Vegetation Biophysical Parameters Research Articles

Accurate solution of the SAIL model by leaf inclination angle calculation based on laser point clouds

ABSTRACT The canopy radiative transfer model enables quantitative inversion of vegetation biophysical parameters by rapidly simulating spectral information. However, structural input parameters, such as the leaf inclination angle distribution (LAD), are often set to empirical values in classic models, leading to simulation errors. Laser point cloud technology has developed rapidly in recent years, and the 3D structure of vegetation can be detailed using laser sensors and processing software, providing the necessary conditions for accurate solutions of radiative transfer models of the canopy. However, the mechanisms for improving existing models of canopy radiative transfer by introducing laser point clouds have not yet been theoretically analyzed in detail. In this study, a precise solution was proposed by incorporating the accurate LAD obtained from laser point clouds into the intermediate function of the traditional Scattering by Arbitrarily Inclined Leaves (SAIL) model, which is a widely used multiangle radiative transfer model. The calculation of the leaf inclination angle and the derivation of the improved model were detailed in this paper. Experiments from different observation angles and bands compared the similarity between the actual measured spectra and the simulated spectra for both the traditional SAIL model and the proposed model with accurate structural parameters. The overall similarity of the proposed model with respect to the conventional SAIL model was improved, indicating that introducing laser point clouds improved the simulation accuracy. The proposed model achieved more significant improvements in the blue 450 nm and red 670 nm bands. Moreover, the similarity improvements differed between different angles. Finally, we analyzed the sensitivity of the structural parameters of the SAIL model and the proposed model using local and global sensitivity analysis methods.

Quantitative Assessment of Factors Influencing the Spatiotemporal Variation in Carbon Dioxide Fluxes Simulated by Multi-Source Remote Sensing Data in Tropical Vegetation

Vegetation plays a vital role in the global carbon cycle, a function of particular significance in regulating carbon dioxide fluxes within tropical ecosystems. Therefore, it is crucial to enhance the precision of carbon dioxide flux estimates for tropical vegetation and to explore the determinants influencing carbon sequestration. In this study, Landsat series images and Sentinel-2 Multispectral Instrument satellite data were used to invert vegetation biophysical parameters, thereby improving the timeliness and resolution of state variables from the boreal ecosystem productivity simulator (BEPS). The BEPS model at a 30 m resolution was developed to accurately capture tropical vegetation carbon dioxide fluxes across Hainan Island (HN) over the preceding two decades. The impacts of climate variations and anthropogenic activities on the carbon dioxide fluxes of tropical vegetation were further quantified using quantile regression models and a land-use transfer matrix. Results indicate significant increases in both net primary productivity (NPP) and net ecosystem productivity (NEP) in HN during the period 2000–2020, by 5.81 and 4.29 g C/m2 year, respectively. Spatial trends in vegetation carbon dioxide fluxes exhibited a consistent decline from inland regions to coastal zones. Anthropogenic activities were the dominant factor in the reduced stability of coastal NPP, while the post-2005 vegetation restoration promoted the southward expansion of high NPP (>1200 g C/m2) in the central part of HN. NPP in this tropical island was more sensitive to temperature than to precipitation, with a 1 °C temperature increase resulting in 4.1 g C/m2 reduction in dry-season NPP compared to wet-season NPP. Upgrades of cropland quality and grassland restoration have improved NPP yields, and land use transfers have resulted in a 0.301 Tg C net increase in NPP. This study provides new insight into the improvement of the carbon dioxide flux model at a finer scale for tropical vegetation and highlights ecological construction as an adaptation strategy to enhance the carbon sinks of tropical vegetation under negative climate change conditions.

A deep transfer learning framework for mapping high spatiotemporal resolution LAI

