a combination of airborne LiDAR and hyperspectral data using a feature based data fusion approach. Tree species classification from hyperspectral data is used to develop a novel ellipsoidal ‘tree shaped’ voxel approach for characterizing the LAD of individual trees in a riparian forest setting. We found that the tree shaped voxels represents a more realistic characterization of the upper and middle parts of the tree canopy in terms of higher LAD values, for trees of different heights in a forest stand

Optimal inverse estimation of ecosystem parameters from observations of carbon and energy fluxes

Canopy structural and leaf photosynthesis parameterizations such as maximum carboxylation capacity (V_(cmax)), slope of the Ball–Berry stomatal conductance model (BB_(slope)) and leaf area index (LAI) are crucial for modeling plant physiological processes and canopy radiative transfer. These parameters are large sources of uncertainty in predictions of carbon and water fluxes. In this study, we develop an optimal moving window nonlinear Bayesian inversion framework to use the Soil Canopy Observation Photochemistry and Energy fluxes (SCOPE) model for constraining V_(cmax), BB_(slope) and LAI with observations of coupled carbon and energy fluxes and spectral reflectance from satellites. We adapted SCOPE to follow the biochemical implementation of the Community Land Model and applied the inversion framework for parameter retrievals of plant species that have both the C₃ and C₄ photosynthetic pathways across three ecosystems. We present comparative analysis of parameter retrievals using observations of (i) gross primary productivity (GPP) and latent energy (LE) fluxes and (ii) improvement in results when using flux observations along with reflectance. Our results demonstrate the applicability of the approach in terms of capturing the seasonal variability and posterior error reduction (40 %–90 %) of key ecosystem parameters. The optimized parameters capture the diurnal and seasonal variability in the GPP and LE fluxes well when compared to flux tower observations (0.95>R²>0.79). This study thus demonstrates the feasibility of parameter inversions using SCOPE, which can be easily adapted to incorporate additional data sources such as spectrally resolved reflectance and fluorescence and thermal emissions

From the Ground to Space: Using Solar-Induced Chlorophyll Fluorescence to Estimate Crop Productivity

Timely and accurate monitoring of crops is essential for food security. Here, we examine how well solar‐induced chlorophyll fluorescence (SIF) can inform crop productivity across the United States. Based on tower‐level observations and process‐based modeling, we find highly linear gross primary production (GPP):SIF relationships for C4 crops, while C3 crops show some saturation of GPP at high light when SIF continues to increase. C4 crops yield higher GPP:SIF ratios (30–50%) primarily because SIF is most sensitive to the light reactions (does not account for photorespiration). Scaling to the satellite, we compare SIF from the TROPOspheric Monitoring Instrument (TROPOMI) against tower‐derived GPP and county‐level crop statistics. Temporally, TROPOMI SIF strongly agrees with GPP observations upscaled across a corn and soybean dominated cropland (R² = 0.89). Spatially, county‐level TROPOMI SIF correlates with crop productivity (R² = 0.72; 0.86 when accounting for planted area and C3/C4 contributions), highlighting the potential of SIF for reliable crop monitoring

Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest

Photosynthesis by terrestrial plants represents the majority of CO₂ uptake on Earth, yet it is difficult to measure directly from space. Estimation of gross primary production (GPP) from remote sensing indices represents a primary source of uncertainty, in particular for observing seasonal variations in evergreen forests. Recent vegetation remote sensing techniques have highlighted spectral regions sensitive to dynamic changes in leaf/needle carotenoid composition, showing promise for tracking seasonal changes in photosynthesis of evergreen forests. However, these have mostly been investigated with intermittent field campaigns or with narrow-band spectrometers in these ecosystems. To investigate this potential, we continuously measured vegetation reflectance (400–900 nm) using a canopy spectrometer system, PhotoSpec, mounted on top of an eddy-covariance flux tower in a subalpine evergreen forest at Niwot Ridge, Colorado, USA. We analyzed driving spectral components in the measured canopy reflectance using both statistical and process-based approaches. The decomposed spectral components co-varied with carotenoid content and GPP, supporting the interpretation of the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI). Although the entire 400–900 nm range showed additional spectral changes near the red edge, it did not provide significant improvements in GPP predictions. We found little seasonal variation in both normalized difference vegetation index (NDVI) and the near-infrared vegetation index (NIRv) in this ecosystem. In addition, we quantitatively determined needle-scale chlorophyll-to-carotenoid ratios as well as anthocyanin contents using full-spectrum inversions, both of which were tightly correlated with seasonal GPP changes. Reconstructing GPP from vegetation reflectance using partial least-squares regression (PLSR) explained approximately 87 % of the variability in observed GPP. Our results linked the seasonal variation in reflectance to the pool size of photoprotective pigments, highlighting all spectral locations within 400–900 nm associated with GPP seasonality in evergreen forests

Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest

Photosynthesis by terrestrial plants represents the majority of CO2 uptake on Earth, yet it is difficult to measure directly from space. Estimation of gross primary production (GPP) from remote sensing indices represents a primary source of uncertainty, in particular for observing seasonal variations in evergreen forests. Recent vegetation remote sensing techniques have highlighted spectral regions sensitive to dynamic changes in leaf/needle carotenoid composition, showing promise for tracking seasonal changes in photosynthesis of evergreen forests. However, these have mostly been investigated with intermittent field campaigns or with narrow-band spectrometers in these ecosystems. To investigate this potential, we continuously measured vegetation reflectance (400&ndash;900 nm) using a canopy spectrometer system, PhotoSpec, mounted on top of an eddy-covariance flux tower in a subalpine evergreen forest at Niwot Ridge, Colorado, USA. We analyzed driving spectral components in the measured canopy reflectance using both statistical and process-based approaches. The decomposed spectral components co-varied with carotenoid content and GPP, supporting the interpretation of the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI). Although the entire 400&ndash;900 nm range showed additional spectral changes near the red edge, it did not provide significant improvements in GPP predictions. We found little seasonal variation in both normalized difference vegetation index (NDVI) and the near-infrared vegetation index (NIRv) in this ecosystem. In addition, we quantitatively determined needle-scale chlorophyll-to-carotenoid ratios as well as anthocyanin contents using full-spectrum inversions, both of which were tightly correlated with seasonal GPP changes. Reconstructing GPP from vegetation reflectance using partial least-squares regression (PLSR) explained approximately 87 % of the variability in observed GPP. Our results linked the seasonal variation in reflectance to the pool size of photoprotective pigments, highlighting all spectral locations within 400&ndash;900 nm associated with GPP seasonality in evergreen forests.</p

Effect of spatial filtering on characterizing soil properties from imaging spectrometer data.

Airborne imaging spectroscopy covering wavelength range of 0.35-2.5 ?m can be used to quantify soil textural properties and chemical constituents. In this paper, we evaluate the effects of spatial resolution on the quantification of soil constituents using a lasso algorithm-based ensemble bootstrapping framework. Airborne visible infrared imaging spectrometer data collected at 7.6 m resolution over Bird's Point New Madrid (BPNM) floodway in Missouri, USA, is upscaled using a spatial filter to simulate a satellite-based sensor and generate multiple coarser resolution datasets, including the originally proposed 60.8 m hyperspectral infrared imager like data. The simulated data at multiple spatial resolutions are used in an ensemble lasso algorithm-based modeling framework for developing quantitative prediction models and spatial mapping of the soil constituents. We outline an evaluation framework with a set of metrics that considers the point-scale model performance as well as the consistency of cross-scale spatial predictions. The model results demonstrate that the ensemble quantification method is scalable, and further the model structure indicates the persistence of important spectral features across spatial resolutions. The probability density functions of the constituents over the BPNM landscape show that it is similar for multiple spatial resolutions. Finally, a comparison of the model predictions with statistical central values together with the within pixel variance across fine to coarse resolutions indicate that the model accurately captures the median values of the fine subgrid that the coarse-resolution data is composed of. This study establishes the feasibility for quantifying soil constituents from space-borne hyperspectral sensors

