Oregon transect: Comparison of leaf-level reflectance with canopy-level and modelled reflectance

The Oregon Transect Ecosystem Research (OTTER) project involves the collection of a variety of remotely-sensed and in situ measurements for characterization of forest biophysical and biochemical parameters. The project includes nine study plots located along an environmental gradient in west-central Oregon, extending from the Pacific coast inland approximately 300km. These plots represent a broad range in ecosystem structure and function. Within the OTTER project, the sensitivity of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) signal to absorption by foliar biochemicals is being examined. AVIRIS data were acquired over all plots in conjunction with the four OTTER Multi-sensor Aircraft Campaigns spanning the growing season. Foilage samples were gathered during each campaign for biochemical determination (at Ames Research Center), to estimate stand-level constituency at each plot. Directional-hemispheric leaf reflectance throughout the 400-2400nm region was measured in the laboratory as an aid to interpreting concurrent AVIRIS data. Obtaining leaf spectra in this manner reduces or eliminates the confounding influences of atmosphere, canopy architecture, and reflectance by woody components, understory, and exposed soils which are present in airborne observations. These laboratory spectra were compared to simulated spectra derived by inverting the PROSPECT leaf-level canopy reflectance derived from AVIRIS data by use of the LOWTRAN-7 atmospheric radiative-transfer model

Estimation of Wheat Plant Density at Early Stages Using High Resolution Imagery

Crop density is a key agronomical trait used to manage wheat crops and estimate yield. Visual counting of plants in the field is currently the most common method used. However, it is tedious and time consuming. The main objective of this work is to develop a machine vision based method to automate the density survey of wheat at early stages. RGB images taken with a high resolution RGB camera are classified to identify the green pixels corresponding to the plants. Crop rows are extracted and the connected components (objects) are identified. A neural network is then trained to estimate the number of plants in the objects using the object features. The method was evaluated over three experiments showing contrasted conditions with sowing densities ranging from 100 to 600 seeds.m(-2). Results demonstrate that the density is accurately estimated with an average relative error of 12%. The pipeline developed here provides an efficient and accurate estimate of wheat plant density at early stages

Hyperspectral remote sensing of foliar nitrogen content

http://www.pnas.orgPeer reviewe

Reply toTownsend et al.: Decoupling contributions from canopy structure and leaf optics is critical for remote sensing leaf biochemistry

Non peer reviewe

What is cost-efficient phenotyping? Optimizing costs for different scenarios

Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) major costs arise from plant handling and manpower; (ii) the total costs per plant/microplot are similar in robotized platform or field experiments with drones, hand-held or robotized ground vehicles; (iii) the cost of vehicles carrying sensors represents only 5–26% of the total costs. These conclusions depend on the context, in particular for labor cost, the quantitative demand of phenotyping and the number of days available for phenotypic measurements due to climatic constraints. Data analysis represents 10–20% of total cost if pipelines have already been developed. A trade-off exists between the initial high cost of pipeline development and labor cost of manual operations. Overall, depending on the context and objsectives, “cost-effective” phenotyping may involve either low investment (“affordable phenotyping”), or initial high investments in sensors, vehicles and pipelines that result in higher quality and lower operational costs

Decoupling Contributions from Canopy Structure and Leaf Optics is Critical for Remote Sensing Leaf Biochemistry (Reply to Townsend, et al.)

Townsend et al. (1) agree that we explained that the apparent relationship (2) between foliar nitrogen (%N) and near-infrared (NIR) canopy reflectance was largely attributable to structure (which is in turn caused by variation in fraction of broadleaf canopy). Our conclusion that the observed correlation with %N was spurious (i.e., lacking a causal basis) is, thus, clearly justified: we demonstrated that structure explained the great majority of observed correlation, where the structural influence was derived precisely via reconciling the observed correlation with radiative-transfer theory. What this also suggests is that such correlations, although observed, do not uniquely provide information on canopy biochemical constituents

Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure

In the context of precision viticulture, remote sensing in the optical domain offers a potential way to map crop structure characteristics, such as vegetation cover fraction, row orientation or leaf area index, that are later used in decision support tools. A method based on the RGB color model imagery acquired with an unmanned aerial vehicle (UAV) is proposed to describe the vineyard 3D macro-structure. The dense point cloud is first extracted from the overlapping RGB images acquired over the vineyard using the Structure from Motion algorithm implemented in the Agisoft PhotoScan software. Then, the terrain altitude extracted from the dense point cloud is used to get the 2D distribution of height of the vineyard. By applying a threshold on the height, the rows are separated from the row spacing. Row height, width and spacing are then estimated as well as the vineyard cover fraction and the percentage of missing segments along the rows. Results are compared with ground measurements with root mean square error (RMSE) = 9.8 cm for row height, RMSE = 8.7 cm for row width and RMSE = 7 cm for row spacing. The row width, cover fraction, as well as the percentage of missing row segments, appear to be sensitive to the quality of the dense point cloud. Optimal flight configuration and camera setting are therefore mandatory to access these characteristics with a good accuracy

Estimation of leaf water content and specific leaf weight from reflectance and transmittance measurements

