Retrieval Of Biophysical Parameters Research Articles

Assessment of the applicability of atmospheric correction algorithms for Type I and Type II water bodies based on in-situ data - a case study using sentinel data

ABSTRACT The application of remote sensing technology in the retrieval of marine biophysical and physical parameters is becoming increasingly widespread. Accurate atmospheric correction is an essential prerequisite for subsequent retrieval of biophysical and physical parameters of water bodies. However, different atmospheric correction algorithms exhibit significant regional applicability variations across various water body types, requiring researchers to select suitable atmospheric correction algorithms for their study areas. In this study, utilizing Sentinel-2 and Sentinel-3 satellite data sources, global publicly available datasets, and in-situ synchronous measured reflectance data for Type I and Type II water bodies, we evaluated the accuracy of atmospheric correction using three types of algorithms based on radiative transfer, dark spectrum fitting, and neural networks. We analysed the sources of algorithm errors and their applicability from both algorithm principles and water body properties, providing criteria for selecting suitable atmospheric correction algorithms for different characteristic water bodies and proposing recommendations for improving correction accuracy.

Validation of Biophysical Parameters of Wheat Retrieved from Sentinel-2 Data Using in-situ Measurements at Anand Region of India

The purpose of this study was to evaluate the accuracy of the Sentinel-2 chlorophyll content and the Leaf Area Index (LAI) retrieved via SNAP toolbox using in situ observed biophysical parameters. SL2B plugins were tested for accuracy and consistency through validation and comparison of spatial variation. The SNAP processor showed good correlations for Leaf Area Index (LAI) in the first year, and it was particularly successful in some selected fields. For the various areas, satisfactory accuracy was maintained in the second year. The findings revealed a reasonable R2, ranging from 0.5 to 0.7, between in-situ LAI and SNAP-retrieved LAI, but with high errors. In chlorophyll assessment, the average estimates of SL2P-derived values were 20 – 90 µg/cm², against the mean values of 40–50 µg/cm² as observed in the field. The standard deviation for the chlorophyll generated from SL2P, albeit variable, was moderate (22.68 µg/cm²). The scatter plots highlighted a close agreement between retrieved and measured values, reinforcing the reliability of the data. Retrievals of LAI and chlorophyll were accurate for Anand district. It highlights the potential of Sentinel 2 data for reliable retrieval of biophysical parameters of wheat.

Optimizing Sentinel-2 feature space for improved crop biophysical and biochemical variables retrieval using the novel spectral triad feature selection algorithm

This study presents a novel Spectral Triad feature selection (STfs) technique based on music theory and compares it to the entire Sentinel-2 feature space and Random Forest-Recursive Feature Elimination (RF-RFE). The optimal subsets were evaluated with Random Forest for retrieving Leaf Area Index (LAI), Leaf Chlorophyll Content (LC ab ), and Canopy Chlorophyll Content (CCC) in a semi-arid agricultural landscape. The results indicated that the proposed STfs algorithm obtained equivalent or better (i.e. by 1 – 3%) retrieval accuracies for LAI (R 2 cv of 66%, root mean squared error of cross-validation [RMSEcv] of 0.53 m2 m−2), LC ab (R 2 cv: 74%, RMSEcv: 7.09 µg cm−2) and CCC (R 2 cv: 77%, RMSEcv: 33.69 µg cm−2), using only 5, 7 and 7 variables, respectively, when compared to RF-RFE and entire Sentinel-2 feature space. Overall, the proposed STfs algorithm has great potential to optimize the spectral feature space of quasi-hyperspectral sensors for rapid crop biophysical and biochemical parameter retrieval.

