Xin Xu, Shuaijie Shen, F. Gao, Jian Wang, Xinming Ma, Shuping Xiong, Zehua Fan
{"title":"考虑不同供水量可以提高WOFOST作物模型和遥感同化预测小麦产量的准确性","authors":"Xin Xu, Shuaijie Shen, F. Gao, Jian Wang, Xinming Ma, Shuping Xiong, Zehua Fan","doi":"10.31545/intagr/154892","DOIUrl":null,"url":null,"abstract":". The study was carried out in order to clarify the effects of different water and irrigation conditions on crop models and remote sensing assimilation results. It involved taking winter wheat from 17 test sites in Henan Province as the research object and calibrating the World Food Studies model. The ensemble Kalman filter algorithm was used to calibrate the two modes and Moderate-resolution Imaging Spectroradiometer-Leaf Area Index of the calibrated world food studies model. The study found that the average error of the world food studies model for simulating flowering and maturity periods is within 2 days, the R 2 of the leaf area index calibration results is between 0.87-0.98, and the R 2 and root mean square error of the verification results are 0.77 and 1.06 respectively. Under the latent model, the R 2 of the world food studies model taking account of the water supply situation and the assimilation results without taking account of the water supply situation are 0.50 and 0.48, respectively. In the water restriction mode, the R 2 increased from 0.79 to 0.86 compared with the assimilation results where the water supply was not considered. The results show that: depending on the water supply of different regions, selecting the corresponding assimilation parameters can effectively improve the prediction accuracy of crop models and remote sensing assimilation for wheat yields under different water and irrigation conditions.","PeriodicalId":13959,"journal":{"name":"International Agrophysics","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Considering different water supplies can improve the accuracyof the WOFOST crop model and remote sensing assimilation in predicting wheat yield\",\"authors\":\"Xin Xu, Shuaijie Shen, F. Gao, Jian Wang, Xinming Ma, Shuping Xiong, Zehua Fan\",\"doi\":\"10.31545/intagr/154892\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". The study was carried out in order to clarify the effects of different water and irrigation conditions on crop models and remote sensing assimilation results. It involved taking winter wheat from 17 test sites in Henan Province as the research object and calibrating the World Food Studies model. The ensemble Kalman filter algorithm was used to calibrate the two modes and Moderate-resolution Imaging Spectroradiometer-Leaf Area Index of the calibrated world food studies model. The study found that the average error of the world food studies model for simulating flowering and maturity periods is within 2 days, the R 2 of the leaf area index calibration results is between 0.87-0.98, and the R 2 and root mean square error of the verification results are 0.77 and 1.06 respectively. Under the latent model, the R 2 of the world food studies model taking account of the water supply situation and the assimilation results without taking account of the water supply situation are 0.50 and 0.48, respectively. In the water restriction mode, the R 2 increased from 0.79 to 0.86 compared with the assimilation results where the water supply was not considered. The results show that: depending on the water supply of different regions, selecting the corresponding assimilation parameters can effectively improve the prediction accuracy of crop models and remote sensing assimilation for wheat yields under different water and irrigation conditions.\",\"PeriodicalId\":13959,\"journal\":{\"name\":\"International Agrophysics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2022-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Agrophysics\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.31545/intagr/154892\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Agrophysics","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.31545/intagr/154892","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRONOMY","Score":null,"Total":0}
Considering different water supplies can improve the accuracyof the WOFOST crop model and remote sensing assimilation in predicting wheat yield
. The study was carried out in order to clarify the effects of different water and irrigation conditions on crop models and remote sensing assimilation results. It involved taking winter wheat from 17 test sites in Henan Province as the research object and calibrating the World Food Studies model. The ensemble Kalman filter algorithm was used to calibrate the two modes and Moderate-resolution Imaging Spectroradiometer-Leaf Area Index of the calibrated world food studies model. The study found that the average error of the world food studies model for simulating flowering and maturity periods is within 2 days, the R 2 of the leaf area index calibration results is between 0.87-0.98, and the R 2 and root mean square error of the verification results are 0.77 and 1.06 respectively. Under the latent model, the R 2 of the world food studies model taking account of the water supply situation and the assimilation results without taking account of the water supply situation are 0.50 and 0.48, respectively. In the water restriction mode, the R 2 increased from 0.79 to 0.86 compared with the assimilation results where the water supply was not considered. The results show that: depending on the water supply of different regions, selecting the corresponding assimilation parameters can effectively improve the prediction accuracy of crop models and remote sensing assimilation for wheat yields under different water and irrigation conditions.
期刊介绍:
The journal is focused on the soil-plant-atmosphere system. The journal publishes original research and review papers on any subject regarding soil, plant and atmosphere and the interface in between. Manuscripts on postharvest processing and quality of crops are also welcomed.
Particularly the journal is focused on the following areas:
implications of agricultural land use, soil management and climate change on production of biomass and renewable energy, soil structure, cycling of carbon, water, heat and nutrients, biota, greenhouse gases and environment,
soil-plant-atmosphere continuum and ways of its regulation to increase efficiency of water, energy and chemicals in agriculture,
postharvest management and processing of agricultural and horticultural products in relation to food quality and safety,
mathematical modeling of physical processes affecting environment quality, plant production and postharvest processing,
advances in sensors and communication devices to measure and collect information about physical conditions in agricultural and natural environments.
Papers accepted in the International Agrophysics should reveal substantial novelty and include thoughtful physical, biological and chemical interpretation and accurate description of the methods used.
All manuscripts are initially checked on topic suitability and linguistic quality.