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Remote Sens. 2017, 9(5), 509; doi:10.3390/rs9050509

Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models

1
DAFNE, Università della Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy
2
Consiglio Nazionale delle Ricerche—Institute of Methodologies for Environmental Analysis (C.N.R.—IMAA), Via del Fosso del Cavaliere, 100, 00133 Roma, Italy
3
Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
4
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Academic Editors: James Campbell and Prasad S. Thenkabail
Received: 5 March 2017 / Revised: 15 May 2017 / Accepted: 19 May 2017 / Published: 22 May 2017
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Abstract

Accurate yield estimation at the field scale is essential for the development of precision agriculture management, whereas at the district level it can provide valuable information for supply chain management. In this paper, Huan Jing (HJ) satellite HJ1A/B and Landsat 8 Operational Land Imager (OLI) images were employed to retrieve leaf area index (LAI) and canopy cover (CC) in the Yangling area (Central China). These variables were then assimilated into two crop models, Aquacrop and simple algorithm for yield (SAFY), in order to compare their performances and practicalities. Due to the models’ specificities and computational constraints, different assimilation methods were used. For SAFY, the ensemble Kalman filter (EnKF) was applied using LAI as the observed variable, while for Aquacrop, particle swarm optimization (PSO) was used, using canopy cover (CC). These techniques were applied and validated both at the field and at the district scale. In the field application, the lowest relative root-mean-square error (RRMSE) value of 18% was obtained using EnKF with SAFY. On a district scale, both methods were able to provide production estimates in agreement with data provided by the official statistical offices. From an operational point of view, SAFY with the EnKF method was more suitable than Aquacrop with PSO, in a data assimilation context. View Full-Text
Keywords: leaf area index (LAI); canopy cover (CC); Landsat 8; HJ1A/B; artificial neural network (ANN); ensemble Kalman filter (EnKF); particle swarm optimization (PSO) leaf area index (LAI); canopy cover (CC); Landsat 8; HJ1A/B; artificial neural network (ANN); ensemble Kalman filter (EnKF); particle swarm optimization (PSO)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Silvestro, P.C.; Pignatti, S.; Pascucci, S.; Yang, H.; Li, Z.; Yang, G.; Huang, W.; Casa, R. Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models. Remote Sens. 2017, 9, 509.

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