Leaf area index (LAI) is an important variable for characterizing vegetation structure. Contemporary satellite-based LAI products with moderate spatial resolution, such as those derived from the MODIS observations, offer unique opportunities for large-scale monitoring but are insufficient for resolving heterogeneous landscapes. Although high-resolution satellite observations can derive detailed LAI maps, the low revisit frequency and the presence of cloud cover disrupt the temporal continuity of these high-resolution data, thereby leading to the fact that most current LAI inversion models for high spatial resolution data are based on single pixel and date without fully utilizing temporal information. Moreover, LAI estimation models trained solely using satellite products or simulations are also impeded by the inconsistencies between field and satellite LAI exist owing to local atmospheric, soil, and canopy conditions, while in-situ LAI measurements are sparse and insufficient for large-scale missions. To address these challenges, this study proposed a new framework based on deep transfer learning, which includes three key features that contribute to its high performance. Firstly, a Bi-directional Long Short-Term Memory (Bi-LSTM) model is pre-trained using MODIS reflectance and MODIS LAI products to capture the general non-linear relationship between reflectance and LAI and incorporate temporal dependencies as prior information to reduce uncertainties associated with ill-posed inversion problems and noises. Secondly, this pre-trained Bi-LSTM is transferred from satellite to field by fine-tuning with sparse in-situ LAI measurements to overcome issues arising from local inconsistencies. Thirdly, reconstructed Landsat time-series images via fusing MODIS and Landsat reflectance images are used as inputs of the Bi-LSTM to generate high-quality Landsat LAI products at both high spatial and temporal resolutions. To validate the proposed approach, field LAI measurements were collected at nine locations across the contiguous U.S. from 2000 to 2018, including three land cover types: croplands, grasslands, and forest. Quantitative assessments demonstrate that the Bi-LSTM outperforms three benchmarks, including a PROSAIL-based Look-Up Table (LUT) method, a random forest-based LAI retrieval and MODIS LAI (MCD15A3H), exhibiting lower RMSE and higher R2 in most cases. Additionally, the Bi-LSTM predictions yield lower random fluctuations than estimations from the LUT·, random forest and MODIS LAI, indicating the higher robustness of the proposed framework. The findings of this study highlight the value of transfer learning in estimation of vegetation biophysical parameters, which involves pre-training using sufficient existing satellite products to produce a generalized model and transferring knowledge from in-situ measurements to bridge gaps between satellite and field. By leveraging advanced transfer learning techniques and multi-source and multi-scale data, the proposed framework enables the production of long-term LAI maps at fine resolutions, facilitating downstream applications in regions characterized by high spatial heterogeneity.

LACC2.0: Improving the LACC Algorithm for Reconstructing Satellite-Derived Time Series of Vegetation Biochemical Parameters

The locally adjusted cubic-spline capping (LACC) algorithm is well recognized for its effectiveness in the global time series reconstruction of vegetation biophysical and biochemical parameters. However, in its application, we often encounter issues, such as identifying positively biased outliers for vegetation biochemical parameters and reducing the influence of long consecutive gaps. In this study, we improved the LACC algorithm to address the above two issues by (1) incorporating a procedure to remove outliers and (2) integrating the spatial information of neighboring pixels for large data gap filling. To evaluate the performance of the new version of LACC (namely LACC2.0), leaf chlorophyll content (LCC) was taken as an example. A reference LCC curve was generated for each pixel of the global map as the true value for global evaluation, and a time series of LCC with real gaps in the original data for each pixel was created by adding Gaussian noises into observations for testing the effectiveness of time series reconstruction algorithms. Results showed that the percentage of pixels with an RMSE smaller than 5 μg/cm2 was improved from 81.2% in LACC to 96.4% in LACC2.0, demonstrating that LACC2.0 had the potential to provide a better reconstruction of global daily satellite-derived vegetation biochemical parameters. This finding highlights the significance of outlier removal and spatial-temporal fusion to enhance the accuracy and reliability of time series reconstruction.

Spatio-Temporal Variability Analysis of Vegetation Dynamics in China from 2000 to 2022 Based on Leaf Area Index: A Multi-Temporal Image Classification Perspective

Leaf Area Index (LAI) is one of the most important biophysical parameters of vegetation, and its dynamic changes can be used as a reflective indicator and differentiation basis of vegetation function. In this study, a VCA–MLC (Vertex Component Analysis–Maximum Likelihood Classification) algorithm is proposed from the perspective of multi-temporal satellite LAI image classification to monitor and quantify the spatial and temporal variability of vegetation dynamics in China since 2000. The algorithm extracts the vegetation endmembers from 46 multi-temporal images of MODIS LAI in 2011 without the aid of other a priori knowledge and uses the maximum likelihood classification method to select the categories that satisfy the requirements of the number of missing periods, absolute distance, and relative distance for the rest pixels to be classified, ultimately dividing the vegetation area of China into 10 vegetation zones called China Vegetation Functional Zones (CVFZ). CVFZ outperforms MCD12Q1 and CLCD land cover datasets in the overall differentiation of vegetation functions and can be used synergistically with other land cover datasets. In this study, CVFZ is used to cut the constant vegetation-type pixels of MCD12Q1 during 2001–2022. The results of the LAI mean time series decomposition of each subregion using the STL (Seasonal-Trend Decomposition based on Loess) method show that the rate of vegetation greening ranges from 9.02 × 10−4 m2m−2yr−1 in shrubland subregions to 2.34 × 10−2 m2m−2yr−1 in savanna subregions. In relative terms, the average greening speed of forests is moderate, and savannas tend to have the fastest average greening speed. The greening speed of grasslands and croplands in different zones varies widely. In contrast, the average greening speed of shrublands is the slowest. In addition, CVFZ detected grasslands with one or two phenological cycles, broadleaf croplands with one or two phenological cycles, and shrublands with no apparent or one phenological cycle.

