Search results for "Remote sensing"

2020

AbstractForecasting crop yields is becoming increasingly important under the current context in which food security needs to be ensured despite the challenges brought by climate change, an expanding world population accompanied by rising incomes, increasing soil erosion, and decreasing water resources. Temperature, radiation, water availability and other environmental conditions influence crop growth, development, and final grain yield in a complex nonlinear manner. Machine learning (ML) techniques, and deep learning (DL) methods in particular, can account for such nonlinear relations between yield and its covariates. However, they typically lack transparency and interpretability, since the…

Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring.

2022

Abstract For agricultural applications, identification of non-photosynthetic above-ground vegetation is of great interest as it contributes to assess harvest practices, detecting crop residues or drought events, as well as to better predict the carbon, water and nutrients uptake. While the mapping of green Leaf Area Index (LAI) is well established, current operational retrieval models are not calibrated for LAI estimation over senescent, brown vegetation. This not only leads to an underestimation of LAI when crops are ripening, but is also a missed monitoring opportunity. The high spatial and temporal resolution of Sentinel-2 (S2) satellites constellation offers the possibility to estimate …

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…

Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions

2020

Abstract Nitrogen (N) is considered as one of the most important plant macronutrients and proper management of N therefore is a pre-requisite for modern agriculture. Continuous satellite-based monitoring of this key plant trait would help to understand individual crop N use efficiency and thus would enable site-specific N management. Since hyperspectral imaging sensors could provide detailed measurements of spectral signatures corresponding to the optical activity of chemical constituents, they have a theoretical advantage over multi-spectral sensing for the detection of crop N. The current study aims to provide a state-of-the-art overview of crop N retrieval methods from hyperspectral data…

Integrating Physics Modelling with Machine Learning for Remote Sensing

2020

L’observació de la Terra a partir de les dades proporcionades per sensors abord de satèl·lits, així com les proporcionades per models de transferència radiativa o climàtics, juntament amb les mesures in situ proporcionen una manera sense precedents de monitorar el nostre planeta amb millors resolucions espacials i temporals. La riquesa, quantitat i diversitat de les dades adquirides i posades a disposició també augmenta molt ràpidament. Aquestes dades ens permeten predir el rendiment dels cultius, fer un seguiment del canvi d’ús del sòl com ara la desforestació, supervisar i respondre als desastres naturals, i predir i mitigar el canvi climàtic. Per tal de fer front a tots aquests reptes, l…

Are remote sensing evapotranspiration models reliable across South American ecoregions?

2021

Many remote sensing-based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman–Monteith Mu model (PM-MOD), and Penman–Monteith Nagler model (PME-VI).…

Quantification of LAI interannual anomalies by adjusting climatological patterns

2011

International audience; Scaling variations and shifts in the timing of seasonal phenology are central features of global change research. In this study, we propose a novel climatology fitting approach to quantify inter-annual anomalies in LAI seasonality. A consistent archive of daily LAI estimates was first derived from historical AVHRR satellite data for the 1981-2000 period over a globally representative sample of sites. The climatology values were then computed by averaging multi-year LAI profiles, gap filling and smoothing to eliminate possible high temporal frequency residual artifacts. The inter-annual variations in LAI were finally quantified by scaling and shifting the seasonal cli…

GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparis…

2013

International audience; This paper describes the scientific validation of the first version of global biophysical products (i.e., leaf area index, fraction of absorbed photosynthetically active radiation and fraction of vegetation cover), namely GEOV1, developed in the framework of the geoland-2/BioPar core mapping service at 1 km spatial resolution and 10-days temporal frequency. The strategy follows the recommendations of the CEOS/WGCV Land Product Validation for LAI global products validation. Several criteria of performance were evaluated, including continuity, spatial and temporal consistency, dynamic range of retrievals, statistical analysis per biome type, precision and accuracy. The…

Accuracy assessment and position correction for low-cost non-differential GPS as applied on an industrial peat bog

1999

A low-cost, non-differentially corrected hand-held GPS receiver was tested on an industrial peat production bog. A correction procedure (‘pseudo-differential correction’) was derived that corrected data points to the nearest position on a line defining the centre of each 15-m wide field. The result was a corrected log of track points for each field for all points lying along the field. It was found that the mean orthogonal distance from a field centreline was linearly correlated with mean uncorrected GPS data error (r 2 0.99) such that as GPS error increased so the accuracy obtained by correction decreased. For a signal with a mean uncorrected error of 30 m it was possible to reduce the err…

Residual errors in ASTER temperature and emissivity standard products AST08 and AST05

2011

Abstract Land surface temperature and emissivity are independent variables, and the thermal-infrared spectral radiance measured in remote sensing is dependent on both. Therefore the inverse Planck equation is under-determined, with two unknowns and a single measurement. Practical inversion algorithms designed to calculate temperature and emissivity from the measurements cannot do a perfect job of separation, and recovered temperature and emissivity may co-vary. For ASTER images, validation studies of recovered temperature and emissivity, regarded individually, have shown that they are within the precision and accuracy limits predicted in designing the ASTER TES algorithm used to calculate …