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Título

Regional crop gross primary productivity and yield estimation using fused Landsat-MODIS data

AutorHe, Mingzhu; Kimball, John S.; Maneta, Marco P.; Maxwell, Bruce D.; Moreno, Álvaro; Beguería, Santiago ; Wu, Xiaocui
Palabras clavecrop yield
gross primary productivity (GPP)
data fusion
Landsat
MODIS
Fecha de publicaciónfeb-2018
EditorMultidisciplinary Digital Publishing Institute
CitaciónHe M, Kimball JS, Maneta MP, Maxwell BD, Moreno A, Beguería S, Wu X. Regional crop gross primary productivity and yield estimation using fused Landsat-MODIS data. Remote Sensing 10 (3): art. 372 (2018).
ResumenAccurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too coarse to capture cropland heterogeneity. The fusion of data from different sensors can provide enhanced information and overcome many of the limitations of individual sensors. In thitables study, we estimate annual crop yields for seven important crop types across Montana in the continental USA from 2008–2015, including alfalfa, barley, maize, peas, durum wheat, spring wheat and winter wheat. We used a satellite data-driven light use efficiency (LUE) model to estimate gross primary productivity (GPP) over croplands at 30-m spatial resolution and eight-day time steps using a fused NDVI dataset constructed by blending Landsat (5 or 7) and Terra MODIS reflectance data. The fused 30-m NDVI record showed good consistency with the original Landsat and MODIS data, but provides better spatiotemporal delineations of cropland vegetation growth. Crop yields were estimated at 30-m resolution as the product of estimated GPP accumulated over the growing season and a crop-specific harvest index (HIGPP). The resulting GPP estimates capture characteristic cropland productivity patterns and seasonal variations, while the estimated annual crop production results correspond favorably with reported county-level crop production data (r = 0.96, relative RMSE = 37.0%, p < 0.05) from the U.S. Department of Agriculture (USDA). The performance of estimated crop yields at a finer (field) scale was generally lower, but still meaningful (r = 0.42, relative RMSE = 50.8%, p < 0.05). Our methods and results are suitable for operational applications of crop yield monitoring at regional scales, suggesting the potential of using global satellite observations to improve agricultural management, policy decisions and regional/global food security.
Descripción21 Pags.- 5 Tabls.- 8 Figs. © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Versión del editorhttps://doi.org/10.3390/rs10030372
URIhttp://hdl.handle.net/10261/163550
DOI10.3390/rs10030372
ISSN2072-4292
E-ISSN2072-4292
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