Search results for "Leaf area index"
showing 10 items of 105 documents
Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes
2019
The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among…
Investigating the Relationship between X-Band SAR Data from COSMO-SkyMed Satellite and NDVI for LAI Detection
2013
Monitoring spatial and temporal variability of vegetation is important to manage land and water resources, with significant impact on the sustainability of modern agriculture. Cloud cover noticeably reduces the temporal resolution of retrievals based on optical data. COSMO-SkyMed (the new Italian Synthetic Aperture RADAR-SAR) opened new opportunities to develop agro-hydrological applications. Indeed, it represents a valuable source of data for operational use, due to the high spatial and temporal resolutions. Although X-band is not the most suitable to model agricultural and hydrological processes, an assessment of vegetation development can be achieved combing optical vegetation indices (V…
Time series of Cosmo-SkyMed data for landcover classification and surface parameter retrieval over agricultural sites
2012
This paper reports on the results of an Italian project aimed at investigating the use of X-band COSMO-SkyMed (CSK) SAR data for applications in agriculture and hydrology. Existing classification and retrieval algorithms have been tailored to CSK data and time series of crop, leaf area index and soil moisture maps have been retrieved and assessed through the comparison with in situ data collected over three agricultural sites. In addition, the CSK-derived surface parameters have been integrated into crop growth and hydrologic models and the resulting improvements have been assessed. Results indicate that multi-temporal dual-polarized CSK data are very well-suited for agricultural crop class…
Power sensitivity analysis of multi-frequency, multi-polarized, multi-temporal SAR data for soil-vegetation system variables characterization
2017
Abstract: The knowledge of spatial and temporal variability of soil water content and others soil-vegetation variables (leaf area index, fractional cover) assumes high importance in crop management. Where and when the cloudiness limits the use of optical and thermal remote sensing techniques, synthetic aperture radar (SAR) imagery has proven to have several advantages (cloud penetration, day/night acquisitions and high spatial resolution). However, measured backscattering is controlled by several factors including SAR configuration (acquisition geometry, frequency and polarization), and target dielectric and geometric properties. Thus, uncertainties arise about the more suitable configurati…
A tractor mounted scanning LIDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with…
2009
The use of a low-cost tractor-mounted scanning Light Detection and Ranging (LIDAR) system for capable of making non-destructive recordings of tree-row structure in orchards and vineyards is described. Field tests consisted of several LIDAR measurements on both sides of the crop row, before and after defoliation of selected trees. Summary parameters describing the tree-row volume and the total crop surface area viewed by the LIDAR (expressed as a ratio with ground surface area) were derived using a suitable numerical algorithm. The results for apple and pear orchards and a wine producing vineyard were shown to be in reasonable agreement with the results derived from a destructive leaf sampli…
A combined optical-microwave method to retrieve soil moisture over vegetated areas
2011
A simple approach for correcting for the effect of vegetation in the estimation of the surface soil moisture (wS) from L-band passive microwave observations is presented in this study. The approach is based on semi-empirical relationships between soil moisture and the polarized reflectivity including the effect of the vegetation optical depth which is parameterized as a function of the normalized vegetation difference index (NDVI). The method was tested against in situ measurements collected over a grass site from 2004 to 2007 (SMOSREX experiment). Two polarizations (horizontal/vertical) and five incidence angles (20◦, 30◦, 40◦, 50◦, and 60◦) were considered in the analysis. The best wS est…
Intercomparison of instruments for measuring leaf area index over rice
2015
Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. LAI estimates can be classified as direct or indirect methods. Direct methods are destructive, time consuming, and difficult to apply over large fields. Indirect methods are non-destructive and cost-effective due to its portability, accuracy and repeatability. In this study, we compare indirect LAI estimates acquired from two classical instruments such as LAI-2000 and digital cameras for hemispherical photography, with LAI estimates acquired with a smart app (PocketLAI) installed on a mobile smartphone. In this work it is shown that LAI…
Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products
2008
International audience; This paper evaluates the performances of a neural network approach to estimate LAI from CYCLOPES and MODIS nadir normalized reflectance and LAI products. A data base was generated from these products over the BELMANIP sites during the 2001-2003 period. Data were aggregated at 3 km x 3 km, resampled at 1/16 days temporal frequency and filtered to reject outliers. VEGETATION and MODIS reflectances show very consistent values in the red, near infrared and short wave infrared bands. Neural networks were trained over part of this data base for each of the 6 MODIS biome classes to retrieve both MODIS and CYCLOPES LAI products. Results show very good performances of neural …
Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: application to PROBA/CHRIS data
2008
Hyperspectral/multiangular data allow the retrieval of important vegetation properties at canopy level, such as the Leaf Area Index (LAI) and Leaf Chlorophyll Content. Current methods are based on the relationship between biophysical properties and retrievals from those spectral bands (from the complete hyperspectral/multiangular information) where specific absorption features are present within the considered spectral range. Furthermore, new sensors such as PROBA/CHRIS provide continuous hyperspectral reflectance measurements that can be considered as a continuous function of wavelength. The mathematical analysis of these continuous functions allows a new way of exploiting the relationship…
Geostatistics for Mapping Leaf Area Index over a Cropland Landscape: Efficiency Sampling Assessment
2010
This paper evaluates the performance of spatial methods to estimate leaf area index (LAI) fields from ground-based measurements at high-spatial resolution over a cropland landscape. Three geostatistical model variants of the kriging technique, the ordinary kriging (OK), the collocated cokriging (CKC) and kriging with an external drift (KED) are used. The study focused on the influence of the spatial sampling protocol, auxiliary information, and spatial resolution in the estimates. The main advantage of these models lies in the possibility of considering the spatial dependence of the data and, in the case of the KED and CKC, the auxiliary information for each location used for prediction pur…