📝 Publications

🌟Selected Paper🌟

1. Wen, Zhuo; Hai, Huang; Xinran, Gao; Xuecao, Li; Jianxi, Huang. An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model. Remote Sensing, 2023; 15(7):1825.
   • This study aims to examine the accuracy of crop yield estimation through joint assimilation of leaf area index (LAI) and soil moisture (SM) and to examine the scale effect between remotely sensed data and crop model simulations. Download paper here
   • 
2. Wen, Zhuo; Shibo, Fang; Xinran, Gao; Lei, Wang; Dong, Wu; Shaolong, Fu; Qingling, Wu; Jianxi, Huang. Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: A case study for winter wheat in Hebei, China during 2009–2013. International Journal of Applied Earth Observation and Geoinformation, 2022; 106, 102668.
   • The objective of this study is to predict winter wheat yield at the regional scale and improve the accuracy by assimilating remotely sensed observations. Download paper here
   • 
3. Wen, Zhuo; Jianxi, Huang; Xiangming, Xiao; Hai, Huang; Rajen, Bajgain; Xiaocui, Wu; Xinran, Gao; Jie, Wang; Xuecao, Li; Pradeep, Wagle. Assimilating remote sensing-based VPM GPP into the WOFOST model for improving regional winter wheat yield estimation. European Journal of Agronomy, 2022; 139(11), 126556.
   • In this study, a novel crop data-model assimilation framework was proposed that assimilates accumulative GPP estimates from the VPM model into the WOFOST model using EnKF algorithm to estimate winter wheat GPP and grain yield. Download paper here
   • 
4. Wen, Zhuo; Shibo, Fang; Dong, Wu; Lei, Wang; Mengqian, Li; Jiansu, Zhang; Xinran, Gao. Integrating remotely sensed water stress factor and crop growth model for winter wheat yield estimation in the North China Plain during 2008 to 2018. Crop Journal, 2022, 10, 1470–1482.
   • The objective of this study was to estimate winter wheat yield at the regional scale in the NCP, and to improve yield estimation accuracy by integrating remotely sensed water stress factor with the WOFOST model. Download paper here
   • 

⭐All Paper List (Updated on April 20, 2024)⭐

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A fast and easy way to produce a 1-km all-weather land surface temperature dataset for China utilizing more ground-based data

Published in IEEE Transactions on Geoscience and Remote Sensing, 2024

This study is to achieve the simple and rapid acquisition of high-precision all-weather high temporal and spatial resolution daily mean land surface temperature data.

Recommended citation: Yanru, Yu; Shibo Fang; Wen Zhuo, Jiahao Han. A fast and easy way to produce a 1-km all-weather land surface temperature dataset for China utilizing more ground-based data. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62, 5002016. https://doi.org/10.1109/TGRS.2024.3368707

A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations

Published in Science of the Total Environment, 2024

This study aims to adopt the “Earth big data” generated from high-density station observation and microwave remote sensing data to monitor LST based on deep learning (U-Net family) for the first time.

Recommended citation: Jiahao, Han; Shibo, Fang; Qianchuan, Mi; Xinyu, Wang; Yanru, Yu; Wen, Zhuo; Xiaofeng, Peng. A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations. Science of The Total Environment, 2024, 914, 169992. https://doi.org/10.1016/j.scitotenv.2024.169992

Revealing the Driving Mechanisms of Land Surface Temperature Spatial Heterogeneity and Its Sensitive Regions in China Based on GeoDetector

Published in Remote Sensing, 2023

This study investigated the driving forces of LST at the annual scale from the period 2003–2018 in the perspective of spatial heterogeneity based on GeoDetector.

Recommended citation: Yanru, Yu; Shibo Fang; Wen Zhuo. Revealing the Driving Mechanisms of Land Surface Temperature Spatial Heterogeneity and Its Sensitive Regions in China Based on GeoDetector. Remote Sensing, 2023, 15(11), 2814. https://doi.org/10.3390/rs15112814

The Improved Winter Wheat Yield Estimation by Assimilating GLASS LAI Into a Crop Growth Model With the Proposed Bayesian Posterior-Based Ensemble Kalman Filter

Published in IEEE Transactions on Geoscience and Remote Sensing, 2023

This study introduces the Bayesian posterior uncertain information from MCMC calibration into an EnKF data assimilation framework to better quantify the model uncertainty in the data assimilation system.

Recommended citation: Hai, Huang; Jianxi, Huang; Yantong, Wu; Wen, Zhuo; Jianjian, Song; Xuecao, Li; Li, Li; Wei, Su; Han, Ma; Shunlin, Liang. The Improved Winter Wheat Yield Estimation by Assimilating GLASS LAI Into a Crop Growth Model With the Proposed Bayesian Posterior-Based Ensemble Kalman Filter. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61, 1-18. https://doi.org/10.1109/TGRS.2023.3259742

An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model

Published in Remote Sensing, 2023

This study aims to examine the accuracy of crop yield estimation through the joint assimilation of leaf area index (LAI) and soil moisture (SM) and to examine the scale effect between remotely sensed data and crop model simulations.