Remote sensing as a tool of biological conservation and grassland monitoring in mountain areas of Southeastern Kazakhstan

Grassland degradation, as a worldwide phenomenon, has economic and bio-conservation aspects. Degradation of mountain grasslands severely impacts the stability of ecosystems in fragile mountain environments and may destroy the habitats of many endemic and endangered species. One of the significant triggers of mountain grassland degradation, along with the possible consequences of climate change, is massive overgrazing. Overgrazing may seriously influence the ecosystem since it results in plant composition changes, soil erosion, water regime disturbances, etc., up to the disappearance of the entire ecosystem. It is crucial to have a reliable and cost-effective instrument for the ecosystem assessment of remote and hardly accessible mountain areas supported by accurate methods of vegetation parameter estimation since the vegetation cover is first to react to externally driven disturbances. The current study was conducted in the Dzungarian Alatau Mountains, inhabited by the rare and endemic anuran amphibian Ranodon sibiricus (Kessler, 1886). The project of the Conservation International Foundation (CIF/326/21) was aimed at the strategy of species conservation. The current study emphasises estimating the overgrazing risks for the amphibian population. More than two hundred ground measurements were done within the Upper Koksu Forestry and adjacent areas to provide representative data on the vegetation parameters. We tested a series of spectral indices related to vegetation biophysical parameters. We found the DWSI, GrNDVI, IRECI and NDI45 indices to provide the best correlations and reasonable accuracy for remote measurements of above-ground biomass, grass cover, and unpalatable grass content. Sentinel-2 data with the red-edge bands, in most cases, provided better performance. Our study confirmed that the use of a single criterion (like above-ground biomass) might result in a serious underestimation of grassland degradation. Data obtained from field surveys and satellite information analysis allowed the evaluation of the optimal grazing load for the Upper Koksu Forestry and provided recommendations for the Action Plan on the Conservation of Ranodon sibiricus.

Remote sensing as a tool of biological conservation and grassland monitoring in mountain areas of Southeastern Kazakhstan

Grassland degradation, as a worldwide phenomenon, has economic and bio-conservation aspects. Degradation of mountain grasslands severely impacts the ecosystem stability in fragile mountain environments and may destroy habitats of many endemic and endangered species. One of the significant triggers of mountain grassland degradation, along with possible consequences of climate change, is massive overgrazing. Overgrazing may seriously influence the ecosystem since it results in plant composition changes, soil erosion, water regime disturbances, etc., up to the disappearance of the entire ecosystem. It is crucial to have a reliable and cost-effective instrument for the ecosystem assessment of remote and hardly accessible mountain areas supported with accurate methods of the vegetation parameters estimation since the vegetation cover is first to react to externally driven disturbances. The current study was conducted in Dzungarian Alatau Mountains, inhabited by the rare and endemic anuran amphibian Ranodon sibiricus, Kessler, 1886. The project of the Conservation International Foundation (CIF/326/21) was aimed at the strategy of this species conservation. The current study emphasises estimating the overgrazing risks for the amphibian population. More than two hundred ground measurements were done within the Upper Koksu Forestry and adjacent areas to provide representative data on the vegetation parameters. We tested a series of spectral indices related to vegetation biophysical parameters. We found DWSI, GrNDVI, IRECI and NDI45 indices to provide the best correlations and reasonable accuracy for remote measurements of above-ground biomass, grasscover and unpalatable grass content. Sentinel-2 data with the red-edge bands, in most cases, provided better performance. Our study confirmed the use of a single criterion (like above-ground biomass) might result in a serious underestimation of grassland degradation. Data obtained from field survey and satellite information analysis allowed the evaluation of the optimal grazing load for the Upper Koksu Forestry and provided recommendations for the Action Plan on the conservation of Ranodon sibiricus.

