Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring
| Autor: | Gonçal Grau-Muedra, Mirco Boschetti, Francesco Nutini, Francisco Javier García-Haro, Alberto Crema, Gustau Camps-Valls, Manuel Campos-Taberner |
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| Rok vydání: | 2016 |
| Předmět: |
2. Zero hunger
010504 meteorology & atmospheric sciences Mean squared error 0211 other engineering and technologies Soil Science Geology Inversion (meteorology) 02 engineering and technology Crop monitoring Rice Leaf area index (LAI) retrieval PROSAIL Smartphone Gaussian process regression (GPR) Landsat SPOT5 Take5 01 natural sciences Atmospheric radiative transfer codes Kriging Satellite data Ground-penetrating radar Environmental science Computers in Earth Sciences Leaf area index Rice crop 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Remote Sensing of Environment Remote sensing of environment 187 (2016): 102–118. doi:10.1016/j.rse.2016.10.009 info:cnr-pdr/source/autori:Campos-Taberner M.; Garcia-Haro F.J.; Camps-Valls G.; Grau-Muedra G.; Nutini F.; Crema A.; Boschetti M./titolo:Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring/doi:10.1016%2Fj.rse.2016.10.009/rivista:Remote sensing of environment/anno:2016/pagina_da:102/pagina_a:118/intervallo_pagine:102–118/volume:187 |
| ISSN: | 0034-4257 |
| DOI: | 10.1016/j.rse.2016.10.009 |
| Popis: | 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 evolution of the LAI estimates using Landsat and SPOT5 data followed consistently the temporal evolution of the in situ LAI measurements acquired on several Mediterranean rice varieties. The estimates had a root-mean-square-error (RMSE) of 0.39 and 0.51 m 2 /m 2 in Spain and 0.38 and 0.47 m 2 /m 2 in Italy for Landsat and SPOT5 respectively, with a strong correlation (R 2 > 0.92) for both cases. Spatial-temporal assessment of the estimated LAI from Landsat and SPOT5 data confirmed the robustness and consistency of the retrieval chain. This paper demonstrates the importance of an adequate characterization of the underlying rice background in order to address changes in background condition related to water management. Results highlight the potential of the proposed chain for deriving multitemporal near real-time decametric LAI maps fundamental for operational rice crop monitoring, and demonstrate the readiness of the proposed method for the processing of data such as the recently launched Sentinel-2. |
| Databáze: | OpenAIRE |
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