Tracking Seasonal and Interannual Variability in Photosynthetic Downregulation in Response to Water Stress at a Temperate Deciduous Forest

The understanding and modeling of photosynthetic dynamics affected by climate variability can be highly uncertain. In this paper, we examined a well‐characterized eddy covariance site in a drought‐prone temperate deciduous broadleaf forest combining tower measurements and satellite observations. We find that an increase in spring temperature usually leads to enhanced spring gross primary production (GPP), but a GPP reduction in late growing season due to water limitation. We evaluated how well a coupled fluorescence‐photosynthesis model (SCOPE) and satellite data sets track the interannual and seasonal variations of tower GPP from 2007 to 2016. In SCOPE, a simple stress factor scaling of Vcmax as a linear function of observed predawn leaf water potential (ψ_(pd)) shows a good agreement between modeled and measured interannual variations in both GPP and solar‐induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment‐2 (GOME‐2). The modeled and satellite‐observed changes in SIF_(yield) are ~30% smaller than corresponding changes in light use efficiency (LUE) under severe stress, for which a common linear SIF to GPP scaling would underestimate the stress reduction in GPP. Overall, GOME‐2 SIF tracks interannual tower GPP variations better than satellite vegetations indices (VIs) representing canopy “greenness.” However, it is still challenging to attribute observed SIF variations unequivocally to greenness or physiological changes due to large GOME‐2 footprint. Higher‐resolution SIF data sets (e.g., TROPOMI) already show the potential to well capture the downregulation of late‐season GPP and could pave the way to better disentangle canopy structural and physiological changes in the future

From the Ground to Space: Using Solar-Induced Chlorophyll Fluorescence to Estimate Crop Productivity

Timely and accurate monitoring of crops is essential for food security. Here, we examine how well solar‐induced chlorophyll fluorescence (SIF) can inform crop productivity across the United States. Based on tower‐level observations and process‐based modeling, we find highly linear gross primary production (GPP):SIF relationships for C4 crops, while C3 crops show some saturation of GPP at high light when SIF continues to increase. C4 crops yield higher GPP:SIF ratios (30–50%) primarily because SIF is most sensitive to the light reactions (does not account for photorespiration). Scaling to the satellite, we compare SIF from the TROPOspheric Monitoring Instrument (TROPOMI) against tower‐derived GPP and county‐level crop statistics. Temporally, TROPOMI SIF strongly agrees with GPP observations upscaled across a corn and soybean dominated cropland (R² = 0.89). Spatially, county‐level TROPOMI SIF correlates with crop productivity (R² = 0.72; 0.86 when accounting for planted area and C3/C4 contributions), highlighting the potential of SIF for reliable crop monitoring

BPNM_Publication

This collection includes data developed and used for the analysis of the Birds Point-New Madrid (BPNM) Floodway activation in 2011. The data collection includes 10 items, all of which present the processed and derived data. The processed differential LiDAR is the 2005 (pre-flood) LiDAR subtracted from the 2011 (post-flood) LiDAR and corrected for flight line errors. The original LiDAR data were obtained from US Army Corps of Engineers. There are 5 simulated maximum velocity data items from HydroSed2D at two locations (O’Bryan Ridge and Ten Mile Pond) and 2 simulation cases (vegetation and no vegetation). The maximum velocity data for the entire Floodway is for the vegetated case. The NASA AVIRIS dataset is classified into classes representing woody vegetation and bare soil. The soil dataset (K/T) is an erodibility index derived from USDA SSURGO data. Additional data for this study was provided by the USGS, and is available along with the report at the following site: http://pubs.usgs.gov/pp/1798e/. This data includes ADCP (Acoustic Doppler Current Profiler) flow measurements from the inflows an outflows of the Floodway, and HOBO depth sensor measurements from various points within the Floodway. This data were used to validate the HydroSed2D simulations