Review paper

A review on the theory of photon transport in leaf canopies

The photon recollision probability (p) and spectral invariant theory (p-theory) have gained much attention in the vegetation remote sensing community over the past decades. A broad range of studies have been performed involving the derivation of theoretical principles, determination of the p value in field measurements and remote sensing studies, and application of the theory in various areas. This review synthesizes current developments in this theory and discusses future directions. The review begins with a brief introduction of the historical background of the p-theory, followed by a description of the general principles of the theory. Subsequently, various methods for estimating the p value in field measurements and remote sensing studies are reviewed. Next, applications of the p-theory in canopy reflectance modeling and vegetation parameter retrieval are illustrated. Finally, the general features of the theory and future research directions are discussed. This study aims to enhance our understanding of the p-theory, its development and applications in various disciplines.

Vegetation in China has experienced significant improvements under the combined effects of human activities and climate change. However, when exploring the effects of climate change on vegetation dynamics, existing studies mainly focus on two meteorological factors (temperature and precipitation) and rarely consider aerosol optical depth (AOD) and photosynthetically active radiation (PAR). Therefore, in this study, we further explored the potential dependence of leaf area index (LAI) on temperature (Tem), precipitation (Pre), AOD, and PAR, based on the analysis of the spatiotemporal variations of LAI in different vegetation types in China. Results showed that the LAI increased significantly from 2001 to 2020, with an overall growth rate of 0.0202/yr; the response of LAI for different vegetation types to each influencing factor varied greatly, and the deciduous broadleaf forests were more sensitive to climate change. Quantitative analysis based on the GeoDetector method showed that the most important driving force of LAI change is Pre (0.596); while Tem (0.394) and AOD (0.325) showed similar effects; by contrast, PAR (0.279) had the lowest driving force on LAI. Among them, the change of Pre is the dominant factor affecting LAI for most vegetation types. From the perspective of regional research, we found that the increase of LAI in the South was greater than that in North China, while human activities (urbanization) were unfavorable to the increase of LAI, such as the Yangtze River Delta where the LAI decreased significantly. This study contributes to a better understanding of the response of different vegetation types to climate and atmospheric changes in China over the past 20 years and provides a scientific reference for developing and implementing ecological restoration measures and promoting sustainable development. Overall, China is 'turning green', and air quality is improving.

Land surface albedo (LSA) is a key parameter in the process of vegetation feedback to climate due to its decisive role in land surface radiation budget. However, our current knowledge on the relationship between LSA and vegetation changes is limited by one-sided attention to sole vegetation growth change or land use/land cover change (LULCC). How vegetation growth or LULCC respectively contributes to LSA change under their interactions remains poorly quantified. In this study, the arid and semi-arid areas of China (ASAC) with profound vegetation changes were selected to tackle this problem. The LSA showed a general downward trend (-0.00009 year⁻¹) during 2000-2018 in response to ASAC's wide-range greening (0.0086 m²m⁻²year⁻¹). Pairwise comparison analysis revealed that under the same unit of vegetation coverage change, the LSA change magnitude was contracted when grassland was converted to cultivated land or forest land, while the conversions of forest land to other vegetations led to an amplified change magnitude of LSA. Vegetation type directly leads to a difference in LSA change magnitude under greening due to their distinct canopy spectral characteristics. Grassland possesses lower LAI and its pixel-level LSA is more prone to be contaminated by background coverage, while LSA of forest land contains more vegetation canopy signal. In most areas where vegetation type converted, greening dominated LSA change with a contribution rate up to 98.14 %, except for the conversion of grassland to forest land, where LULCC accounted for about 66.38 % of LSA change. This study could improve the estimation of LSA from LAI, which is useful for optimizing vegetation-climate interaction models.