Analysis of the Directional Characteristics of the Clumping Index (CI) Based on RAMI-V Canopy Scenes

The vegetation canopy clumping index (CI) is an important indicator for understanding radiative transport processes, radiation interception, and the photosynthesis of vegetation canopies. However, most studies consider CI only in the nadir or specific direction. In this study, we analyze the directional characteristics of the CI based on RAMI-V (radiation transfer model intercomparison) activity, which represents most typical canopies. The directional gap fraction and CI of these scenes are accurately calculated based on the LESS (large-scale remote sensing data and image simulation framework) model. According to our results, the directional characteristics of the CI are affected by many factors, such as vegetation type, season, and canopy structure. Generally, the CI of a coniferous forest varies little with zenith angle, while the CI of a broad-leaf forest demonstrates the different trend. In winter, the CI is smaller than that in summer, and the variation in the CI at the zenith angle is less. The row structure scenes exhibit different directional characteristics along and perpendicular to the row direction, and their CIs tend to increase with zenith angle. To accurately model the directional CI, we propose a modified Gompertz function model. Compared with other directional CI models, this model has the advantages of high precision and strong applicability ( R 2 = 0.975). By studying the directional characteristics of CI, we can enhance the usability of radiative transfer modeling and the accuracy of canopy biophysical parameter retrieval for vegetation with different structures.

Full-band, multi-angle, multi-scale, and temporal dynamic field spectral measurements in China

Field-measured spectra are critical for remote sensing physical modelling, retrieval of structural, biophysical, and biochemical parameters, and other practical applications. We present a library of field spectra, which includes (1) portable field spectroradiometer measurements of vegetation, soil, and snow in the full-wave band, (2) multi-angle spectra measurements of desert vegetation, chernozems, and snow with consideration of the anisotropic reflectance of land surface, (3) multi-scale spectra measurements of leaf and canopy of different vegetation cover surfaces, and (4) continuous reflectance spectra time-series data revealing vegetation growth dynamics of maize, rice, wheat, rape, grassland, and so on. To the best of our knowledge, this library is unique in simultaneously providing full-band, multi-angle, multi-scale spectral measurements of the main surface elements of China covering a large spatial extent over a 10-year period. Furthermore, the 101 by 101 satellite pixels of Landsat ETM/OLI and MODIS surface reflectance centered around the field site were extracted, providing a vital linkage between ground measurements and satellite observations. The code language used for this work is Matlab 2016a.

Testing Sentinel-2 spectral configurations for estimating relevant crop biophysical and biochemical parameters for precision agriculture using tree-based and kernel-based algorithms

Sentinel-2 spectral configurations, S2-10m and S2-20m, were evaluated for retrieving essential crop biophysical and biochemical parameters and their effect on the performance of three machine learning regression algorithms (MLRAs) in two African semi-arid sites. The results were benchmarked against all spectral bands (S2-All). The results show that the S2-20m was more robust in retrieving Leaf Area Index (LAI) (RMSE cv : 0.58 m2 m−2, 0.47 m2 m−2), while the S2-10m provided optimal retrievals Leaf Chlorophyll a + b (LC ab ) (RMSE cv : 6.89 µg cm−2, 7.02 µg cm−2) for the two sites, respectively. In contrast, S2-20m performed better in retrieving Canopy Chlorophyll Content (CCC) in Bothaville to an RMSE cv of 35.65 µg cm−2, while S2-10m yielded relatively lower uncertainties (RMSE cv of 26.84 µg cm−2) in Harrismith. Moreover, various MLRAs were sensitive to the various spectral configurations, and performance varied by site. GPR and XGBoost were more robust, and thus have the most potential for crop biophysical and biochemical parameter retrieval in both sites. Based on the benchmark results, the two configurations can be used independently. The results obtained here are relevant for the rapid development of essential crop biophysical and biochemical parameters for precision agriculture using Sentinel-2’s 10 m or 20 m bands, without the need for resampling.

