Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Global quasi-daily fractional vegetation cover estimated from the DSCOVR EPIC directional hotspot dataset

Published versio

Reduction of structural impacts and distinction of photosynthetic pathways in a global estimation of GPP from space-borne solar-induced chlorophyll fluorescence

Quantifying global photosynthesis remains a challenge due to a lack of accurate remote sensing proxies. Solar-induced chlorophyll fluorescence (SIF) has been shown to be a good indicator of photosynthetic activity across various spatial scales. However, a global and spatially challenging estimate of terrestrial gross primary production (GPP) based on satellite SIF remains unresolved due to the confounding effects of species-specific physical and physiological traits and external factors, such as canopy structure or photosynthetic pathway (C-3 or C-4). Here we analyze an ensemble of far-red SIF data from OCO-2 satellite and ground observations at multiple sites, using the spectral invariant theory to reduce the effects of canopy structure and to retrieve a structure-corrected total canopy SIF emission (SIFtotal). We find that the relationships between observed canopy-leaving SIF and ecosystem GPP vary significantly among biomes. In contrast, the relationships between SIFtotal and GPP converge around two unique models, one for C-3 and one for C-4 plants. We show that the two single empirical models can be used to globally scale satellite SIF observations to terrestrial GPP. We obtain an independent estimate of global terrestrial GPP of 129.56 +/- 6.54 PgC/year for the 2015-2017 period, which is consistent with the state-of-the-art data- and process-oriented models. The new GPP product shows improved sensitivity to previously undetected 'hotspots' of productivity, being able to resolve the double-peak in GPP due to rotational cropping systems. We suggest that the direct scheme to estimate GPP presented here, which is based on satellite SIF, may open up new possibilities to resolve the dynamics of global terrestrial GPP across space and time.Peer reviewe

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

Upcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral data. This paper reviews the widely applied coupled PROSPECT and SAIL radiative transfer models (PROSAIL), regarding their suitability for the retrieval of biophysical and biochemical variables in the context of agricultural crop monitoring. Evaluation was carried out using a systematic literature review of 281 scientific publications with regard to their (i) spectral exploitation, (ii) vegetation type analyzed, (iii) variables retrieved, and (iv) choice of retrieval methods. From the analysis, current trends were derived, and problems identified and discussed. Our analysis clearly shows that the PROSAIL model is well suited for the analysis of imaging spectrometer data from future satellite missions and that the model should be integrated in appropriate software tools that are being developed in this context for agricultural applications. The review supports the decision of potential users to employ PROSAIL for their specific data analysis and provides guidelines for choosing between the diverse retrieval techniques

Development of an earth observation processing chain for crop biophysical parameters at local and global scale

This thesis’ topics embrace remote sensing for Earth observation, specifically in Earth vegetation monitoring. The Thesis’ main objective is to develop and implement an operational processing chain for crop biophysical parameters estimation at both local and global scales from remote sensing data. Conceptually, the components of the chain are the same at both scales: First, a radiative transfer model is run in forward mode to build a database composed by simulations of vegetation surface reflectance and concomitant biophysical parameters associated to those spectrum. Secondly, the simulated database is used for training and testing nonlinear and non-parametric machine learning regression algorithms. The best model in terms of accuracy, bias and goodness-of-fit is then selected to be used in the operational retrieval chain. Once the model is trained, remote sensing surface reflectance data is fed into the trained model as input in the inversion process to retrieve the biophysical parameters of interest at both local and global scales depending on the inputs spatial resolution and coverage. Eventually, the validation of the leaf area index estimates is performed at local scale by a set of ground measurements conducted during coordinated field campaigns in three countries during 2015 and 2016 European rice seasons. At global scale, the validation is performed through intercomparison with the most relevant and widely validated reference biophysical products. The work elaborated in this Thesis is structured in six chapters including an introduction of remote sensing for Earth observation, the developed processing chain at local scale, the ground LAI measurements acquired with smartphones, the developed chain at global scale, a chapter discussing the conclusions of the work, and a chapter which includes an extended abstract in Valencian. The Thesis is completed by an annex which include a compendium of peer-reviewed publications in remote sensing international journals

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation

Improving ecological forecasts using model and data constraints

Terrestrial ecosystems are essential to human well-being, but their future remains highly uncertain, as evidenced by the huge disparities in model projections of the land carbon sink. The existence of these disparities despite the recent explosion of novel data streams, including the TRY plant traits database, the Landsat archive, and global eddy covariance tower networks, suggests that these data streams are not being utilized to their full potential by the terrestrial ecosystem modeling community. Therefore, the overarching objective of my dissertation is to identify how these various data streams can be used to improve the precision of model predictions by constraining model parameters. In chapter 1, I use a hierarchical multivariate meta-analysis of the TRY database to assess the dependence of trait correlations on ecological scale and evaluate the utility of these correlations for constraining ecosystem model parameters. I find that global trait correlations are generally consistent within plant functional types, and leveraging the multivariate trait space is an effective way to constrain trait estimates for data-limited traits and plant functional types. My next two chapters assess the ability to measure traits using remote sensing by exploring the links between leaf traits and reflectance spectra. In chapter 2, I introduce a method for estimating traits from spectra via radiative transfer model inversion. I then use this approach to show that although the precise location, width, and quantity of spectral bands significantly affects trait retrieval accuracy, a wide range of sensor configurations are capable of providing trait information. In chapter 3, I apply this approach to a large database of leaf spectra to show that traits vary as much within as across species, and much more across species within a functional type than across functional types. Finally, in chapter 4, I synthesize the findings of the previous chapters to calibrate a vegetation model's representation of canopy radiative transfer against observed remotely-sensed surface reflectance. Although the calibration successfully constrained canopy structural parameters, I identify issues with model representations of wood and soil reflectance that inhibit its ability to accurately reproduce remote sensing observations

Multiangular crop differentiation and LAI estimation using PROSAIL model inversion

xiii, 161 leaves : ill., map ; 29 cmUnderstanding variations in remote sensing data with illumination and sensor angle changes is important in agricultural crop monitoring. This research investigated field bidirectional reflectance factor (BRF) in crop differentiation and PROSAIL leaf area index (LAI) estimation. BRF and LAI data were collected for planophile and erectophile crops at three growth stages. In the solar principal plane, BRF differed optimally at 860 nm 60 days after planting (DAP) for canola and pea, at 860 nm 45 and 60 DAP for wheat and barley, and at 860 nm and 670 nm 45 and 60 DAP for planophiles versus erectophiles. The field BRF data helped better understand PROSAIL LAI estimation. NDVI was preferred for estimating LAI, however the MTVI2 vegetation index showed high sensitivity to view angles, particularly for erectophiles. The hotspot was important for crop differentiation and LAI. Availability of more along-track, off-nadir looking spaceborne sensors was recommended for agricultural crop monitoring