A Methodological Framework for Mapping Canopy Cover Using ICESat-2 in the Southern USA

NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides exceptional opportunities for characterizing the structure of ecosystems through the acquisition of along-track, three-dimensional observations. Focusing on canopy cover as a fundamental parameter for assessing forest conditions, the overall goal of this study was to establish a framework for generating a gridded 30 m canopy cover product with ICESat-2. Specifically, our objectives were to (1) Determine and compare ICESat-2-derived canopy cover with airborne lidar-derived and the 2016 National Land Cover Database (NLCD) cover product estimates, and (2) Evaluate a methodology for wall-to-wall mapping of canopy cover. Using two Southern US sites, the Sam Houston National Forest (SHNF) in south-east Texas and the Solon Dixon Forestry Education Center (SDFEC) in southern Alabama, four measures of canopy cover estimated with ICESat-2′s Land-Vegetation Along-Track Product, or ATL08, were evaluated at the 30 m pixel scale. Comparisons were made using spatially coinciding NLCD pixels and airborne lidar-derived reference canopy cover. A suite of Landsat and Landsat-derived parameters were then used as predictors to model and map each measure of canopy cover with Random Forests (RF), and their accuracies were assessed and compared. Correlations (r) between ICESat-2-derived and airborne lidar canopy cover at the pixel scale ranged from 0.57 to 0.78, and R2 up to 0.81 was produced between NLCD and ICESat-2-derived canopy cover. RF models developed for extrapolating ICESat-2-derived canopy cover estimate yielded R2 values between 0.50 and 0.61 (RMSEs between 16% and 20%) when evaluated with airborne lidar-derived canopy cover. With a demonstrated capability of ICESat-2 to estimate vegetation biophysical parameters, the findings serve to support the spatially comprehensive mapping of other vegetation attributes, especially forest aboveground biomass, and contribute to the development of an up-to-date gridded canopy cover product.

Potential Assessment of PRISMA Hyperspectral Imagery for Remote Sensing Applications

Hyperspectral imagery plays a vital role in precision agriculture, forestry, environment, and geological applications. Over the past decade, extensive research has been carried out in the field of hyperspectral remote sensing. First introduced by the Italian Space Agency ASI in 2019, space-borne PRISMA hyperspectral imagery (PHSI) is taking the hyperspectral remote sensing research community into the next era due to its unprecedented spectral resolution of ≤12 nm. Given these abundant free data and high spatial resolution, it is crucial to provide remote sensing researchers with information about the critical attributes of PRISMA imagery, making it the most viable solution for various land and water applications. Hence, in the present study, a SWOT analysis was performed for PHSI using recent case studies to exploit the potential of PHSI for different remote sensing applications, such as snow, soil, water, natural gas, and vegetation. From this analysis, it was found that the higher reflectance spectra of PHSI, which have comprehensive coverage, have greater potential to extract vegetation biophysical parameters compared to other applications. Though the possible use of these data was demonstrated in a few other applications, such as the identification of methane gases and soil mineral mapping, the data may not be suitable for continuous monitoring due to their limited acquisition, long revisiting times, noisy bands, atmospheric interferences, and computationally heavy processing, particularly when executing machine learning models. The potential applications of PHSI include large-scale and efficient mapping, transferring technology, and fusion with other remote sensing data, whereas the lifetime of satellites and the need for interdisciplinary personnel pose challenges. Furthermore, some strategies to overcome the aforementioned weaknesses and threats are described in our conclusions.

Predicting Leaf Phenology in Forest Tree Species Using UAVs and Satellite Images: A Case Study for European Beech (Fagus sylvatica L.)

Understanding forest tree phenology is essential for assessing forest ecosystem responses to environmental changes. Observations of phenology using remote sensing devices, such as satellite imagery and Unmanned Aerial Vehicles (UAVs), along with machine learning, are promising techniques. They offer fast, accurate, and unbiased results linked to ground data to enable us to understand ecosystem processes. Here, we focused on European beech, one of Europe’s most common forest tree species, along an altitudinal transect in the Carpathian Mountains. We performed ground observations of leaf phenology and collected aerial images using UAVs and satellite-based biophysical vegetation parameters. We studied the time series correlations between ground data and remote sensing observations (GLI r = 0.86 and FCover r = 0.91) and identified the most suitable vegetation indices (VIs). We trained linear and non-linear (random forest) models to predict the leaf phenology as a percentage of leaf cover on test datasets; the models had reasonable accuracy, RMSE percentages of 8% for individual trees, using UAV, and 12% as an average site value, using the Copernicus biophysical parameters. Our results suggest that the UAVs and satellite images can provide reliable data regarding leaf phenology in the European beech.