Gaussian Process Regression Model for Crop Biophysical Parameter Retrieval from Multi-Polarized C-Band SAR Data

Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop growth and helping farmers make market decisions. Many parametric and non-parametric machine learning techniques have been utilized to estimate these parameters. A general non-parametric approach that follows a Bayesian framework is the Gaussian Process (GP). The parameters of this process-based technique are assumed to be random variables with a joint Gaussian distribution. The purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data. RADARSAT-2 full-polarimetric images and in situ measurements of wheat, canola, and soybeans obtained from the SMAPVEX16 campaign over Manitoba, Canada, are used to evaluate the performance of these GPR models. The results from this research demonstrate that both the full-pol (HH+HV+VV) combination and the dual-pol (HV+VV) configuration can be used to estimate PAI, VWC, and WB for these three crops.

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.

Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest

Light detection and ranging (LiDAR) has contributed immensely to forest mapping and 3D tree modelling. From the perspective of data acquisition, the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels. This research develops a general framework to integrate ground-based and UAV-LiDAR (ULS) data to better estimate tree parameters based on quantitative structure modelling (QSM). This is accomplished in three sequential steps. First, the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy. Next, redundancy and noise were removed for the ground-based/ULS LiDAR data fusion. Finally, tree modeling and biophysical parameter retrieval were based on QSM. Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest, including poplar and dawn redwood species. Generally, ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data. The fusion-derived tree height, tree volume, and crown volume significantly improved by up to 9.01%, 5.28%, and 18.61%, respectively, in terms of r RMSE. By contrast, the diameter at breast height (DBH) is the parameter that has the least benefits from fusion, and r RMSE remains approximately the same, because stems are already well sampled from ground data. Additionally, particularly for dense forests, the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR. Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests, whereby the improvement owing to fusion is not significant. • Mitigation of the limitations of tree localization in case of ground-based/ULS LiDAR data co-registration using virtual keypoints. • Fusion of ground-based/ULS LiDAR data based on removal of redundant and noisy points. • Investigation and evaluation of the utility of ground-based/ULS LiDAR data fusion for tree parameter estimation. • Ground-based/ULS LiDAR data fusion improved estimation of tree height, tree volume, and crown volume. • Ground-based/ULS LiDAR data fusion has strong potential in QSM of trees particularly in high-stand-density forest.

Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms

With the availability of high-resolution data due to sensor technology advancement, it is now easier for researchers and scientists to detect or view the spectral variability of different crops. For this study, Leaf chlorophyll content (LCC) and Leaf area index (LAI) of the crops Maize (Zea mays), Mustard (Brassica), and pink Lentils (Lens esculenta) under different irrigation and fertilizer treatments have been analyzed. In total, rigorous assessment of 25-hyperspectral vegetation indices (VIs) at both leaf and canopy level for chlorophyll content, whereas 7- hyperspectral VIs for LAI at canopy level were computed to investigate the robustness of these VIs for LCC and LAI assessment. Variable importance in projection (VIP) using Partial Least Square regression (PLSR) and coefficient of determination (R2) were computed for all the VIs to extract the most sensitive information for the retrieval of LCC and LAI. As a result, the VIs using the red-edge reflectance bands at 705 and 750 nm were found highly responsive to LAI compared to other wavebands. In contrast, the VIs indices made of green (550 nm), red (670, 690, and 700 nm), and red-edge (705, 750 nm) bands were found highly sensitive to the temporal LCC values of lentils and maize crop beds. In addition, the temporal LCC values of Mustard crop beds’ were found sensitive to the VIs made of green (550 nm), red (670, 690, and 700 nm), and NIR (800 nm) wavebands. The three VIs having high VIP and R2 values were selected as optimum sets of input to build support vector regression models using radial (SVR-Rad), linear (SVR-Li), polynomial (SVR-Poly), Random Forrest Regression (RFR), Partial least square regression (PLSR), and Hybrid neural fuzzy inference system (HyFIS). The analysis showed that the SVR-Rad model outperformed the SVR-Li, SVR-Poly, RFR, PLSR, and HyFIS models in terms of robustness for biophysical and biochemical parameters retrieval using hyperspectral data.

