Characterizing the Variability of the Structure Parameter in the PROSPECT Leaf Optical Properties Model

Light scattering in stacked mesophyll cells results in similarity characteristic of solar spectral reflectance and transmittance of natural leaves

Solar spectral reflectance and transmittance of natural leaves exhibit dramatic similarity. To elucidate the formation mechanism and physiological significance, a radiative transfer model was constructed, and the effects of stacked mesophyll cells, chlorophyll content and leaf thickness on the visible light absorptance of the natural leaves were analyzed. Results indicated that light scattering caused by the stacked mesophyll cells is responsible for the similarity. The optical path of visible light in the natural leaves is increased with the scattering process, resulting in that the visible light transmittance is significantly reduced meanwhile the visible light reflectance is at a low level, thus the visible light absorptance tends to a maximum and the absorption of photosynthetically active radiation (PAR) by the natural leaves is significantly enhanced. Interestingly, as two key leaf functional traits affecting the absorption process of PAR, chlorophyll content and leaf thickness of the natural leaves in a certain environment show a convergent behavior, resulting in the high visible light absorptance of the natural leaves, which demonstrates the PAR utilizing strategies of the natural leaves. This work provides a new perspective for revealing the evolutionary processes and ecological strategies of natural leaves, and can be adopted to guide the improvement directions of crop photosynthesis.

Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion

Despite the potential implications of a cropland canopy water content (CCWC) thematic product, no global remotely sensed CCWC product is currently generated. The successful launch of the Landsat-8 Operational Land Imager (OLI) in 2012, Sentinel-2A Multispectral Instrument (MSI) in 2015, followed by Sentinel-2B in 2017, make possible the opportunity for CCWC estimation at a spatial and temporal scale that can meet the demands of potential operational users. In this study, we designed and tested a novel radiative transfer model (RTM) inversion technique to combine multiple sources of a priori data in a look-up table (LUT) for inverting the NASA Harmonized Landsat Sentinel-2 (HLS) product for CCWC estimation. This study directly builds on previous research for testing the constraint of the leaf parameter (Ns) in PROSPECT, by applying those constraints in PRO4SAIL in an agricultural setting where the variability of canopy parameters are relatively minimal. In total, 225 independent leaf measurements were used to train the LUTs, and 102 field data points were collected over the 2015–2017 growing seasons for validating the inversions. The results confirm increasing a priori information and regularization yielded the best performance for CCWC estimation. Despite the relatively low variable canopy conditions, the inclusion of Ns constraints did not improve the LUT inversion. Finally, the inversion of Sentinel-2 data outperformed the inversion of Landsat-8 in the HLS product. The method demonstrated ability for HLS inversion for CCWC estimation, resulting in the first HLS-based CCWC product generated through RTM inversion.

Monitoring oil contamination in vegetated areas with optical remote sensing: A comprehensive review

The monitoring of soil contamination deriving from oil and gas industry remains difficult in vegetated areas. Over the last decade, optical remote sensing has proved helpful for this purpose. By tracking alterations in vegetation biochemistry through its optical properties, multi- and hyperspectral remote sensing allow detecting and quantifying crude oil and petroleum products leaked following accidental leakages or bad cessation practices. Recent advances in this field have led to the development of various methods that can be applied either in the field using portable spectroradiometers or at large scale on airborne and satellite images. Experiments carried out under controlled conditions have largely contributed to identifying the most important factors influencing the detection of oil (plant species, mixture composition, etc.). In a perspective of operational use, an important effort is still required to make optical remote sensing a reliable tool for oil and gas companies. The current methods used on imagery should extend their scope to a wide range of contexts and their application to upcoming satellite-embedded hyperspectral sensors should be considered in future studies.

Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUE_Canopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUE_Canopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUE_Canopy. The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.