Estimating crop coefficients and actual evapotranspiration in citrus orchards with sporadic cover weeds based on ground and remote sensing data

Accurate estimations of actual crop evapotranspiration are of utmost importance to evaluate crop water requirements and to optimize water use efficiency. At this aim, coupling simple agro-hydrological models, such as the well-known FAO-56 model, with remote observations of the land surface could represent an easy-to-use tool to identify biophysical parameters of vegetation, such as the crop coefficient Kc under the actual field conditions and to estimate actual crop evapotranspiration. This paper intends, therefore, to propose an operational procedure to evaluate the spatio-temporal variability of Kc in a citrus orchard characterized by the sporadic presence of ground weeds, based on micro-meteorological measurements collected on-ground and vegetation indices (VIs) retrieved by the Sentinel-2 sensors. A non-linear Kc(VIs) relationship was identified after assuming that the sum of two VIs, such as the normalized difference vegetation index, NDVI, and the normalized difference water index, NDWI, is suitable to represent the spatio-temporal dynamics of the investigated environment, characterized by sparse vegetation and the sporadic presence of spontaneous but transpiring soil weeds, typical of winter seasons and/or periods following events wetting the soil surface. The Kc values obtained in each cell of the Sentinel-2 grid (10 m) were then used as input of the spatially distributed FAO-56 model to estimate the variability of actual evapotranspiration (ETa) and the other terms of water balance. The performance of the proposed procedure was finally evaluated by comparing the estimated average soil water content and actual crop evapotranspiration with the corresponding ones measured on-ground. The application of the FAO-56 model indicated that the estimated ETa were characterized by root-mean-square-error, RMSE, and mean bias-error, MBE, of 0.48 and -0.13 mm d−1 respectively, while the estimated soil water contents, SWC, were characterized by RMSE equal to 0.01 cm3 cm−3 and the absence of bias, then confirming that the suggested procedure can produce highly accurate results in terms of dynamics of soil water content and actual crop evapotranspiration under the investigated field conditions.

Improving the Estimation of Apple Leaf Photosynthetic Pigment Content Using Fractional Derivatives and Machine Learning

As a key functional trait, leaf photosynthetic pigment content (LPPC) plays an important role in the health status monitoring and yield estimation of apples. Hyperspectral features including vegetation indices (VIs) and derivatives are widely used in retrieving vegetation biophysical parameters. The fractional derivative spectral method shows great potential in retrieving LPPC. However, the performance of fractional derivatives and machine learning (ML) for retrieving apple LPPC still needs to be explored. The objective of this study is to test the capacity of using fractional derivative and ML methods to retrieve apple LPPC. Here, the hyperspectral data in the 400–2500 nm domains was used to calculate the fractional derivative order of 0.2–2, and then the sensitive bands were screened through feature dimensionality reduction to train ML to build the LPPC estimation model. Additionally, VIs-based ML methods and empirical regression models were developed to compare with the fractional derivative methods. The results showed that fractional derivative-driven ML methods have higher accuracy than the ML methods driven by the original spectra or vegetation index. The results also showed that the ML methods perform better than empirical regression models. Specifically, the best estimates of chlorophyll content and carotenoid content were achieved using support vector regression (SVR) at the derivative order of 0.2 (R2 = 0.78) and 0.4 (R2 = 0.75), respectively. The fractional derivative maintained a good universality in retrieving the LPPC of multiple phenological periods. Therefore, this study highlights that the fractional derivative and ML improved the estimation of apple LPPC.

Far-field bistatic scattering simulation for rice crop biophysical parameters retrieval using modified radiative transfer model at X- and C-band

Dual-polarimetric (i.e., HH and VV) scattering responses at X- and C-bands from indigenously designed far-field bistatic specular (bi-spec) scatterometer acquired over the entire rice crop phenology have been analyzed using a modified parametric radiative transfer model (MRTM). The scattering responses are examined over a wide-ranging bi-spec incidence angle varying from 20° to 60° at 10° intervals. Furthermore, optimization of the bi-spec scatterometer system showed high sensitivity at 40° specular angle of incidence based on the correlation analysis between the measured value of bi-spec scattering coefficient (σMeasured0) and vegetation biophysical parameters such as leaf area index (LAI) and plant water content (PWC). The MRTM implied to investigate the dominance of surface (σSurface0) and vegetation(σVegetation0) specular scattering components within the total value of simulated bi-spec scattering coefficient (σSimulated0) in forward scattering alignment (FSA) convention. The vegetation phase function (VPF) and a bi-directional reflectance distribution function (BRDF) are parameterized to approximate scattering responses from the vegetation volume layer and the surface beneath vegetation. In addition, empirical frequency-specific parameters (i.e., b1and b2) are used to simulate temporal dynamics of σSimulated0 using a linear relationship between vegetation optical depth (VOD) with LAI and PWC. The model and empirical frequency-specific parameters are calibrated using a constrained non-linear least square optimization algorithm, and the results are validated against the value of σMeasured0. According to the simulation findings, the total specular scattering decomposition offers a robust model for interpreting time-series microwave scattering scenarios through vegetation in the FSA convention. Moreover, as compared to C-band, the inverse modeling of MRTM showed high retrieval accuracies of LAI at VV polarization and PWC at HH polarization for the X-band.