Recommended citation: Wen, Zhuo; Hai, Huang; Xinran, Gao; Xuecao, Li; Jianxi, Huang. An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model. Remote Sensing, 2023; 15(7):1825. https://doi.org/10.3390/rs15071825

Assimilating remote sensing-based VPM GPP into the WOFOST model for improving regional winter wheat yield estimation

Published in European Journal of Agronomy, 2022

In this study, a novel crop data-model assimilation framework was proposed that assimilates accumulative GPP estimates from the VPM model into the WOFOST model using EnKF algorithm to estimate winter wheat GPP and grain yield.

Recommended citation: Wen, Zhuo; Jianxi, Huang; Xiangming, Xiao; Hai, Huang; Rajen, Bajgain; Xiaocui, Wu; Xinran, Gao; Jie, Wang; Xuecao, Li; Pradeep, Wagle. Assimilating remote sensing-based VPM GPP into the WOFOST model for improving regional winter wheat yield estimation. European Journal of Agronomy, 2022; 139(11), 126556. https://doi.org/10.1016/j.eja.2022.126556

Effects of climate warming on the potential northern planting boundaries of Three Main Grain Crops in China

Published in Agriculture, 2022

In this study, we analyzed the effect of climate warming on the potential northern planting boundaries of three specific crops (winter wheat, spring maize, double and triple rice cropping systems) during two time periods (1961–1990 and 1991–2020) using meteorological data from 2437 national weather stations.

Recommended citation: Wen, Zhuo; Shibo, Fang; Yuping, Ma; Rui, Zhang; Lei, Wang; Mengqian, Li; Jiansu, Zhang; Xinran, Gao. Effects of climate warming on the potential northern planting boundaries of three main grain crops in China. Agriculture, 2022; 12(6), 746. https://doi.org/10.3390/agriculture12060746

Integrating remotely sensed water stress factor with a crop growth model for winter wheat yield estimation in the North China Plain during 2008–2018

Published in Crop Journal, 2022

The objective of this study was to estimate winter wheat yield at the regional scale in the NCP, and to improve yield estimation accuracy by integrating remotely sensed water stress factor with the WOFOST model.

Recommended citation: Wen, Zhuo; Shibo, Fang; Dong, Wu; Lei, Wang; Mengqian, Li; Jiansu, Zhang; Xinran, Gao. Integrating remotely sensed water stress factor and crop growth model for winter wheat yield estimation in the North China Plain during 2008 to 2018. Crop Journal, 2022, 10, 1470–1482. https://doi.org/10.1016/j.cj.2022.04.004

A dataset of winter wheat aboveground biomass in China during 2007–2015 based on data assimilation

Published in Scientific Data, 2022

This study aims to assimilate a reprocessed temporal-spatial fltered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015.

Recommended citation: Hai, Huang; Jianxi, Huang; Xuecao, Li; Wen, Zhuo; Yantong, Wu; Quandi, Niu; Wei, Su; Wenping, Yuan. A dataset of winter wheat aboveground biomass in China during 2007–2015 based on data assimilation. Scientific Data, 2022, 9(1), 200. https://doi.org/10.1038/s41597-022-01305-6

Crop yield prediction using MODIS LAI TIGGE weather forecasts and WOFOST model A case study for winter wheat in Hebei China during 2009–2013

Published in International Journal of Applied Earth Observation and Geoinformation, 2021

The objective of this study is to predict winter wheat yield at the regional scale and improve the accuracy by assimilating remotely sensed observations.

Recommended citation: Wen, Zhuo; Shibo, Fang; Xinran, Gao; Lei, Wang; Dong, Wu; Shaolong, Fu; Qingling, Wu; Jianxi, Huang. Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model A case study for winter wheat in Hebei, China during 2009–2013. International Journal of Applied Earth Observation and Geoinformation, 2022; 106, 102668. https://doi.org/10.1016/j.jag.2021.102668

Developing machine learning models with multisource inputs for improved land surface soil moisture in China

Published in Computers and Electronics in Agriculture, 2021

This paper aims to achieve accurate and spatiotemporally continuous land SSM coverage within the 0–10 cm soil layer by building and comparing models with fve frequently used ML techniques.

Recommended citation: Lei, Wang; Shibo, Fang; Zhifang, Pei; Dong, Wu; Yongchao, Zhu; Wen, Zhuo. Developing machine learning models with multisource inputs for improved land surface soil moisture in China. Computers and Electronics in Agriculture, 2022, 192, 106623. https://doi.org/10.1016/j.compag.2021.106623

Analysis of variation in reference evapotranspiration and its driving factors in mainland China from 1960 to 2016

Published in Environmental Research Letters, 2021

This study aims to analyze the spatial distribution of annual and seasonal ETo in mainland China from 1960 to 2016.

