Retrieving Leaf Area Index With a Neural Network Method: Simulation and Validation

Leaf area index () is a crucial biophysical parameter that is indispensable for many biophysical and climatic models. A neural network algorithm in conjunction with extensive canopy and atmospheric radiative transfer simulations is presented in this paper to estimateLAIfromLandsat-7 Enhanced ThematicMapper Plus data. Two schemes were explored; the first was based on surface reflectance, and the second on top-of-atmosphere (TOA) radiance. The implication of the second scheme is that atmospheric corrections are not needed for estimating the surface LAI. A soil reflectance index (SRI) was proposed to account for variable soil background reflectances. Ground-measured LAI data acquired at Beltsville, MD were used to validate both schemes. The results indicate that both methods can be used to estimate LAI accurately. The experiments also showed that the use of SRI is very critical.This work was supported in part by the U.S. National Aeronautics and Space Administration (NASA) under Grant NAG5-6459 and Grant NCC5462

Sun-angle effects on remote-sensing phenology observed and modelled using himawari-8

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time

Vliv atmosférické a topografické korekce na přesnost odhadu množství chlorofylu ve smrkových lesních porostech

Odstraňování efektů zemské atmosféry (tzv. atmosférická korekce) je jednou z klíčových součástí předzpracování obrazových dat dálkového průzkumu Země používaných pro kvantitativní nebo semi-kvantitativní analýzu. Přestože v současné době existuje velké množství robustních výpočetních technik kvantitativního odhadu různých parametrů zemského povrchu, vliv atmosférické korekce na výsledky těchto odhadů zpravidla není brán dostatečně v úvahu. Hlavním cílem této práce je zhodnocení vlivu použití různých technik atmosférické korekce na přesnost kvantitativního odhadu množství chlorofylu v lesních porostech smrku ztepilého (Picea abies). Obsah chlorofylu byl určován na podkladě výpočtu vybraných vegetačních indexů, které jsou na obsah chlorofylu citlivé (ANCB650-720, MSR, N718, TCARI/OSAVI a D718/D704). Hodnoty těchto indexů byly simulovány pomocí kombinace modelů radiativního transferu PROSPECT a DART. Výsledné odhady obsahu chlorofylu byly na závěr validovány pomocí výsledků laboratorního stanovení obsahu chlorofylu v odebraných vzorcích smrkových jehlic. Kromě toho byl v rámci práce odvozen nový index pro hodnocení podobnosti dvou srovnávaných spekter nazvaný normalized Area Under Difference Curve (nAUDC). V rámci této práce byla testována potenciální možnost náhrady standardní atmosférické korekce...Removal of atmospheric effects (atmospheric correction) is an essential step in a pre-processing chain of all remotely sensed image data used for any quantitative or semi-quantitative analysis. Although there are many robust computing techniques allowing quantitative estimation of various parameters of the Earth's surface, the influence of atmospheric correction on the accuracy of such estimation is usually not taken into account at all. The main focus of this thesis is to assess the influence of the use of different atmospheric correction techniques on the Norway spruce (Picea abies) canopy chlorophyll content estimation accuracy. Canopy chlorophyll content was estimated using values of chlorophyll sensitive vegetation indices (ANCB650-720, MSR, N718, TCARI/OSAVI and D718/D704) simulated by a coupling of PROSPECT and DART radiative transfer models and validated by a ground-truth dataset. A new spectral similarity index called normalized Area Under Difference Curve (nAUDC) was developed to allow mutual comparison of two spectra originating from hyperspectral datasets corrected by different atmospheric correction methods. Potential substitutability of the standard physically-based ATCOR-4 atmospheric correction by the empirical correction based on the data acquired by the downwelling irradiance...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling

Outbreaks of Xylella fastidiosa (Xf) in Europe generate considerable economic and environmental damage, and this plant pest continues to spread. Detecting and monitoring the spatio-temporal dynamics of the disease symptoms caused by Xf at a large scale is key to curtailing its expansion and mitigating its impacts. Here, we combined 3-D radiative transfer modelling (3D-RTM), which accounts for the seasonal background variations, with passive optical satellite data to assess the spatio-temporal dynamics of Xf infections in olive orchards. We developed a 3D-RTM approach to predict Xf infection incidence in olive orchards, integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with radiative transfer modelling and field observations. Sentinel-2A time series data collected over a two-year period were used to assess the temporal trends in Xf-infected olive orchards in the Apulia region of southern Italy. Hyperspectral images spanning the same two-year period were used for validation, along with field surveys; their high resolution also enabled the extraction of soil spectrum variations required by the 3D-RTM to account for canopy background effect. Temporal changes were validated with more than 3000 trees from 16 orchards covering a range of disease severity (DS) and disease incidence (DI) levels. Among the wide range of structural and physiological vegetation indices evaluated from Sentinel-2 imagery, the temporal variation of the Atmospherically Resistant Vegetation Index (ARVI) and Optimized Soil-Adjusted Vegetation Index (OSAVI) showed superior performance for DS and DI estimation (r2VALUES>0.7, p < 0.001). When seasonal understory changes were accounted for using modelling methods, the error of DI prediction was reduced 3-fold. Thus, we conclude that the retrieval of DI through model inversion and Sentinel-2 imagery can form the basis for operational vegetation damage monitoring worldwide. Our study highlight the value of interpreting temporal variations in model retrievals to detect anomalies in vegetation health.Data collection was partially supported by the European Union's Horizon 2020 research and innovation programme through grant agreements POnTE (635646) and XF-ACTORS (727987). A. Hornero was supported by research fellowship DTC GEO 29 “Detection of global photosynthesis and forest health from space” from the Science Doctoral Training Centre (Swansea University, UK). The authors would also like to thank QuantaLab-IAS-CSIC (Spain) for laboratory assistance and the support provided during the airborne campaigns and image processing. B. Landa, C. Camino, M. Montes-Borrego, M. Morelli, M. Saponari and L. Susca are acknowledged for their support during the field campaigns, as well as IPSP-CNR and Dipartimento di Scienze del Suolo (Università di Bari, Italy) as host institutions