Extending the GOSAILT Model to Simulate Sparse Woodland Bi-Directional Reflectance with Soil Reflectance Anisotropy Consideration

Anisotropic canopy reflectance plays a crucial role in estimating vegetation biophysical parameters, whereas soil reflectance anisotropy affects canopy reflectance. However, woodland canopy bidirectional reflectance distribution function (BRDF) models considering soil anisotropy are far from universal, especially for the BRDF models of mountain forest. In this study, a mountain forest canopy model, named geometric-optical and mutual shadowing and scattering from arbitrarily inclined-leaves model coupled with topography (GOSAILT), was extended to consider the soil anisotropic reflectance characteristics by introducing the simple soil directional (SSD) reflectance model. The modified GOSAILT model (named GOSAILT-SSD) was evaluated using unmanned aerial vehicle (UAV) field observations and discrete anisotropic radiative transfer (DART) simulations. Then, the effects of Lambertian soil assumption on simulating the vi-directional reflectance factor (BRF) were evaluated across different fractions of vegetation cover (Cv), view zenith angles (VZA), solar zenith angles (SZA), and spectral bands with the GOSAILT-SSD model. The evaluation results, with the DART simulations, show that the performance of the GOSAILT-SSD model in simulating canopy BRF is significantly improved, with decreasing RMSE, from 0.027 to 0.017 for the red band and 0.051 to 0.037 for the near-infrared (NIR) band. Meanwhile, the GOSAILT-SSD simulations show high consistency with UAV multi-angular observations (R2 = 0.97). Besides, it is also found that the BRF simulation errors caused by Lambertian soil assumption are too large to be neglected, with a maximum relative bias of about 45% for the red band. This inappropriate assumption results in a remarkable BRF underestimation near the hot spot direction and an obvious BRF overestimation for large VZA in the solar principal plane (PP). Meanwhile, this simulation bias decreases with the increase of fraction of vegetation cover. This study provides an effective technique to improve the capability of the mountain forest canopy BRDF model by considering the soil anisotropic characteristics for advancing the modeling of radiative transfer (RT) processes over rugged terrain.

A Physics-Based Algorithm to Couple CYGNSS Surface Reflectivity and SMAP Brightness Temperature Estimates for Accurate Soil Moisture Retrieval

Remotely sensed soil moisture (SM) with high accuracy and high spatial&#x2013;temporal resolution is crucial to meteorological, agricultural, hydrological, and environmental applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that uses the L-band signal transmitted by the GNSS satellites to develop daily SM data products. In this study, a physics-based algorithm is proposed to couple CYGNSS surface reflectivity (SR) and Soil Moisture Active Passive (SMAP) brightness temperature estimates for accurate SM retrieval. The algorithm is based on the radiative transfer model and the SMAP data to derive a combined parameter of the vegetation optical depth (<inline-formula> <tex-math notation="LaTeX">$\tau$ </tex-math></inline-formula>) and the surface roughness parameter (<inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula>). The CYGNSS L1 Version 2.1 data of the years 2017&#x2013;2018 and 2019&#x2013;2020 are used for calibration and validation, respectively. The SM estimates agree and correlate well with the SMAP SM and <i>in situ</i> SM data on a global scale (<inline-formula> <tex-math notation="LaTeX">$R =0.679$ </tex-math></inline-formula>, RMSE <inline-formula> <tex-math notation="LaTeX">$=0.051\,\,\text{m}^{3}\text{m}^{-3}$ </tex-math></inline-formula>, and MAE <inline-formula> <tex-math notation="LaTeX">$=0.045\,\,\text{m}^{3}\text{m}^{-\mathrm {3}}$ </tex-math></inline-formula> against SMAP SM; <inline-formula> <tex-math notation="LaTeX">$R = 0.729$ </tex-math></inline-formula> against <i>in situ</i> SM). The proposed algorithm makes contributions from two aspects. First, the proposed algorithm provides a physics-based algorithm using SMAP brightness temperature to calibrate the attenuation due to vegetation and surface roughness on the CYGNSS-derived SR. Unlike attenuation models that have been explored previously in the context of CYGNSS, this algorithm executes the calibration without relying on observations of <inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula> or vegetation biophysical parameters as inputs but with the SMAP brightness temperature as the only observations. Second, the proposed algorithm provides a new way for the combined usage of CYGNSS and SMAP to improve the temporal and spatial coverages of global SM with temporal coverage increased by 38.2&#x0025; and spatial coverage increased by 31.6&#x0025;.

