0000000000535367
AUTHOR
Alfonso Calera
showing 5 related works from this author
Multisensor comparison of NDVI for a semi‐arid environment in Spain
2009
The joint use of multiresolution sensors from different satellites offers many opportunities to describe vegetation and its dynamics. This paper introduces the concept of a virtual constellation (defined as an ensemble of all Earth Observation satellites in orbit that satisfy common requirements) for agricultural applications and contributes to providing the necessary inter-sensor calibration methodology for spectral reflectances and NDVI. For this purpose, we performed an observational study, comparing reflectances and the Normalized Difference Vegetation Index (NDVI), from near-synchronous image pairs of Landsat 7 Enhanced Thematic Mapper Plus (ETM+), as the reference sensor and Landsat 5…
Fluorescence explorer (FLEX): An optimised payload to map vegetation photosynthesis from space
2006
The FLuorescence EXplorer (FLEX) mission proposes to launch a satellite for the global monitoring of steady-state chlorophyll fluorescence in terrestrial vegetation. Fluorescence is a sensitive probe of photosynthetic function in both healthy and physiologically perturbed vegetation, and a powerful non-invasive tool to track the status, resilience, and recovery of photochemical processes and moreover provides important information on overall photosynthetic performance with implications for related carbon sequestration. The early responsiveness of fluorescence to atmospheric, soil and plant water balance, as well as to atmospheric chemistry and human intervention in land usage makes it an ob…
Spectro-temporal reflectance surfaces: a new conceptual framework for the integration of remote-sensing data from multiple different sensors
2012
The conflict between spatial and temporal resolution of satellite systems, as well as the frequent presence of clouds in the images, has been a traditional limitation of remote sensing in the optical domain. Nevertheless, most of the conceptual tools and algorithms developed classically in remote sensing are based on the input of a series of cloud-free images from identical sensors. In this study, we propose a conceptual framework that is able to ingest data from several different sensors, make them homogeneous, eliminate clouds virtually, and make them usable in a flexible, efficient, and transparent way. The methodology is based on previous developments such as spatial ‘downscaling’, temp…
Monitoring barley and corn growth from remote sensing data at field scale
2004
Vegetation indices have been used for operational quantitative monitoring of vegetation. Here, corn and barley cultures have been used to relate meaningful biophysical parameters such as dry biomass and Crop Growth Rate (CGR) to the well-established Normalized Difference Vegetation Index (NDVI). We explain these relationships by means of the use of the Light Use Efficiency (LUE) models, based on the positive relation between primary production and Absorbed Photosynthetically Active Radiation (APAR). In these models we introduce NDVI as a linear estimator of f APAR. Experimental data over corn and barley show that dry biomass is linearly related to the Time-Integrated Value of the NDVI (TIND…
Monitoring 10-m LST from the Combination MODIS/Sentinel-2, Validation in a High Contrast Semi-Arid Agroecosystem
2020
Downscaling techniques offer a solution to the lack of high-resolution satellite Thermal InfraRed (TIR) data and can bridge the gap until operational TIR missions accomplishing spatio-temporal requirements are available. These techniques are generally based on the Visible Near InfraRed (VNIR)-TIR variable relations at a coarse spatial resolution, and the assumption that the relationship between spectral bands is independent of the spatial resolution. In this work, we adopted a previous downscaling method and introduced some adjustments to the original formulation to improve the model performance. Maps of Land Surface Temperature (LST) with 10-m spatial resolution were obtained as output fro…