Authors: Franch, Belen; Vermote, Eric F.; Roger, Jean-Claude; Murphy, Emilie; Becker-Reshef, Inbal; Justice, Chris; Claverie, Martin; Nagol, Jyoteshwar; Csiszar, Ivan; Meyer, Dave; Baret, Frederic; Masuoka, Edward; Wolfe, Robert; Devadiga, Sadashiva
The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS.
Authors: Madec, Simon; Baret, Fred; de Solan, Benoit; Thomas, Samuel; Dutartre, Dan; Jezequel, Stephane; Hemmerle, Matthieu; Colombeau, Gallian; Comar, Alexis
The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phnomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the plant height can be estimated. Plant height first defined as the z-value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of plant height (RMSE = 3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion plant height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values (H-2 > 0.90) were found for both techniques when lodging was not present. The dynamics of plant height shows that it carries pertinent information regarding the period and magnitude of the plant stress. Further, the date when the maximum plant height is reached was found to be very heritable (H-2 > 0.88) and a good proxy of the flowering stage. Finally, the capacity of plant height as a proxy for total above ground biomass and yield is discussed.
Authors: Li, Wenjuan; Baret, Frederic; Weiss, Marie; Buis, Samuel; Lacaze, Roselyne; Demarez, Valerie; Dejoux, Jean-francois; Battude, Marjorie; Camacho, Fernando
A wide range of ecological, agricultural, hydrological and meteorological applications at local to regional scales requires decametric biophysical data. However, before the launch of SENTINEL-2A, only few decametric products are produced and most of them remain limited by the small number of available observations, mostly due to a moderate revisit frequency combined with cloud occurrence. Conversely, kilometric and hectometric biophysical products are now widely available with almost complete and continuous coverage, but the associated spatial resolution limits the application over heterogeneous landscapes. The objective of this study is to combine unfrequent decametric spatial resolution products with frequent hectometric spatial resolution products to improve the temporal frequency and completeness of decametric observations. The study focuses on the fraction of photosynthetically active radiation absorbed by the green vegetation (FAPAR) because of its important role in canopy models and small dependency to scaling issues.
An algorithm is developed to provide near real time estimates of FAPAR called DHF (for Decametric Hectometric Fusion) at a decametric resolution and dekadal time step. It is assumed that the FAPAR time course is described by a second-degree polynomial function over a limited 60-days temporal window for each decametric pixel. To reduce the dimensionality of the problem, landcover classes are considered instead of each individual pixel. For each class, the coefficients of the polynomial function are adjusted using the temporal course of the available decametric FAPAR products, under the constraint of providing a good match with the time course of the hectometric dekadal FAPAR products. The point spread function associated to the hectometric FAPAR products and the possible biases between the decametric and hectometric FAPAR products are explicitly accounted for.
The algorithm was evaluated over a time series of decametric Landsat-8 FAPAR images (30 m) and hectometric (330 m) dekadal GEOV3 FAPAR derived from PROBA-V images acquired in 2014 over a site in the South-West of France.
Results show that the estimated DHF FAPAR products capture well the expected seasonal variation and spatial distribution while improving the temporal frequency and spatial and temporal completeness of the original Landsat-8 products. A leave one out exercise shows that the DHF values are in very good agreement with the Landsat-8 FAPAR (RMSE = 0.05-0.14) that were not used when computing the DHF. This demonstrates the robustness of the algorithm and interest under cloudy regions. Additional comparison with ground measurements collected over 14 sunflower fields along the growth season confirms the good performances of the DHF FAPAR products (RMSE = 0.11).
Authors: Yan Guangjian; Hu Ronghai; Luo Jinghui; Marie, Weiss; Jiang Hailan; Mu Xihan; Xie Donghui; Zhang Wuming
Accurate estimation of leaf chlorophyll content (Cab) from remote sensing is of tremendous significance to monitor the physiological status of vegetation or to estimate primary production. Many vegetation indices (Vls) have been developed to retrieve Cab at the canopy level from meter- to decameter-scale reflectance observations. However, most of these Vls may be affected by the possible confounding influence of canopy structure. The objective of this study is to develop methods for Cab estimation using millimeter to centimeter spatial resolution reflectance imagery acquired at the field level.