Application of the Trace Coherence to HH-VV PolInSAR TanDEM-X Data for Vegetation Height Estimation

This article investigates, for the first time, the inclusion of the operator Trace Coherence (TrCoh) in polarimetric and interferometric synthetic aperture radar (SAR) methodologies for the estimation of biophysical parameters of vegetation. A modified inversion algorithm based on the well-known Random Volume over Ground (RVoG) model, which employs the TrCoh, is described and evaluated. In this regard, a different set of coherence extrema is used as input for the retrieval stage. In addition, the proposed methodology improves the inversion algorithm by employing analytical solutions rather than approximations. Validation is carried out exploiting single-pass HH-VV bistatic TanDEM-X data, together with reference data acquired over a paddy rice area in Spain. The added value of the TrCoh and the convenience of the use of analytical solutions are assessed by comparing with the conventional polarimetric SAR interferometry (PolInSAR) algorithm. Results demonstrate that the modified proposed methodology is computationally more effective than current methods on this dataset. For the same scene, the steps required for inversion are computed in 6 min with the conventional method, while it only takes 6 s with the proposed approach. Moreover, vegetation height estimates exhibit a higher accuracy with the proposed method in all fields under evaluation. The root-mean-squared error reached with the modified method improves by 7 cm with respect to the conventional algorithm.

BOST: A Canopy Reflectance Model Suitable for Both Continuous and Discontinuous Canopies Over Sloping Terrains

Canopy reflectance models over sloping terrain are critical and reliable tools for vegetation biophysical parameter retrieval. However, the applicability of existing models is strictly limited by individual canopy structure, such as continuous or discontinuous canopies, and hybrid vegetation structure is seldom considered in current models. Therefore, this defect greatly limits the application of canopy reflectance models in rugged terrains. To overcome this problem, we developed a canopy reflectance model suitable for both continuous and discontinuous canopies over sloping terrains (BOST). The scattering contributions of different canopies can be estimated by incorporating clumping indices; thus, we designed an algorithm to convert the clumping indices from flat terrain to sloping terrains, which explicitly considers the terrain effect on radiative transfer within canopies. In model validations, multiple schemes were used to obtain the objective evaluation results, including analyzing the terrain induced clumping index variations, comparing with the computer simulation model, typical canopy reflectance models, and the image reflectance in a real scene. The results suggested that the BOST model can capture the pattern of terrain-induced scattering components and reflectance distortions, and be successfully used in simulating the reflectance in a real mountainous region [RMSE (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) values of 0.0016 (0.889), 0.0183 (0.932), and 0.0023 (0.903) in the red, NIR, and green bands, respectively]. BOST performed better in terms of accuracy and efficiency than the typical models. Further testing of BOST in topographic normalization and biophysical parameter retrieval showed its practical usage potential. These validations confirmed the good performance of BOST in both continuous and discontinuous vegetation scenes and indicated it can provide a promising tool to retrieve biophysical parameters in mountainous areas.

Modeling the radiation regime of a discontinuous canopy based on the stochastic radiative transport theory: Modification, evaluation and validation

Canopy radiative transfer (RT) modeling is critical for the quantitative retrieval of vegetation biophysical parameters and has been under intensive research over the decades. RT models of discontinuous canopies, such as three-dimensional (3D) RT models, posed challenges for the early one-dimensional (1D) hypothesis. Although 3D RT models have higher accuracy, theoretically, they suffer from two problems: detailed scene parameters and complex computational steps. To overcome these problems, the stochastic radiative transfer (SRT) theory, which is known to have the accuracy of 3D RT while being as simple as 1D RT, has been adapted from atmospheric research to the study of vegetation canopies. While the SRT model has been adopted into the operational production of vegetation parameters, its accuracy needs further improvement because of the insufficient consideration of hotspot effects. Additionally, the evaluation and validation of SRT process are still preliminary, which hinders its further development and application. To provide the community with missing information and a scientific basis for subsequent model improvement, we modified, evaluated, and validated the SRT model in this study. First, we proposed the new version of SRT model to better achieve the coupling of SRT process and hotspot effect by dividing the previous SRT into four subproblems. Then, we evaluated the performance of the modified SRT by comparing multiple intermediate variables in the SRT process with 3D computer simulations, and analyzed the model sensitivity to key input parameters as well as the spatial distribution and conservation of radiation energy. Our findings reconfirmed that the SRT theory can well describe the radiation regime of discontinuous canopies with balanced efficiency and accuracy. Moreover, the newly proposed coupling scheme of hotspot effect further improves the model performance in the hotspot regions. Finally, the unmanned aerial vehicle (UAV) observations served as a reference to validate the modeled canopy reflectance, which shows a high concordance. These results provide a detailed theoretical basis for applications and further improvements of the SRT model.