Recommended citation: Dong, Wu; Shibo, Fang; Xingyuan, Tong; Lei, Wang; Wen, Zhuo; Zhifang, Pei; Yingjie, Wu; Ju, Zhang; Mengqian, Li. Analysis of variation in reference evapotranspiration and its driving factors in mainland China from 1960 to 2016. Environmental Research Letters, 2021, 16, 054016. https://doi.org/10.1088/1748-9326/abf687

Using long-term earth observation data to reveal the factors contributing to the early 2020 desert locust upsurge and the resulting vegetation loss

Published in Remote Sensing, 2021

In this study, remotely sensed images and long-term meteorological observations across the desert locust invasion area were analyzed to explore the complex drivers, vegetation losses and growing trends during the locust upsurge.

Recommended citation: Lei, Wang; Wen, Zhuo; Zhifang, Pei; Xingyuan, Tong; Wei, Han; Shibo, Fang. Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss. Remote Sensing, 2021, 13, 680. https://doi.org/10.3390/rs13040680

Prediction of winter wheat maturity dates through assimilating remotely sensed leaf area index into crop growth model

Published in Remote Sensing, 2020

In this study, a data assimilation framework incorporating the LAI product from MODIS into a WOFOST model was proposed to predict the maturity dates of winter wheat in Henan province, China.

Recommended citation: Wen, Zhuo; Jianxi, Huang; Xinran, Gao; Hongyuan, Ma; Hai, Huang; Wei, Su; Jihua, Meng; Ying, Li; Huailiang, Chen; Dongqin, Yin. Prediction of winter wheat maturity dates through assimilating remotely sensed leaf area index into crop growth model. Remote Sensing. 2020; 12(18), 2896. https://doi.org/10.3390/rs12182896

Soil temperature estimation at different depths, using remotely-sensed data

Published in Journal of Integrative Agriculture, 2020

This study aims at proposing a simple but accurate approach to providing spatially continuous estimation of the daily spatial dynamics of soil temperature using moderate resolution imaging spectroradiometer (MODIS), taking northern China as case study.

Recommended citation: Ran, Huang; Jianxi, Huang; Chao, Zhang; Hongyuan, Ma; Wen, Zhuo; Yingyi, Chen; Dehai, Zhu; Qingling, Wu; Mansaray, R. Lamin. Soil temperature estimation at different depths using remotely sensed data. Journal of Integrative Agriculture, 2019, 18(0) 2-15. https://doi.org/10.1016/S2095-3119(19)62657-2

Assimilating soil moisture retrieved from Sentinel-1 and Sentinel-2 data into WOFOST model to improve winter wheat yield estimation

Published in Remote Sensing, 2019

The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model.

Recommended citation: Wen, Zhuo; Jianxi, Huang; Li, Li; Xiaodong, Zhang; Hongyuan, Ma; Xinran, Gao; Baodong, Xu; Xiangming, Xiao. Assimilating soil moisture retrieved from Sentinel-1 and Sentinel-2 data into WOFOST model to improve winter wheat yield estimation. Remote Sensing, 2019; 11(13), 1618. https://doi.org/10.3390/rs11131618

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

Published in International Journal of Digital Earth, 2018

In this study, we evaluated the ability of three widely used remotely sensed drought indices (VHI, DSI and TVDI) for agricultural drought monitoring in Shaanxi and Henan provinces in the North China Plain.

Recommended citation: Jianxi, Huang; Wen, Zhuo; Ying, Li; Ran, Huang; Fernando, Sedano; Jinwei, Dong; Liyan, Tian; Yanbo, Huang; Dehai, Zhu; Xiaodong, Zhang. Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield. International Journal of Digital Earth, 2020; 13(4):504-526. https://doi.org/10.1080/17538947.2018.1542040

RDCRMG: A raster dataset clean & reconstitution multi-grid architecture for remote sensing monitoring of vegetation dryness

Published in Remote Sensing, 2018

This paper reports on a Raster Dataset Clean & Reconstitution Multi-Grid (RDCRMG) architecture for remote sensing monitoring of vegetation dryness in which different types of raster datasets have been partitioned, organized and systematically applied.

Recommended citation: Sijing, Ye; Diyou, Liu; Xiaochuang, Yao; Huaizhi, Tang; Quan, Xiong; Wen, Zhuo; Zhenbo, Du; Jianxi, Huang; Wei, Su; Shi, Shen; Zuliang, Zhao; Shaolong, Cui; Lixin, Ning; Dehai, Zhu; Changxiu, Cheng; Changqing, Song. RDCRMG A raster dataset clean & reconstitution multi-grid architecture for remote sensing monitoring of vegetation dryness. Remote Sensing, 2018, 10, 1376. https://doi.org/10.3390/rs10091376