Use spectral derivatives for estimating canopy water content

Hyperspectral remote sensing has demonstrated great potential for accurate retrieval of canopy water content (CWC). This CWC is defined by the product of the leaf equivalent water thickness (EWT) and the leaf area index (LAI). In this paper the spectral information provided by the canopy water absorption feature at 970 nm for estimating and predicting CWC was studied using a modelling approach and in situ spectroradiometric measurements. The relationship of the first derivative at the right slope of the 970 nm water absorption feature with CWC was investigated with the PROSAIL radiative transfer model at a 1 nm sampling interval and tested for field spectroradiometer measurements obtained at an extensively grazed fen meadow as test site. PROSAIL simulations (using coupled SAIL/PROSPECT-5 models) showed a linear relationship between the first derivative over the 1015 – 1050 nm spectral interval and CWC (R2 = 0.97), which was not sensitive for leaf and canopy structure, soil brightness and illumination and observation geometry. For 40 plots at the fen meadow ASD FieldSpec spectral measurements yielded an R2 of 0.68 for the derivative over the 1015 – 1050 nm interval with CWC. This relationship appeared to match the simulated relationship obtained from the PROSAIL model. It showed that one may transfer simulated results to real measurements obtained in the field, thus giving them a physical basis and more general applicability. Consistency of the results confirmed the potential of using simulation results for calibrating the relationship between this first derivative and CWC. Another advantage of using the derivative at the right slope of the 970 nm absorption feature is its distance from the atmospheric water vapour absorption feature at 940 nm. If one cannot correct well for the effects of atmospheric water vapour, the derivative at the right slope is preferred over the one at the left slope

Using hyperspectral remote sensing data for retrieving canopy water content

Canopy water content (CWC) is important for understanding functioning of terrestrial ecosystems. Spectral derivatives at the slopes of the 970 nm and 1200 nm water absorption features offer good potential as estimators for CWC. An extensively grazed fen meadow is used as test site in this study. Results are compared with simulations with the PROSAIL radiative transfer model. The first derivative at the left slope of the feature at 970 nm is found to be highly correlated with CWC and the relationship corresponds to the one found with PROSAIL simulations. Use of the derivative over the 940 – 950 nm interval is suggested. In order to avoid interference with absorption by atmospheric water vapour, the potential of estimating CWC using the first derivative at the right slope of the 970 nm absorption feature is recommended. Correlations are a bit lower than those at the left slope, but better than those obtained with water band indices, as shown in previous studies. FieldSpec measurements show that one may use derivatives around the middle of the right slope within the interval between 1015 nm and 1050 nm

Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping

The continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUTN₅₀) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven- and eight-band combinations, the LUTN₅₀ approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUTN50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications

Effect of environmental conditions on the relationship between solar induced fluorescence and gross primary productivity at an OzFlux grassland site

Recent studies have utilized coarse spatial and temporal resolution remotely sensed solar induced fluorescence (SIF) for modeling terrestrial gross primary productivity (GPP) at regional scales. Although these studies have demonstrated the potential of SIF, there have been concerns about the ecophysiological basis of the relationship between SIF and GPP in different environmental conditions. Launched in 2014, the Orbiting Carbon Observatory-2 (OCO-2) has enabled fine scale (1.3-by-2.5 km) retrievals of SIF that are comparable with measurements recorded at eddy covariance towers. In this study, we examine the effect of environmental conditions on the relationship of OCO-2 SIF with tower GPP over the course of a growing season at a well-characterized natural grassland site. Combining OCO-2 SIF and eddy covariance tower data with a canopy radiative transfer and an ecosystem model, we also assess the potential of OCO-2 SIF to constrain the estimates of V_(cmax), one of the most important parameters in ecosystem models. Based on the results, we suggest that although environmental conditions play a role in determining the nature of relationship between SIF and GPP, overall the linear relationship is more robust at ecosystem scale than the theory based on leaf-level processes might suggest. Our study also shows that the ability of SIF to constrain V_(cmax) is weak at the selected site

Estimation of maize canopy properties from remote sensing by inversion of 1-D and 4-D models

The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. However the accuracy of the estimates depends on a range of factors, most notably the realism with which the canopy is represented by the models and the possibility of introducing a priori knowledge on canopy characteristics to constrain the inversion procedure. The objective of the present work was to compare the performances and operational limitations of two contrasting types of radiative transfer models: a classical one-dimensional canopy reflectance model, PROSPECT?SAIL (PROSAIL), and a three-dimensional dynamic (4-D) maize model. The latter introduces greater realism into the description of the canopy structure and implicit a priori information on the crop. The assessment was carried out with multiple view angle data recorded from field experiments on maize at stages V5 to V8. The simplex numerical optimization algorithm was used to invert the two models, using spectral reflectance data for PROSAIL and gap fraction data for the 4-D maize model. Leaf area index (LAI) was estimated with a RMSE of 0.48 for PROSAIL and 0.35 for the 4-D model. Retrieval of average leaf inclination angle (ALA) was problematic with both models. The effect of the number and distribution of observation view angles was examined, and the results highlight the advantage of oblique angle measurements.L'articolo é disponibile sul sito dell'editore: http://www.springerlink.co

High resolution fire hazard index based on satellite images

In December 2015, after 3 year of activity, the FP7 project PREFER (Space-based Information Support for Prevention and REcovery of Forest Fires Emergency in the MediteRranean Area) came to an end. The project was designed to respond to the need to improve the use of satellite images in applications related to the emergency services, in particular, to forest fires. The project aimed at developing, validating and demonstrating information products based on optical and SAR (Synthetic Aperture Radar) imagery for supporting the prevention of forest fires and the recovery/damage assessment of burnt area. The present paper presents an improved version of one of the products developed under the PREFER project, which is the Daily Fire Hazard Index (DFHI)

