Holly Croft

Remote sensing of soil surface properties

Remote sensing is now in a strong position to provide meaningful spatial data for use in soil science investigations. In the last 10 years, advancements in remote sensing techniques and technologies have given rise to a wealth of exciting new research findings in soil-related disciplines. This paper provides a critical insight into the role played by remote sensing in this field, with a specific focus on soil surface monitoring. Two key soil properties are considered in this review, soil surface roughness and moisture, because these two variables have benefited most from recent cutting-edge advances in remote sensing. Of note is the fact that the major recent advancements in spatial assessment of soil structure have emerged from optical remote sensing, while the soil moisture community has benefited from advancements in microwave systems, justifying the focus of this paper in these specific directions. The paper considers the newest techniques within active, passive, optical and microwave remote sensing and concludes by considering future challenges, multisensor approaches and the issue of scale — which is a key cross-disciplinary research question of relevance to soil scientists and remote sensing scientists alike.

Leaf chlorophyll content as a proxy for leaf photosynthetic capacity

Abstract Improving the accuracy of estimates of forest carbon exchange is a central priority for understanding ecosystem response to increased atmospheric CO2 levels and improving carbon cycle modelling. However, the spatially continuous parameterization of photosynthetic capacity (Vcmax) at global scales and appropriate temporal intervals within terrestrial biosphere models (TBMs) remains unresolved. This research investigates the use of biochemical parameters for modelling leaf photosynthetic capacity within a deciduous forest. Particular attention is given to the impacts of seasonality on both leaf biophysical variables and physiological processes, and their interdependent relationships. Four deciduous tree species were sampled across three growing seasons (2013–2015), approximately every 10 days for leaf chlorophyll content (ChlLeaf) and canopy structure. Leaf nitrogen (NArea) was also measured during 2014. Leaf photosynthesis was measured during 2014–2015 using a Li‐6400 gas‐exchange system, with A‐Ci curves to model Vcmax. Results showed that seasonality and variations between species resulted in weak relationships between Vcmax normalized to 25°C (Vcmax25) and NArea (R2 = 0.62, P < 0.001), whereas ChlLeaf demonstrated a much stronger correlation with Vcmax25 (R2 = 0.78, P < 0.001). The relationship between ChlLeaf and NArea was also weak (R2 = 0.47, P < 0.001), possibly due to the dynamic partitioning of nitrogen, between and within photosynthetic and nonphotosynthetic fractions. The spatial and temporal variability of Vcmax25 was mapped using Landsat TM/ETM satellite data across the forest site, using physical models to derive ChlLeaf. TBMs largely treat photosynthetic parameters as either fixed constants or varying according to leaf nitrogen content. This research challenges assumptions that simple NArea–Vcmax25 relationships can reliably be used to constrain photosynthetic capacity in TBMs, even within the same plant functional type. It is suggested that ChlLeaf provides a more accurate, direct proxy for Vcmax25 and is also more easily retrievable from satellite data. These results have important implications for carbon modelling within deciduous ecosystems.

Seasonal controls of canopy chlorophyll content on forest carbon uptake: Implications for GPP modeling

Forested ecosystems represent an important part of the global carbon cycle, with accurate estimates of gross primary productivity (GPP) crucial for understanding ecosystem response to environmental controls and improving global carbon models. This research investigated the relationships between leaf area index (LAI) and leaf chlorophyll content (ChlLeaf) with forest carbon uptake. Ground measurements of LAI and ChlLeaf were taken approximately every 9 days across the 2013 growing season from day of year (DOY) 130 to 290 at Borden Forest, Ontario. These biophysical measurements were supported by on-site eddy covariance flux measurements. Differences in the temporal development of LAI and ChlLeaf were considerable, with LAI reaching maximum values within approximately 10 days of bud burst at DOY 141. In contrast, ChlLeaf accumulation only reached maximum values at DOY 182. This divergence has important implications for GPP models which use LAI to represent the fraction of light absorbed by a canopy (fraction of absorbed photosynthetic active radiation (fAPAR)). Daily GPP values showed the strongest relationship with canopy chlorophyll content (ChlCanopy; R2 = 0.69, p < 0.001), with the LAI and GPP relationship displaying nonlinearity at the start and end of the growing season (R2 = 0.55, p < 0.001). Modeled GPP derived from LAI × PAR and ChlCanopy × PAR was tested against measured GPP, giving R2 = 0.63, p < 0.001 and R2 = 0.82, p < 0.001, respectively. This work demonstrates the importance of considering canopy pigment status in deciduous forests, with models that use fAPARLAI rather than fAPARChl neglecting to account for the importance of leaf photosynthetic potential.

