Search results for " Process"
showing 10 items of 17204 documents
Comparison between SMOS Vegetation Optical Depth products and MODIS vegetation indices over crop zones of the USA
2014
The Soil Moisture and Ocean Salinity (SMOS) mission provides multi-angular, dual-polarised brightness temperatures at 1.4 GHz, from which global soil moisture and vegetation optical depth (tau) products are retrieved. This paper presents a study of SMOS' tau product in 2010 and 2011 for crop zones of the USA. Retrieved tau values for 504 crop nodes were compared to optical/IR vegetation indices from the MODES (Moderate Resolution Imaging Spectroradiometer) satellite sensor, including the Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVE), Leaf Area Index (LAI), and a Normalised Difference Water Index (NOW!) product. tau values were observed to increase during the…
Towards Quantifying Non-Photosynthetic Vegetation for Agriculture Using Spaceborne Imaging Spectroscopy
2021
Non-photosynthetic vegetation (NPV) has been identified as priority variable in the context of new spaceborne imaging spectroscopy missions. In this study we provide a first attempt to quantify NPV biomass from these unprecedented data streams to be provided by multiple recently launched or planned instruments. A hybrid workflow is proposed including Gaussian process regression (GPR) trained over radiative transfer model (RTM) simulations and applying active learning strategies. A soybean field data set including two dates with NPV measurements on yellow and senescent (brown) plant organs was used for model validation, resulting in relative errors of 13.4%. This prototype retrieval model wa…
Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring
2016
Abstract This paper presents an operational chain for high-resolution leaf area index (LAI) retrieval from multiresolution satellite data specifically developed for Mediterranean rice areas. The proposed methodology is based on the inversion of the PROSAIL radiative transfer model through the state-of-the-art nonlinear Gaussian process regression (GPR) method. Landsat and SPOT5 data were used for multitemporal LAI retrievals at high-resolution. LAI estimates were validated using time series of in situ LAI measurements collected during the rice season in Spain and Italy. Ground LAI data were collected with smartphones using PocketLAI, a specific phone application for LAI estimation. Temporal…
Crop Phenology Retrieval Through Gaussian Process Regression
2021
Monitoring crop phenology significantly assists agricultural managing practices and plays an important role in crop yield predictions. Multi-temporal satellite-based observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or deriving biophysical variables. This study presents a framework for automatic corn phenology characterization based on high spatial and temporal resolution time series. By using the Difference Vegetation Index (DVI) estimated from Sentinel-2 data over Iowa (US), independent phenological models were optimized using Gaussian Processes regression. Their respective performances were assessed based on simulated phenological indi…
Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index
2018
Crop canopy water content (CWC) is an essential indicator of the crop’s physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar …
Scenario-based discrimination of common grapevine varieties using in-field hyperspectral data in the western of Iran
2019
Abstract Field spectroscopy is an accurate, rapid and nondestructive technique for monitoring of agricultural plant characteristics. Among these, identification of grapevine varieties is one of the most important factors in viticulture and wine industry. This study evaluated the discriminatory ability of field hyperspectral data and statistical techniques in case of five common grapevine varieties in the western of Iran. A total of 3000 spectral samples were acquired at leaf and canopy levels. Then, in order to identify the best approach, two types of hyperspectral data (wavelengths from 350 to 2500 nm and 32 spectral indices), two data reduction methods (PLSR and ANOVA-PCA) and two classif…
Cheese flavour : instrumental techniques
2004
This chapter discusses instrumental techniques to analyze cheese flavor. It focuses on recent advances made to study and identify the taste-active components present in the water-soluble fraction of cheese. A general procedure for the preparation of fractions involves an extraction of grated cheese by water followed by a fractionation scheme, generally adapted from the fractionation protocol used to isolate cheese nitrogen fractions in the study of proteolysis in cheese during ripening. However, as sub-fractions have to be evaluated sensorially to assess their relative sensory impact and try to link it to their chemical composition, a suitable eluent has to be used in the chromatographic st…
Crop Yield Estimation and Interpretability With Gaussian Processes
2021
This work introduces the use of Gaussian processes (GPs) for the estimation and understanding of crop development and yield using multisensor satellite observations and meteo- rological data. The proposed methodology combines synergistic information on canopy greenness, biomass, soil, and plant water content from optical and microwave sensors with the atmospheric variables typically measured at meteorological stations. A com- posite covariance is used in the GP model to account for varying scales, nonstationary, and nonlinear processes. The GP model reports noticeable gains in terms of accuracy with respect to other machine learning approaches for the estimation of corn, wheat, and soybean …
Land use classification from multitemporal Landsat imagery using the Yearly Land Cover Dynamics (YLCD) method
2011
Abstract Several previous studies have shown that the inclusion of the LST (Land Surface Temperature) parameter to a NDVI (Normalized Difference Vegetation Index) based classification procedure is beneficial to classification accuracy. In this work, the Yearly Land Cover Dynamics (YLCD) approach, which is based on annual behavior of LST and NDVI, has been used to classify an agricultural area into crop types. To this end, a time series of Landsat-5 images for year 2009 of the Barrax (Spain) area has been processed: georeferenciation, destriping and atmospheric correction have been carried out to estimate NDVI and LST time series for year 2009, from which YLCD parameters were estimated. Then…
Effect of cactus pear cultivation after Mediterranean maquis on soil carbon stock, δ13C spatial distribution and root turnover
2014
Abstract Mediterranean ecosystems are characterized by nearly complete replacement of natural vegetation by intensive croplands and orchards leading to strong soil degradation. Organic carbon is usually accumulated in soils under maquis leading to partial regeneration of fertility for future agricultural use. The aim of this work was to investigate the effect of land use change from maquis to agriculture on soil organic carbon (SOC) stock and its spatial distribution in a Mediterranean system. Three Mediterranean land use systems (seminatural vegetation, cactus pear crop and olive grove) were selected in Sicily and analysed for soil C stocks and their δ13C. Total SOC and δ13C were measured …