A multi-year cross-validation experiment for estimating rice plant area index (PAI) over the JECAM-India test site from simulated RADARSAT constellation mission (RCM) compact polarimetric SAR data

ABSTRACT Using the cross-validation approach, strategies for estimating biophysical parameters are still pre-operational with synthetic aperture radar (SAR) data. In this regard, the Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR inter-comparison experiments provide an opportunity for the potential implementation of cross-validation strategies for biophysical parameters retrieval utilizing the next-generation compact polarimetric (CP) modes available from the RADARSAT Constellation Mission (RCM). This work first uses the conventional semi-empirical Water Cloud Model (WCM) modified by exploiting the scattering power decompositions of CP measurements to estimate the Plant Area Index (PAI) for rice. The modified WCM (MWCM) is then inverted using the scattering power components from the decomposition. We compare the PAI estimates using MWCM- between the estimates obtained from (1) the conventional WCM using the RH and RV backscatter intensities and (2) MWCM- decomposition scattering powers. We exploit a time series of simulated compact-pol SAR data over the JECAM test site in Vijayawada, India, throughout 2018 and 2019. We use the C-band RADARSAT-2 full-pol data to simulate the RADARSAT Constellation Mission (RCM) compact-pol mode data. Utilizing the advantage of systematically collected multi-year SAR data and in-situ measurements, the present research also assesses the calibrated model transferability performances to another data set and cross-validation of a model in a multi-year experiment setting. The comparative analysis indicates potential improvements in PAI estimation with MWCM- scattering powers. A high range of correlation coefficient () between the estimated and observed PAI is observed with good Root Mean Square Error (RMSE) of m2 m−2, and Mean Absolute Error (MAE) of m2 m−2.

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.

Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages

ABSTRACT Leaf area index (LAI) and canopy chlorophyll density (CCD) are key indicators of crop growth status. In this study, we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages. The indices were calculated with Sentinel-2 MSI data and hyperspectral data. Their performances were validated against ground measurements using R2, RMSE, and bias. The results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage, head emergence stage, and filling stage. Furthermore, red-edge modified indices outperformed the traditional indices for both data genres. Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early jointing and milk development stage, while indices with long red-edge wavelength estimate the sought variables better at the middle three stages. The results were consistent with the red-edge inflection point shift at different growth stages. The best indices for Sentinel-2 LAI retrieval, Sentinel-2 CCD retrieval, hyperspectral LAI retrieval, and hyperspectral CCD retrieval at five growth stages were determined in the research. These results are beneficial to crop trait monitoring by providing references for crop biophysical and biochemical parameters retrieval.

Retrieval and validation of forest background reflectivity from daily Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) data across European forests

Abstract. Information about forest background reflectance is needed for accurate biophysical parameter retrieval from forest canopies (overstory) with remote sensing. Separating under- and overstory signals would enable more accurate modeling of forest carbon and energy fluxes. We retrieved values of the normalized difference vegetation index (NDVI) of the forest understory with the multi-angular Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo data (gridded 500 m daily Collection 6 product), using a method originally developed for boreal forests. The forest floor background reflectance estimates from the MODIS data were compared with in situ understory reflectance measurements carried out at an extensive set of forest ecosystem experimental sites across Europe. The reflectance estimates from MODIS data were, hence, tested across diverse forest conditions and phenological phases during the growing season to examine their applicability for ecosystems other than boreal forests. Here we report that the method can deliver good retrievals, especially over different forest types with open canopies (low foliage cover). The performance of the method was found to be limited over forests with closed canopies (high foliage cover), where the signal from understory becomes too attenuated. The spatial heterogeneity of individual field sites and the limitations and documented quality of the MODIS BRDF product are shown to be important for the correct assessment and validation of the retrievals obtained with remote sensing.