Accurate and efficient simulation of remote sensing images is increasingly needed in order to better exploit remote sensing observations and to better design remote sensing missions. DART (Discrete Anisotropic Radiative Transfer), developed since 1992 based on the discrete ordinates method (i.e., standard mode DART-FT), is one of the most accurate and comprehensive 3D radiative transfer models to simulate the radiative budget and remote sensing observations of urban and natural landscapes. Recently, a new method, called DART-Lux, was integrated into DART model to address the requirements of massive remote sensing data simulation for large-scale and complex landscapes. It is developed based on efficient Monte Carlo light transport algorithms (i.e., bidirectional path tracing) and on DART model framework. DART-Lux can accurately and rapidly simulate the bidirectional reflectance factor (BRF) and spectral images of arbitrary landscapes. This paper presents its theory, implementation, and evaluation. Its accuracy, efficiency and advantages are also discussed. The comparison with standard DART-FT in a variety of scenarios shows that DART-Lux is consistent with DART-FT (relative differences <1%) with simulation time and memory reduced by a hundredfold. DART-Lux is already part of the DART version freely available for scientists (https://dart.omp.eu).

Characterizing the distribution, mechanism, and behaviour of invasive species is crucial to implementing an effective plan to protect and manage native grassland ecosystems. Hyperspectral remote sensing has been used to map and monitor invasive species at various spatial and temporal scales. However, most studies focus either on invasive tree species mapping or on the landscape level using coarse-spatial resolution imagery. These coarse-resolution images are not fine enough to distinguish individual invasive grasses, especially in a heterogeneous environment where invasive species are small, fragmented, and co-existent with native plants with similar color and texture. To capture the small yet highly dynamic invasive plants at different stages of the growing season and under various topography and hydrological conditions, we use airborne high-resolution narrow-band hyperspectral imagery (HrHSI) to map invasive species in a heterogeneous grassland ecosystem in southern Ontario, Canada. The results show that there is high spectral and textural separability between two invasive species and between invasive and native plants, leading to an overall species classification accuracy of up to 89.6%. The combination of resultant species-level maps and the digital elevation model (DEM) showed that seasonality is the dominant factor that drives the distribution of invasive species at the landscape level, while small-scale topographic variations partially explain local patches of invasive species. This study provides insights into the feasibility of using HrHSI in mapping invasive species in a heterogeneous ecosystem and offers the means to understand the mechanism and behaviour of invasive species for a more effective grassland management strategy.

The leaf area index (LAI), defined as one-half of the total green leaf area per unit horizontal ground surface area, is a key parameter in agriculture, forestry, ecology, and other fields. The clumping index (CI) accounts for the nonrandom spatial distribution of the foliage elements in the canopy, thereby considerably influencing the accuracy of LAI estimation with optical field-based instruments. Most of the traditional clumping effect correction methods for LAI measurements are based on the theory developed for one-dimensional (1D) data of the vegetation canopy. The development of new methods remains necessary for LAI measurement with two-dimensional (2D) data, including images from digital cover photography (DCP). We found that the fractal dimension (FD) of a 2D ground-based DCP image is an effective tool for correcting the clumping effect and estimating the LAI. The universal formula was derived to describe the relationship between the LAI and FD for randomly distributed leaves using the box-counting method (BCM) and the Boolean model. For the clumped leaves, the universal formula is related to the FD, LAI, and CI. The LAI and CI can be calculated with the FD and gap probability derived from DCP images. Eighteen simulated scenes of different vegetation structure patterns, three realistic canopy scenes of the fourth phase of the radiative transfer model intercomparison (RAMI), and field-measured data acquired from 5 plots in 4 temporal phases were provided to validate the method. The results showed good agreement with the reference (R² = 0.93 and RMSE = 0.46 for simulated data; uncertainty from 0.31 to 1.05 for realistic canopy; R² = 0.85 and RMSE = 0.34 for field-measured data). This validation with downward DCP images shows that the proposed FD method assesses the clumping effect more thoroughly compared to the clumping effect correction methods originally designed for 1D data. The FD method is expected to improve the measurement accuracy of LAI with DCP images, especially for heterogeneous canopies.