Specific absorption coefficients for water and dry matter were estimated using a wide range of variation of fresh leaves. The coefficients were derived from the inversion of the PROSPECT leaf optical property model using reflectance and transmittance spectra measured over the 1 300-2 400-nm domain and the corresponding water content (g.cm-2) and specific leaf weight (mass of dry matter per unit leaf.area, g.cm-2). Results show that the estimated values of the specific absorption coefficient for dry matter were not reliable in the strong water absorption bands, although there was agreement with previous studies in spectral regions where water contributed moderately to leaf absorption. We thus proposed to use the values derived by Fourty et al (1996) for dry leaves for the specific absorption coefficient of dry matter. Estimated values of the specific absorption coefficient of water were slightly higher than the values proposed by Curcio and Petty (1951) for pure water. We then investigated the possibility of estimating leaf water content and specific weight by inverting the PROSPECT model using concurrently or separately reflectance and/or transmittance spectra measured over fresh leaves and the specific absorption coefficients proposed by Fourty et al (1996) for dry matter, and Curcio and Petty (1951) for water. Results obtained on the training data set and on an independent data set show accurate and robust estimates of both water content (RMSE = 0.0025 g.cm-2) and specific leaf weight (RMSE = 0.0016 g.cm-2). when reflectance and transmittance were used concurrently. When either reflectance or transmittance measurements were used, the performances of water and dry matter content estimation decreased because of the relaxation of constraints in the inversion process. Possible applications of these results are discussed.Les coefficients spécifiques de l’eau et de la matière sèche sont estimés en inversant le modèle Prospect de propriétés optiques des feuilles sur une collection variée de feuilles fraîches sur lesquelles les contenus en eau (g.cm-2), les masses sèches surfacique (masse de matière sèche par unité de surface de feuille, g.cm-2) et les spectres de réflectance et transmittance dans le domaine 1 300 - 2 400 nm ont été mesurés. Les résultats montrent que les valeurs estimées du coefficient d’absorption spécifique de la matière sèche ne sont pas fiables dans les bandes de forte absorption par l’eau, alors qu’en dehors de ces bandes, un bon accord est observé avec les résultats antérieurs. Nous proposons donc d’utiliser les valeurs de coefficient spécifique d’absorption de Fourty et al (1996) calculées sur des feuilles sèches. Les valeurs estimées du coefficient spécifique d’absorption de l’eau sont légèrement surestimées par rapport aux valeurs proposées par Curcio et Petty (1951). Nous avons ensuite évalué la possibilité d’estimer le contenu en eau et la masse sèche surfacique en inversant le modèle Prospect en utilisant des spectres de réflectance et/ou de transmittance mesurés sur des feuilles fraîches, avec les coefficient spécifiques proposés par Fourty et al pour la matière sèche ou par Curcio et Petty pour l’eau. Les résultats obtenus sur le jeu d’apprentissage et un jeu de données indépendant montrent une bonne estimation du contenu en eau (RMSE = 0,0016 g.cm-2) et de la masse surfacique sèche (RMSE = 0,0016 g.cm-2) quand la réflectance et la transmittance sont utilisées simultanément. En revanche, quand la réflectance ou la transmittance est utilisée seule, la précision des estimations diminue significativement du fait de la réduction des contraintes imposées au processus d’inversion. Les applications possibles de ces résultats sont discutées

A multisensor fusion approach to improve LAI time series

High-quality and gap-free satellite time series are required for reliable terrestrial monitoring. Moderate resolution sensors provide continuous observations at global scale for monitoring spatial and temporal variations of land surface characteristics. However, the full potential of remote sensing systems is often hampered by poor quality or missing data caused by clouds, aerosols, snow cover, algorithms and instrumentation problems. A multisensor fusion approach is here proposed to improve the spatio-temporal continuity, consistency and accuracy of current satellite products. It is based on the use of neural networks, gap filling and temporal smoothing techniques. It is applicable to any optical sensor and satellite product. In this study, the potential of this technique was demonstrated for leaf area index (LAI) product based on MODIS and VEGETATION reflectance data. The FUSION product showed an overall good agreement with the original MODIS LAI product but exhibited a reduction of 90% of the missing LAI values with an improved monitoring of vegetation dynamics, temporal smoothness, and better agreement with ground measurement

Assessment of Three Methods for Near Real-Time Estimation of Leaf Area Index From AVHRR Data

Near real-time (NRT) estimation of leaf area index (LAI) is essential for monitoring rapid surface process changes within operational systems. This paper assesses the performances of three methods for the NRT estimation of LAI: 1) Whittaker (Whit); 2) Gaussian process model (GPM); and 3) the climatological temporal smoothing and gap filling (CTSGF). The methods were evaluated using Advanced Very High Resolution Radiometer time series over a selection of BELMANIP2 sites representative of seasonal patterns of global biome vegetated areas and under varying level of noise and missing observations (gaps). A simulation experiment was designed to evaluate the predictive capabilities of the three methods with an emphasis on the global and local structure of missing observations in the time series. The results show that the three methods achieve similar performances (RMSE < 0.4) when the fraction of missing data over the whole time series is lower than 65% or the length of gaps is smaller than 10 days. Conversely, for fraction of gaps higher than 65% or periods of gaps longer than 10 days, CTSGF is found to provide more accurate (RMSE < 0.4 up to 60 days with missing data) NRT estimates of LAI than Whit and GPM. CTSGF uses the baseline seasonal cycle derived from the interannual median values of LAI to fill gaps in the time series and improve the NRT projections. Our findings support the operational use of the CTSGF algorithm for NRT estimation of biophysical products at the global scale