UAVSAR Tomography for Vertical Profile Generation of Tropical Forest of Mondah National Park, Gabon

AbstractPolarimetric synthetic‐aperture radar (SAR) remote sensing has been widely used for structural and biophysical parameters retrieval of forest vegetation. It has been found that the combination of polarimetric properties and interferometric characteristics of SAR remote sensing provides the capacity to retrieve forest height. The prime objective of this research was to investigate the potential of Polarimetric Synthetic Aperture Radar Tomography (PolTomSAR) for the forested and river region of Mondah National Park, Gabon. SAR tomography is an improved method for acquiring the height of geographical features. UAVSAR L‐band fully polarimetric multibaseline data have been used in this research (1.275 GHz). SAR data and ground data over the area have been collected in the year 2016. With the superresolution‐based Capon algorithm, multiple scatterers located at a different vertical position in the same azimuth range cell has been resolved and reconstructed. This work provides a framework for Capon‐based tomographic processing of multibaseline UAVSAR data for vertical profile retrieval of forest vegetation. The height profile of the forest patch having sparse, as well as dense vegetation, were retrieved. The vertical profile for a single azimuthal bin was obtained in range direction. The tomographic profile obtained was cross‐checked with the field‐measured forest height for the 16 locations in Mondah National Park, Gabon. To check the accuracy of the applied method, the statistical method of R2 and root‐mean‐square error (RMSE) is employed. The obtained RMSE of the result is 4.21 m and R2 is 0.92. The obtained results were concluded to find the potential of the Capon algorithm for the tomographic reconstruction of UAVSAR data.

Assessing the impact of drought stress on grasslands using multi-temporal SAR data of Sentinel-1: a case study in Eastern Cape, South Africa

ABSTRACT Grasslands are a major component of the natural vegetation in South Africa, it covers almost one-third of the country’s land surface. The current study focuses on assessing the impact of drought stress on communal and commercial grasslands in Eastern Cape Province using multi-temporal analysis of Sentinel-1 SAR data. Data analysis included retrieval of biophysical parameters of grasslands using Sentinel-1 SAR backscattering coefficients. Correlation analysis was performed between SAR data and NDVI values derived from Landsat-8 optical data. A linear mixed-effects regression analysis was performed for detecting the impact of drought on communal and commercial grasslands during a drought and non-drought growing seasons. Our results indicate that vegetation dynamics in grasslands in the study area are highly responsive to the climatic fluctuations. A significant correlation between backscattering coefficients of Sentinel-1 SAR data (VH and VV) and NDVI was observed for both communal and commercial grasslands (R2 = 0.89). Results also demonstrated that communal grasslands are more affected by drought impact than commercial grasslands. In commercial grasslands, management activities can improve the growing conditions of grasslands, reduce the impact of drought, and subsequently increase the resilience and productivity of this ecosystem.

Canopy modeling of aquatic vegetation: A geometric optical approach (AVGO)

Aquatic vegetation is an important component in the ecosystems of wetlands and coastal waters, for example it can take up harmful substances and pollutants from the water. Constructing a remote sensing model of aquatic vegetation can deepen our understanding of the spectral characteristics and directional reflection characteristics of the aquatic vegetation canopy under various environmental conditions. However, the existing aquatic vegetation models are limited to continuously distributed canopies. This paper proposes an aquatic-vegetation- geometric-optical (AVGO) model at canopy scale for discrete aquatic vegetation. After dividing the reflectance of water background into specular components and diffuse components, the reflectances of all reflecting surfaces are classified into different reflectance components based on the differences in their materials and on whether the reflectors are illuminated by direct sunlight. Then the scene bidirectional reflectance factor (BRF) is regarded as an area-weighted sum of different reflectance components. Detailed verifications were conducted to ensure that the simulation model operates as intended. The performance of the AVGO model was qualitatively validated with the WCRM model and showed the physical plausibility of the model prediction. Quantitative validations against an artificially designed experiment were consistent with the bidirectional reflectance and the nadir-viewed spectral reflectance. This model can be used for canopy modeling of discrete emergent or submerged aquatic vegetation. Potential applications include retrieval of biophysical or biochemical parameters and aquatic vegetation abundance based on hyperspectral data.