A simple spectro-goniometer for collection of multiple view angle reflectance factors

Field spectroradiometers are widely used for environmental applications where data describing visible and near-infrared reflectance factors are of interest. Recent developments in spaceborne and airborne instruments with multiple view angle (MVA) capabilities have resulted in a demand for ground measurements to support these missions. Lightweight portable spectroradiometers offer an appropriate means of collecting MVA spectral reflectance factor data because they are more easily manoeuvrable than other spectroradiometers, but their physical capabilities have not yet been explored in this context. This letter presents the results of a focused experiment aimed at evaluating the field capabilities of a miniaturized Ocean Optics instrument in MVA settings for soil surface roughness applications. MVA hemispherical-conical reflectance factors were collected in situ from soil surfaces whose roughness was determined using a laser profiling survey. The results showed a significant negative relationship (R 2 = 0.74; p < 0.01) between directional reflectance factors measured at 870 nm in the forward-scattering region and a soil structural measure derived from laser profiling data. This corroborates the results of other published studies and suggests that Ocean Optics instruments can be used to support hyperspectral MVA investigations.

Testing the Top-Down Model Inversion Method of Estimating Leaf Reflectance Used to Retrieve Vegetation Biochemical Content Within Empirical Approaches

A top-down model inversion method of estimating leaf reflectance from hyperspectral remote sensing measurements has been tested with an empirical approach used to estimate chlorophyll content. Leaf reflectance is obtained by inverting a geometric-optical model, 5-Scale, validated using hyperspectral AVIRIS data. The shaded scene fractions and the M factor, which includes both the multiple scattering effect and the shaded components, are computed for inverting canopy reflectance into leaf reflectance. The inversion is based on the look-up tables (LUTs) approach. The simulated leaf reflectance values are combined in hyperspectral indices for leaf chlorophyll retrieval and compared against the measured leaf chlorophyll content in the Greater Victoria Watershed District (GVWD), British Columbia (BC). The results demonstrate that the modeled canopy reflectance and AVIRS data are in good agreement for all locations. The regressions of the modified simple ratio [(R728 - R434)/(R720 - R434)] and modified normalized difference index [(R728 - R720)/(R728 + R720 -2R434)] against chlorophyll content exhibit the best fit using second-order polynomial functions with the root-mean-square errors (RMSE) of 4.434 and 4.247, and coefficients of determination of 0.588 and 0.588, respectively. Larger RMSE are observed when the direct canopy-level retrieval, using canopy-level generated vegetation indices, is considered, suggesting the importance of the proposed canopy-to-level reflectance inversion step in chlorophyll retrieval based on hyperspectral vegetation indices. This approach allows for estimation of leaf level information in the absence of leaf spectra field measurements, and simplifies further applications of hyperspectral imagery at the regional scale.

Comparison of Big‐Leaf, Two‐Big‐Leaf, and Two‐Leaf Upscaling Schemes for Evapotranspiration Estimation Using Coupled Carbon‐Water Modeling

Author(s): Luo, X; Chen, JM; Liu, J; Black, TA; Croft, H; Staebler, R; He, L; Arain, MA; Chen, B; Mo, G; Gonsamo, A; McCaughey, H | Abstract: ©2018. American Geophysical Union. All Rights Reserved. Evapotranspiration (ET) is commonly estimated using the Penman-Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon-water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two-big-leaf scheme (TBL) and a two-leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2 = 0.71, root-mean-square error = 0.05 mm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light-saturated and light-unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf-level processes to the canopy scale in BL and TBL.