Retrieval of Vegetation Biochemicals Using a Radiative Transfer Model and Hyperspectral data

Accurate quantitative estimation of vegetation biochemical characteristics is necessary for a large variety of agricultural and ecological applications. The advent of hyperspectral remote sensing has offered possibilities for measuring specific vegetation variables that were difficult to measure using conventional multi-spectral sensors. In this study, the potential of biophysical modelling to predict leaf and canopy chlorophyll contents in a heterogeneous grassland is investigated. The well-known PROSAIL model was inverted with HyMap measurements by means of a look-up table (LUT). HyMap images along with simultaneous in situ measurements of chlorophyll content were acquired over a National Park. We tested the impact of using multiple solutions and spectral sub-setting on parameter retrieval. To assess the performance of the model inversion, the RMSE and R2 between independent in situ measurements and estimated parameters were used. The results of the study demonstrated that inversion of the PROSAIL model yield higher accuracies for Canopy chlorophyll content, in comparison to Leaf chlorophyll content (R2=0.84, RMSE=0.24). Further a careful selection of spectral subset, which comprised the development of a new method to subset the spectral data, proved to contain sufficient information for a successful model inversion. Consequently, it increased the estimation accuracy of investigated parameters (R2=0.87, RMSE=0.22). Our results confirm the potential of model inversion for estimating vegetation biochemical parameters using hyperspectral measurements.JRC.DG.G.3 - Monitoring agricultural resource

Using the right slope of the 970 nm absorption feature for estimating canopy water content

Canopy water content (CWC) is important for understanding the functioning of terrestrial ecosystems. Biogeochemical processes like photosynthesis, transpiration and net primary production are related to foliar water. The first derivative of the reflectance spectrum at wavelengths corresponding to the left slope of the minor water absorption band at 970 nm was found to be highly correlated with CWC and PROSAIL model simulations showed that it was insensitive to differences in leaf and canopy structure, soil background and illumination and observation geometry. However, these wavelengths are also located close to the water vapour absorption band at about 940 nm. In order to avoid interference with absorption by atmospheric water vapour, the potential of estimating CWC using the first derivative at the right slope of the 970 nm absorption feature was studied. Measurements obtained with an ASD FieldSpec spectrometer for three test sites were related to CWC (calculated as the difference between fresh and dry weight). The first site was a homogeneous grassland parcel with a grass/clover mixture. The second site was a heterogeneous floodplain with natural vegetation like grasses and various shrubs. The third site was an extensively grazed fen meadow. Results for all three test sites showed that the first derivative of the reflectance spectrum at the right slope of the 970 nm absorption feature was linearly correlated with CWC. Correlations were a bit lower than those at the left slope (at 942.5 nm) as shown in previous studies, but better than those obtained with water band indices. FieldSpec measurements showed that one may use any derivative around the middle of the right slope within the interval between 1015 nm and 1050 nm. We calculated the average derivative at this interval. The first site with grassland yielded an R2 of 0.39 for the derivative at the previously mentioned interval with CWC (based on 20 samples). The second site at the heterogeneous floodplain yielded an R2 of 0.45 for this derivative with CWC (based on 14 samples). Finally, the third site with the fen meadow yielded an R2 of 0.68 for this derivative with CWC (based on 40 samples). Regression lines between the derivative at the right slope of the 970 nm absorption feature and CWC for all three test sites were similar although vegetation types were quite different. This indicates that results may be transferable to other vegetation types and other site

Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model

A large proportion of the global land surface is covered by pasture. The advent of the Sentinel satellites program provides free datasets with good spatiotemporal resolution that can be a valuable source of information for monitoring pasture resources. We combined optical remote sensing data (proximal hyperspectral and Sentinel 2A) with a radiative transfer model (PROSAIL) to estimate leaf area index (LAI), and biomass, in a dairy farming context. Three sites in Southern England were used: two pasture farms that differed in pasture type and management, and a set of small agronomy trial plots with different mixtures of grasses, legumes and herbs, as well as pure perennial ryegrass. The proximal and satellite spectral data were used to retrieve LAI via PROSAIL model inversion, which were compared against field observations of LAI. The potential of bands of Sentinel 2A that corresponded with a 10 m resolution was studied by convolving narrow spectral bands (from a handheld hyperspectral sensor) into Sentinel 2A bands (10 m). Retrieved LAI, using these spectrally resampled S2A data, compared well with measured LAI, for all sites, even for those with mixed species cover (although retrieved LAI was somewhat overestimated for pasture mixtures with high LAI). This proved the suitability of 10 m Sentinel 2A spectral bands for capturing LAI dynamics for different types of pastures. We also found that inclusion of 20 m bands in the inversion scheme did not lead to any further improvement in retrieved LAI. Sentinel 2A image based retrieval yielded good agreement with LAI measurements obtained for a typical perennial ryegrass based pasture farm. LAI retrieved in this way was used to create biomass maps (that correspond to indirect biomass measurements by Rising Plate Meter (RPM)), for mixed-species paddocks for a farm for which limited field data were available. These maps compared moderately well with farmer-collected RPM measurements for this farm. We propose that estimates of paddock-averaged and within-paddock variability of biomass are more reliably obtained from a combined Sentinel 2A-PROSAIL approach, rather than by manual RPM measurements. The physically based radiative transfer model inversion approach outperformed the Normalised Difference Vegetation Index based retrieval method, and does not require site specific calibrations of the inversion scheme