Modelling gross primary productivity in tropical savanna pasturelands for livestock intensification in Brazil

In the grass-fed cattle ranching sector, precise and accurate information on the available dry biomass for consumption is of fundamental importance for the economically and environmentally sustainable meat production. In extensive pasture areas, as those found in Brazil, the use of orbital remote sensing data can be instrumental for the fast retrieval of biophysical parameters related to biomass production, based on which the livestock intensification potential can be determined. Within this context, in this study we estimated the dry biomass production of the State of Goiás (located in the Cerrado biome) pasturelands based on the use of a new highly precise and accurate pasture map and MODIS MOD13Q1 NDVI images (normalized difference vegetation index). Gross primary productivity (GPP) estimates, at 250m spatial resolution, were derived based on the light use efficiency of Brachiaria brizantha (dominant pasture species in the Cerrado), which determines the amount of Absorbed Photosynthetically Active Radiation (APAR) that is utilized in carbon fixation. Considering the current Animal Unit (AU) per hectare and the estimated forage production, the cattle herd in Goiás could double (reaching about 40 million heads) without the need of expanding the existing pasture areas; i.e., with the appropriate information (as the one derived in this study), proper management practices, and adequate public policies, cattle ranching in Goiás (and in Brazil) can be substantially improved (regarding productivity gains), while preventing new deforestations and increases in GHG emissions.

A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory

The availability of global high-resolution land cover maps provides promising a priori knowledge for characterizing subpixel heterogeneity and improving predictions of directional reflectance of coarse-resolution pixels. Due to mutual shadowing and sheltering effects between the adjacent forest and cropland patches, the spectral nonlinear mixing of patchy ecotones is significant, especially when the sun illuminates the ecotone from the forest side with high solar zenith angle. The spectral linear mixture (SLM) approach leads to overestimation of the bidirectional reflectance factor (BRF) in the red band in the principal plane (PP), with a maximum absolute error (MAE) of 0.0063 and a maximum relative error (MRE) of 52.5%, and to underestimation in the near-infrared band in PP with an MAE of 0.0940 and an MRE of 14.5%. In a scenario with randomly distributed boundary orientations, the overestimation of SLM increases with the degree of fragmentation and the view zenith angle. We propose a Radiative Transfer model for patchy ECotones (RTEC). which improves $R^{2}$ from 0.61 to 0.94 in the red band of Landsat-8 directional reflectance at the validation site. The RTEC model provides an efficient and analytical approach for directional reflectance predictions over heterogeneous patchy landscapes at coarse resolution and will be used for biophysical parameter retrievals [e.g., the leaf area index (LAI)] in future applications.

Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India

Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm- Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite images. The study was conducted in Central Tarai Forest Division, Haldwani, located in the Uttarakhand state, India. A total of 49 ESUs (Elementary Sampling Unit) of 30 m × 30 m size were established based on variability in composition and age of plantation stands. In-situ LAI was recorded using plant canopy imager during the leaf growing, peak and senescence seasons. The PROSAIL model was calibrated with site-specific biophysical and biochemical parameters before used to the predicted LAI. The plantation LAI was also predicted by an empirical approach using optimally chosen Sentinel-2 vegetation indices. In addition, Sentinel-2 and MODIS LAI products were evaluated with respect to LAI measurements. MLRA-GPR offered best results for predicting LAI of leaf growing (R2 = 0.9, RMSE = 0.14), peak (R2 = 0.87, RMSE = 0.21) and senescence (R2 = 0.86, RMSE = 0.31) seasons while LUT inverted model outperformed VI’s based parametric regression model. Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI.