Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

Leaf area index (LAI) and leaf chlorophyll content (Chll) represent key biophysical and biochemical controls on water, energy and carbon exchange processes in the terrestrial biosphere. In combination, LAI and Chll provide critical information on vegetation density, vitality and photosynthetic potentials.However, simultaneous retrieval of LAI and Chll fromspace observations is extremely challenging. Regularization strategies are required to increase the robustness and accuracy of retrieved properties and enable more reliable separation of soil, leaf and canopy parameters. To address these challenges, the REGularized canopy reFLECtance model (REGFLEC) inversion system was refined to incorporate enhanced techniques for exploiting ancillary LAI and temporal information derived from multiple satellite scenes. In this current analysis, REGFLEC is applied to a time-series of Landsat data. A novel aspect of the REGFLEC approach is the fact that no site-specific data are required to calibrate the model, which may be run in a largely automated fashion using information extracted entirely from image-based and other widely available datasets. Validation results, based upon in-situ LAI and Chll observations collected over maize and soybean fields in centralNebraska for the period 2001–2005, demonstrate Chll retrievalwith a relative root-mean-square-deviation (RMSD) on the order of 19% (RMSD = 8.42 μg cm−2). While Chll retrievals were clearly influenced by the version of the leaf optical properties model used (PROSPECT), the application of spatio-temporal regularization constraints was shown to be critical for estimating Chll with sufficient accuracy. REGFLEC also reproduced the dynamics of in-situ measured LAI well (r2 = 0.85), but estimates were biased low, particularly over maize (LAI was underestimated by ~36 %). This disparity may be attributed to differences between effective and true LAI caused by significant foliage clumping not being properly accounted for in the canopy reflectance model (SAIL). Additional advances in the retrieval of canopy biophysical and leaf biochemical constituents will require innovative use of existing remote sensing data within physically realistic canopy reflectancemodels along with the ability to exploit the enhanced spectral and spatial capabilities of upcoming satellite systems

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)

Generating Global Leaf Area Index from Landsat: Algorithm Formulation and Demonstration

This paper summarizes the implementation of a physically based algorithm for the retrieval of vegetation green Leaf Area Index (LAI) from Landsat surface reflectance data. The algorithm is based on the canopy spectral invariants theory and provides a computationally efficient way of parameterizing the Bidirectional Reflectance Factor (BRF) as a function of spatial resolution and wavelength. LAI retrievals from the application of this algorithm to aggregated Landsat surface reflectances are consistent with those of MODIS for homogeneous sites represented by different herbaceous and forest cover types. Example results illustrating the physics and performance of the algorithm suggest three key factors that influence the LAI retrieval process: 1) the atmospheric correction procedures to estimate surface reflectances; 2) the proximity of Landsatobserved surface reflectance and corresponding reflectances as characterized by the model simulation; and 3) the quality of the input land cover type in accurately delineating pure vegetated components as opposed to mixed pixels. Accounting for these factors, a pilot implementation of the LAI retrieval algorithm was demonstrated for the state of California utilizing the Global Land Survey (GLS) 2005 Landsat data archive. In a separate exercise, the performance of the LAI algorithm over California was evaluated by using the short-wave infrared band in addition to the red and near-infrared bands. Results show that the algorithm, while ingesting the short-wave infrared band, has the ability to delineate open canopies with understory effects and may provide useful information compared to a more traditional two-band retrieval. Future research will involve implementation of this algorithm at continental scales and a validation exercise will be performed in evaluating the accuracy of the 30-m LAI products at several field sites

Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations

Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.} Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 {and Landsat 8} optical remote sensing data and machine learning methods in order to estimate crop GPP. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precisionlevel attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multisatellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications

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

Contribution of leaf specular reflection to canopy reflectance under black soil case using stochastic radiative transfer model

Numerous canopy radiative transfer models have been proposed based on the assumption of “ideal bi-Lambertian leaves” with the aim of simplifying the interactions between photons and vegetation canopies. This assumption may cause discrepancy between the simulated and measured canopy bidirectional reflectance factor (BRF). Few studies have been devoted to evaluate the impacts of such assumption on simulation of canopy BRF at a high-to-medium spatial resolution (∼30 m). This paper focuses on quantifying the contribution of leaf specular reflection on the estimation of canopy BRF under a black soil case using one of the most efficient radiative transfer models, the stochastic radiative transfer model. Analyses of field and satellite data collected over the boreal Hyytiälä forest in Finland show that leaf specular reflection may lead to errors of up to 33.1% at 550 nm and 32.8% at 650 nm in terms of relative root mean square error. The results suggest that, in order to minimize these errors, leaf specular reflection should be accounted for in modeling BRF.This research was supported by the Fundamental Research Funds for the Central Universities under Grant No. 531107051063 and Guangxi Natural Science Foundation under Grant No. 2016JJD110017. We would like to thank Dr. Rautiainen Miina and Mottus Matti for sharing the field data and the USGS for making the EO-1 Hyperion hyperspectral data publically available. (531107051063 - Fundamental Research Funds for the Central Universities; 2016JJD110017 - Guangxi Natural Science Foundation)Accepted manuscrip

Joint retrieval of growing season corn canopy LAI and leaf chlorophyll content by fusing Sentinel-2 and MODIS images

Continuous and accurate estimates of crop canopy leaf area index (LAI) and chlorophyll content are of great importance for crop growth monitoring. These estimates can be useful for precision agricultural management and agricultural planning. Our objectives were to investigate the joint retrieval of corn canopy LAI and chlorophyll content using filtered reflectances from Sentinel-2 and MODIS data acquired during the corn growing season, which, being generally hot and rainy, results in few cloud-free Sentinel-2 images. In addition, the retrieved time series of LAI and chlorophyll content results were used to monitor the corn growth behavior in the study area. Our results showed that: (1) the joint retrieval of LAI and chlorophyll content using the proposed joint probability distribution method improved the estimation accuracy of both corn canopy LAI and chlorophyll content. Corn canopy LAI and chlorophyll content were retrieved jointly and accurately using the PROSAIL model with fused Kalman filtered (KF) reflectance images. The relation between retrieved and field measured LAI and chlorophyll content of four corn-growing stages had a coefficient of determination (R2) of about 0.6, and root mean square errors (RMSEs) ranges of mainly 0.1-0.2 and 0.0-0.3, respectively. (2) Kalman filtering is a good way to produce continuous high-resolution reflectance images by synthesizing Sentinel-2 and MODIS reflectances. The correlation between fused KF and Sentinel-2 reflectances had an R2 value of 0.98 and RMSE of 0.0133, and the correlation between KF and field-measured reflectances had an R2 value of 0.8598 and RMSE of 0.0404. (3) The derived continuous KF reflectances captured the crop behavior well. Our analysis showed that the LAI increased from day of year (DOY) 181 (trefoil stage) to DOY 236 (filling stage), and then increased continuously until harvest, while the chlorophyll content first also increased from DOY 181 to DOY 236, and then remained stable until harvest. These results revealed that the jointly retrieved continuous LAI and chlorophyll content could be used to monitor corn growth conditions

Estimación de flujos de agua entre suelo, vegetación y atmósfera mediante teledetección = Water fluxes estimation between soil, vegetation and atmosphere using remote sensing

En la frontera entre la superficie terrestre y la atmósfera se producen numerosos procesos físicos relacionados con el ciclo hidrológico. Cuando se producen precipitaciones en forma de lluvia, y el agua alcanza la superficie terrestre, una parte llega al suelo y otra parte puede ser interceptada por la vegetación. La fracción que llega al suelo se infiltra en la zona no saturada donde se almacena, lo humedece, disuelve los elementos que son absorbidos posteriormente por la vegetación y modifica las propiedades físicas del suelo. Para que la vegetación pueda desarrollarse es necesario que la planta abra los estomas, absorba CO2 y realice la fotosíntesis. Durante este proceso se produce una pérdida de agua a través de la hoja, que si es lo suficientemente grande puede llegar a hacer que la planta marchite si no es capaz de reponerla del suelo. El agua del suelo es devuelta a la atmósfera posteriormente mediante la evaporación y la transpiración de las plantas. La primera parte del trabajo se ha centrado en la estimación de parámetros biofísicos y estructurales de la vegetación, concretamente los relacionados con el contenido de agua. Para ello se han empleado numerosos datos recogidos en campo a lo largo de dos años fenológicos completos y se relacionaron con las medidas espectrales a dos escalas diferentes, campo y sensor MODIS (500 m). El contenido de agua se calculó usando tres métricas diferentes calculadas a partir de la misma muestra, el Contenido de Humedad de la Vegetación (FMC), el Espesor Equivalente de Agua (EWT) y el Contenido de Agua del Dosel (CWC). Además se usaron dos estimaciones a partir de Modelos de Transferencia Radiativa (RTM) para la obtención del FMC y CWC que fueron comparados con las obtenidas a partir de los modelos empíricos creados a partir los índices espectrales. Otras variables relacionadas como el contenido de materia seca (Dm) y el índice de área foliar (LAI) fueron también evaluadas usando índices de vegetación. Entre los resultados destacables de este estudio se encuentran en primer lugar los relacionados con el protocolo de recogida de datos en campo. En este estudio se obtuvieron evidencias de que las diferencias temporales a la hora de recoger datos en campo son más importantes que las diferencias espaciales en este ecosistema. Además se demostró la necesidad de mostrar consistencia en el protocolo de muestreo: tamaño de la muestra, hora de recogida de las muestras, etc. y en la importancia de evitar, en lo posible la toma de decisiones, generalmente subjetivas, por parte de los operadores de campo. Otro resultado destacable ha sido demostrar la existencia de una alta variabilidad del Dm a lo largo del año. Esto indica que asumir, como sugieren algunos autores, un valor constante de Dm para la estimación del espesor equivalente de agua a partir del contenido de humedad de la vegetación no es una opción viable en este ecosistema. De los índices de vegetación que fueron comparados en el estudio, el que presentó menores correlaciones fue el Índice de Vegetación Resistente a la Atmósfera (VARI). Se observaron algunas diferencias en el comportamiento de los modelos empíricos obtenidos con MODIS y las producidas a partir de medidas espectrales de campo, obteniendo resultados algo mejores en el caso de MODIS. Este hecho posiblemente sea debido a que las adquisiciones de del sensor MODIS presentan diferentes ángulos de observación, lo que reduce la proporción de suelo captada por el sensor y por lo tanto capturando una mayor proporción del dosel. La comparación entre los modelos empíricos y las estimaciones a partir de RTM demostró que en este caso los modelos empíricos aún mejoran las estimaciones de los modelos físicos desarrollados en zonas similares para estimar el contenido de humedad de la vegetación. La segunda parte del trabajo se ha centrado en la estimación del contenido de humedad del suelo combinando datos ópticos y térmicos mediante el cálculo del Índice de Temperatura y Sequedad de la Vegetación (TVDI) cuya obtención se basa en la técnica del triángulo. Se han investigado diferentes factores que afectan a la definición del triángulo, y cómo estos afectan los valores del TVDI y a su relación final con el contenido de humedad del suelo. En este trabajo se introdujo una modificación al cálculo del TVDI en la que se sustituyó el Índice de Vegetación de Diferencia Normalizada (NDVI) por el Índice de Diferencia Infraroja Normalizada (NDII). Esta modificación se tradujo en una mejora en el comportamiento de los modelos empíricos para estimar el contenido de humedad del suelo. Finalmente en la tesis se investiga el comportamiento de la EF en la zona de estudio y su estimación a partir de teledetección. El principal motivo del empleo de la EF es que ha sido ampliamente utilizada para estimar la evapotranspiración diaria, asumiendo que la EF es constante a lo largo del día. A partir de las medidas recogidas por una torre de flujos se han evaluado las variaciones diarias y se han validado las estimaciones de EF calculadas a partir de imágenes Landsat. Se ha usado una nueva versión modificada de la técnica del triángulo en la que se ha introducido el índice de área foliar adaptado a la escala Landsat a partir del producto MODIS (de 1 km a 30 m) como sustituto del índice de vegetación. Además se muestra un innovador método basado en las estadísticas propias del triángulo para la selección de las fechas a incluir en el análisis estadístico. La validación de las estimaciones de EF se ha llevado a cabo de dos maneras diferentes: usando las contribuciones de todos los pixeles incluidos en la zona de influencia de la torre; y utilizando el valor del único pixel correspondiente a la localización de la torre, mostrando ambas aproximaciones escasas diferencias en cuanto a resultados. Además se han comparado las EF diarias y la correspondiente a la hora de la pasada de Landsat sobre la zona de estudio. En este caso se observaron mayores diferencias, lo cual indica que el supuesto de una EF constante a lo largo del día ha de ser tomada con ciertas precauciones si el objetivo final es el cálculo de la evapotranspiración diaria