Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model
Few studies were focused on yield estimation of perennial fruit tree crops by integrating remotely-sensed information into crop models. This study presented an attempt to assimilate a single leaf area index (LAI) near to maximum vegetative development stages derived from Landsat satellite data into...
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doaj-b7cf206ec7974287b7b7991e284566302020-11-25T00:14:41ZengMDPI AGRemote Sensing2072-42922019-05-01119111910.3390/rs11091119rs11091119Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST ModelTiecheng Bai0Nannan Zhang1Benoit Mercatoris2Youqi Chen3Southern Xinjiang Research Center for Information Technology in Agriculture, Tarim University, Alaer 843300, ChinaSouthern Xinjiang Research Center for Information Technology in Agriculture, Tarim University, Alaer 843300, ChinaTERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, Liège University, Passage des Déportés, 2, 5030 Gembloux, BelgiumInstitute of Agricultural Resources and Regional Planning of CAAS, No.12 Zhongguancun South St., Haidian District, Beijing 100081, ChinaFew studies were focused on yield estimation of perennial fruit tree crops by integrating remotely-sensed information into crop models. This study presented an attempt to assimilate a single leaf area index (LAI) near to maximum vegetative development stages derived from Landsat satellite data into a calibrated WOFOST model to predict yields for jujube fruit trees at the field scale. Field experiments were conducted in three growth seasons to calibrate input parameters for WOFOST model, with a validated phenology error of −2, −3, and −3 days for emergence, flowering, and maturity, as well as an R<sup>2</sup> of 0.986 and RMSE of 0.624 t ha<sup>−1</sup> for total aboveground biomass (TAGP), R<sup>2</sup> of 0.95 and RMSE of 0.19 m<sup>2</sup> m<sup>−2</sup> for LAI, respectively. Normalized Difference Vegetation Index (NDVI) showed better performance for LAI estimation than a Soil-adjusted Vegetation Index (SAVI), with a better agreement (R<sup>2</sup> = 0.79) and prediction accuracy (RMSE = 0.17 m<sup>2</sup> m<sup>−2</sup>). The assimilation after forcing LAI improved the yield prediction accuracy compared with unassimilated simulation and remotely sensed NDVI regression method, showing a R<sup>2</sup> of 0.62 and RMSE of 0.74 t ha<sup>−1</sup> for 2016, and R<sup>2</sup> of 0.59 and RMSE of 0.87 t ha<sup>−1</sup> for 2017. This research would provide a strategy to employ remotely sensed state variables and a crop growth model to improve field-scale yield estimates for fruit tree crops.https://www.mdpi.com/2072-4292/11/9/1119Assimilationleaf area indexjujube yield estimationWOFOST model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tiecheng Bai Nannan Zhang Benoit Mercatoris Youqi Chen |
spellingShingle |
Tiecheng Bai Nannan Zhang Benoit Mercatoris Youqi Chen Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model Remote Sensing Assimilation leaf area index jujube yield estimation WOFOST model |
author_facet |
Tiecheng Bai Nannan Zhang Benoit Mercatoris Youqi Chen |
author_sort |
Tiecheng Bai |
title |
Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model |
title_short |
Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model |
title_full |
Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model |
title_fullStr |
Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model |
title_full_unstemmed |
Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model |
title_sort |
improving jujube fruit tree yield estimation at the field scale by assimilating a single landsat remotely-sensed lai into the wofost model |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-05-01 |
description |
Few studies were focused on yield estimation of perennial fruit tree crops by integrating remotely-sensed information into crop models. This study presented an attempt to assimilate a single leaf area index (LAI) near to maximum vegetative development stages derived from Landsat satellite data into a calibrated WOFOST model to predict yields for jujube fruit trees at the field scale. Field experiments were conducted in three growth seasons to calibrate input parameters for WOFOST model, with a validated phenology error of −2, −3, and −3 days for emergence, flowering, and maturity, as well as an R<sup>2</sup> of 0.986 and RMSE of 0.624 t ha<sup>−1</sup> for total aboveground biomass (TAGP), R<sup>2</sup> of 0.95 and RMSE of 0.19 m<sup>2</sup> m<sup>−2</sup> for LAI, respectively. Normalized Difference Vegetation Index (NDVI) showed better performance for LAI estimation than a Soil-adjusted Vegetation Index (SAVI), with a better agreement (R<sup>2</sup> = 0.79) and prediction accuracy (RMSE = 0.17 m<sup>2</sup> m<sup>−2</sup>). The assimilation after forcing LAI improved the yield prediction accuracy compared with unassimilated simulation and remotely sensed NDVI regression method, showing a R<sup>2</sup> of 0.62 and RMSE of 0.74 t ha<sup>−1</sup> for 2016, and R<sup>2</sup> of 0.59 and RMSE of 0.87 t ha<sup>−1</sup> for 2017. This research would provide a strategy to employ remotely sensed state variables and a crop growth model to improve field-scale yield estimates for fruit tree crops. |
topic |
Assimilation leaf area index jujube yield estimation WOFOST model |
url |
https://www.mdpi.com/2072-4292/11/9/1119 |
work_keys_str_mv |
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