0000000000211222
AUTHOR
Nieves Pasqualotto
Influencia del ángulo de observación en la estimación del índice de área foliar (LAI) mediante imágenes PROBA/CHRIS
La estimación de variables biofísicas como el Índice de Área Foliar (LAI) mediante técnicas de teledetección es objeto de numerosos estudios, ya que de su conocimiento se puede extraer valiosa información sobre el estado de la vegetación. En este trabajo se estudia la estimación del LAI mediante imágenes multiangulares PROBA/CHRIS, analizando el comportamiento de la reflectividad medida en sus 5 ángulos de observación, en las longitudes de onda de 665 y 705 nm correspondientes a la banda de absorción de la clorofila y la reflectividad de la vegetación en el Red-Edge, respectivamente. El Índice de Diferencia Normalizada (NDI) calculado en estas longitudes de onda, mostró una buena correlació…
Mapping productivity and essential biophysical parameters of cultivated tropical grasslands from sentinel-2 imagery.
Nitrogen (N) is the main nutrient element that maintains productivity in forages
Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index
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 …
Remote Estimation of Canopy Water Content in Different Crop Types with New Hyperspectral Indices
A diverse range of vegetation indices have earlier been developed for the remote estimation of canopy water content (CWC), but most of them are not universally applicable. The aim of this study is to define new indices valid for a wide variety of crop types, that allow to obtain 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), which consists of field data including water content and other biophysical parameters collected for 6 different crops (lucerne, corn, potato, sugar beet, garlic and onion) and associated TOC reflectance spectra acquired by the HyMap airborne sensor. Sp…
Canopy chlorophyll content and LAI estimation from Sentine1-2: Vegetation indices and Sentine1-2 Leve1-2A automatic products comparison
The aim of this work is to analyze different methodologies for the estimation of leaf area index (LAI) and canopy chlorophyll content (CCC), using the Sentine1-2 satellite. LAI and CCC are biophysical parameters indicator of crop health state and fundamental in the productivity prediction. The purpose is to define the most optimal LAI and CCC estimation method for operational use in the monitoring of agricultural areas. Moreover, the CCC and LAI automatic products obtained directly through the Sentinel Application Platform Software (SNAP) biophysical processor and Sentine1-2 images by means of an artificial neural network (ANN) are validated. On the other hand, common vegetation indices use…
Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)
The spatial quantification of green leaf area index (LAIgreen), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2) a spatial resolution of up to 10 m, and (3) narrow bands in the red and red-edge region, a highly promising mission for agricultural monitoring. The aim of this work is to define an easy implementable LAIgreen index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAIgreen measurements were used. Commonly used LAIgreen indices applied on the Sentinel-2 10 m ×