Deriving Maximum Light Use Efficiency From Crop Growth Model and Satellite Data to Improve Crop Biomass Estimation

Maximum light use efficiency (LUEmax) is an important parameter in biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable crop LUEmax from satellite remote sensing data. LUEmax at the plot level was retrieved first by assimilating field measured green leaf area index and biomass into a crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. LUEmax was then mapped using the best regression model from a VI. The influence factors on LUEmax variability were also assessed. Contrary to a fixed LUEmax, our results suggest that LUEmax is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level LUEmax and VIs, particularly the two-band enhanced vegetation index for winter wheat (Triticum aestivum) and the green chlorophyll index for maize (Zea mays) at the milk stage, provided a potential to derive LUEmax from remote sensing observations. To evaluate the quality of LUEmax derived from remote sensing data, biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant LUEmax and the derived variable LUEmax. Significant improvements in biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable LUEmax . This study offers a new way to derive LUEmax for a specific PEM and to improve the accuracy of biomass estimation using remote sensing.

Nitrogen Availability Dampens the Positive Impacts of CO2 Fertilization on Terrestrial Ecosystem Carbon and Water Cycles

Author(s): He, L; Chen, JM; Croft, H; Gonsamo, A; Luo, X; Liu, J; Zheng, T; Liu, R; Liu, Y | Abstract: ©2017. American Geophysical Union. All Rights Reserved. The magnitude and variability of the terrestrial CO2 sink remain uncertain, partly due to limited global information on ecosystem nitrogen (N) and its cycle. Without N constraint in ecosystem models, the simulated benefits from CO2 fertilization and CO2-induced increases in water use efficiency (WUE) may be overestimated. In this study, satellite observations of a relative measure of chlorophyll content are used as a proxy for leaf photosynthetic N content globally for 2003–2011. Global gross primary productivity (GPP) and evapotranspiration are estimated under elevated CO2 and N-constrained model scenarios. Results suggest that the rate of global GPP increase is overestimated by 85% during 2000–2015 without N limitation. This limitation is found to occur in many tropical and boreal forests, where a negative leaf N trend indicates a reduction in photosynthetic capacity, thereby suppressing the positive vegetation response to enhanced CO2 fertilization. Based on our carbon-water coupled simulations, enhanced CO2 concentration decreased stomatal conductance and hence increased WUE by 10% globally over the 1982 to 2015 time frame. Due to increased anthropogenic N application, GPP in croplands continues to grow and offset the weak negative trend in forests due to N limitation. Our results also show that the improved WUE is unlikely to ease regional droughts in croplands because of increases in evapotranspiration, which are associated with the enhanced GPP. Although the N limitation on GPP increase is large, its associated confidence interval is still wide, suggesting an urgent need for better understanding and quantification of N limitation from satellite observations.

Canopy-Level Photochemical Reflectance Index from Hyperspectral Remote Sensing and Leaf-Level Non-Photochemical Quenching as Early Indicators of Water Stress in Maize

In this study, we evaluated the effectiveness of photochemical reflectance index (PRI) and non-photochemical quenching (NPQ) for assessing water stress in maize for the purpose of developing remote sensing techniques for monitoring water deficits in crops. Leaf-level chlorophyll fluorescence and canopy-level PRI were measured concurrently over a maize field with five different irrigation treatments, ranging from 20% to 90% of the field capacity (FC). Significant correlations were found between leaf-level NPQ (NPQleaf) and the ratio of chlorophyll to carotenoid content (Chl/Car) (R2 = 0.71, p < 0.01) and between NPQleaf and the actual photochemical efficiency of photosystem II (ΔF/Fm′) (R2 = 0.81, p < 0.005). At the early growing stage, both canopy-level PRI and NPQleaf are good indicators of water stress (R2 = 0.65 and p < 0.05; R2 = 0.63 and p < 0.05, respectively). For assessment of extreme water stress on plant growth, a relationship is also established between the quantum yield of photochemistry in PSII (ΦP) and the quantum yield of fluorescence (ΦF) as determined from photochemical quenching (PQ) and non-photochemical quenching (NPQleaf) of excitation energy at different water stress levels. These results would be helpful in monitoring soil water stress on crops at large scales using remote sensing techniques.

Leaf Pigment Content

Plant pigments in terrestrial ecosystems are crucial for sustaining life on the planet, through their pivotal role in plant photosynthesis. They are also important indicators of plant health and nutrient status. As such, a range of empirical and physically based methods exist for estimating leaf pigment content, from the leaf level to larger scale mapping efforts from airborne and satellite imagery. The suitability of these methods varies according to observational scale, application, technical expertise, and cost. Obtaining accurate foliar pigment values from optical remote sensing techniques is vital for the monitoring of vegetation–environment interactions and a range of terrestrial ecosystem processes.