Hyperspectral images were acquired over sugar beet canopies from a ground-based platform in the 400-1000 nm range, concurrently to Cab, green fraction (GF), green area index (GAI) ground measurements. The original image spatial resolution was successively degraded from 1 mm to 35 cm, resulting in eleven sets of hyperspectral images. Vegetation and soil pixels were discriminated, and for each spatial resolution, measured Cab values were related to various Vls computed over four sets of reflectance spectra extracted from the images (soil and vegetation pixels, only vegetation pixels, 50% darkest and brightest vegetation pixels). The selected Vls included some classical Vls from the literature as well as optimal combinations of spectral bands, including simple ratio (SR), modified normalized difference (mND) and structure insensitive pigment index (SIPI). In the case of mND and SIPI, the use of a blue reference band instead of the classical near-infrared one was also investigated. For the eleven spatial resolutions, the four pixel selections and the five VI formats, similar band combinations are obtained when optimizing VI performances: the main bands of interest are generally located in the blue, red, red edge and near-infrared domains. Overall, mND(blue)[728,850] defined as (R-440-R-728)/(R-440+R-850) and computed over the brightest green pixels obtains the best correlations with C-ab for spatial resolutions finer than 8.8 cm with a root mean square error of prediction better than 2.6 mu g/cm(2). Conversely, mND(blue)[728,8501 poorly correlates with variations in GF and GAI, thus reducing the risk of deriving non-causal relationships with Cab that would actually be due to the covariance between C-ab and these canopy structure variables. As mND(blue)[728,8501 can be calculated from most current multispectral sensors, it is therefore a promising VI to retrieve C-ab from millimeter- to centimeter-scale reflectance imagery. (C) 2017 Elsevier Inc. All rights reserved.
Plant density is useful variable that determines the fate of the wheat crop. The most commonly used method for plant density quantification is based on visual counting from ground level. The objective of this study is to develop and evaluate a method for estimating wheat plant density at the emergence stage based on high resolution imagery taken from UAV at very low altitude with application to high throughput phenotyping in field conditions. A Sony ILCE alpha 5100L RGB camera with 24 Mpixels and equipped with a 60 mm focal length lens was flying aboard an hexacopter at 3 to 7 m altitude at about 1 m/s speed. This allows getting ground resolution between 0.20 mm to 0.45 mm, while providing 59-77% overlap between images. The camera was looking with 45 degrees zenith angle in a compass direction perpendicular to the row direction to maximize the cross section viewed of the plants and minimize the effect of the wind created by the rotors. Agisoft photoscan software was then used to derive the position of the cameras for each image. Images were then projected on the ground surface to finally extract subsamples used to estimate the plant density. The extracted images were first classified to separate the green pixels from the background and the rows were then identified and extracted. Finally, image object (group of connected green pixels) was identified on each row and the number of plants they contain was estimated using a Support Vector Machine whose training was optimized using a Particle Swarm Optimization.
Three experiments were conducted in Greoux, Avignon and Clermont sites with some variability in the sowing dates, densities, genotypes, flight altitude, and growth stage at the time of the image acquisition. The application of the method on the 270 samples available over the three sites provides a RMSE and relative RMSE on estimates of 34.05 plants/m(2) and 14.31% with a bias of 9.01 plants/m(2). However, differences in performances were observed between the three sites, mostly related to the growth stage at the time of the flight. Plants should have between one to two leaves when images are taken. Further, a specific sensitivity analysis shows that the ground resolution of the images should be better than 0.40 mm. Finally, the repeatability of the method is good especially when images are taken from similar observational geometries. The current limits and possible improvements of the method proposed are finally discussed. (C) 2017 Elsevier Inc. All rights reserved.
Authors: Weiss, Marie; Baret, Frederic
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.
Authors: Steven, Michael D.; Malthus, Timothy J.; Baret, Frederic
No abstract.