Revisiting the use of red and near-infrared reflectances in vegetation studies and numerical climate models

Surface reflectance data acquired in red and near-infrared spectra by remote sensing sensors are traditionally applied to construct various vegetation indices (VIs), which are related to vegetation biophysical parameters. Most VIs use pre-defined weights (usually equal to 1) for the red and NIR reflectance values, therefore constraining particular weights for red and NIR during the VI design phase, and potentially limiting capabilities of the VI to explain an independent variable. In this paper, we propose an approach to estimate biophysical variables, such as Leaf Area Index (LAI), Canopy Chlorophyll Content (CCC) and Fraction of Photosynthetically Active Radiation (FPAR) absorbed by green vegetation, represented as linear combinations of the red and NIR reflectances with weights determined empirically from observations and radiative transfer model (PROSAIL) simulations. The proof of concept is first tested on available close-range observations over maize and soybean crops in Nebraska, USA. The empirical results compare well with those from PROSAIL model simulations. The proposed LAI model is then used with data from Landsat 8, Sentinel-2 and Planet/Dove, and the results are validated with in situ LAI measurements in Ukraine. We show that the weights on red and NIR reflectances are vegetation-specific and stable in time. The approach is further tested on crops and forests in the conterminous USA and on a global scale using MODIS LAI and FPAR products as proxies for “ground observations”. These LAI and FPAR, however, are not independently measured but derived from the corresponding remotely sensed reflectances, which precludes recommending a final set of the weights/coefficients for the users, and, thus, should be considered mostly for demonstrating the concept. The results for crop types, other than maize and soybean, and for all forests are conceptual and need to be tested with real ground data. It was, however, encouraging to see that the derived maps of coefficients/weights exhibit regular patterns over the globe compatible with those of vegetation classes and crop types. Tedious and thorough work on compiling available in situ measurements on various crops and forests needs to be accomplished prior to large-scale applications, and the method needs to be further tested and proven that it works at a large scale.The proposed parameterization may be attractive for global studies of various sub-classes of vegetation, once the parameter coefficients are established, validated, tabulated and their stability verified. Ultimately, this approach may provide quantification of vegetation traits for the past decades and be a useful asset for climate models that include satellite-derived land cover classifications and vegetation variables for simulating surface fluxes.This is a conceptual paper, with a proof-of-concept supported by observations over two crops, for which we had close-range observations. It is not a technical note, which would provide users with a recommended set of coefficients for global applications. Our intent was to develop a paradigm, which could ultimately be useful in global models.

Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data

Yellow rust, caused by Puccinia striiformis f. sp. Tritici, is a serious disease attacking wheat (Triticum aestivum L.) across the globe. The occurrence of yellow rust can result in severe yield reduction and economic loss. Hyperspectral remote sensing has demonstrated potential in detecting yellow rust, with the majority of studies distinguishing qualitatively between diseased and healthy individuals or performing simple grading of disease severity. However, research on the quantification of the severity of yellow rust is limited. To fill this gap in the literature, in the current study, we constructed a new spectral index, the yellow rust optimal index (YROI), using the hyperspectral data obtained by ASD field spectrometer to quantitatively estimate yellow rust severity. The index is based on the spectral response of spores, and vegetation biophysical and biochemical parameters (VPCPs); and integrated with the PROSPECT-D model. We evaluated the new index and compared it with 11 commonly used yellow rust detection indices using experimental leaf- and canopy-scale spectral datasets. Results demonstrated the superior accuracy of YROI for both the leaf (R2 = 0.822, RMSE = 0.070) and canopy (R2 = 0.542, RMSE = 0.085) scales. In this research, we quantitatively analyzed the spectral response mechanism of wheat yellow rust, which provided a new idea for the quantitative identification of crop diseases. Moreover, our results can be employed as a reference and theoretical basis for the accurate and timely quantitative identification of crop diseases over the large areas in the future.