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Yue-Ming Hu
College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China

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Journal article
Published: 28 September 2018 in Sustainability
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To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.

ACS Style

Shu-Di Fan; Yue-Ming Hu; Lu Wang; Zhen-Hua Liu; Zhou Shi; Wen-Bin Wu; Yu-Chun Pan; Guang-Xing Wang; A-Xing Zhu; Bo Li. Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products. Sustainability 2018, 10, 3459 .

AMA Style

Shu-Di Fan, Yue-Ming Hu, Lu Wang, Zhen-Hua Liu, Zhou Shi, Wen-Bin Wu, Yu-Chun Pan, Guang-Xing Wang, A-Xing Zhu, Bo Li. Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products. Sustainability. 2018; 10 (10):3459.

Chicago/Turabian Style

Shu-Di Fan; Yue-Ming Hu; Lu Wang; Zhen-Hua Liu; Zhou Shi; Wen-Bin Wu; Yu-Chun Pan; Guang-Xing Wang; A-Xing Zhu; Bo Li. 2018. "Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products." Sustainability 10, no. 10: 3459.

Journal article
Published: 23 July 2018 in Sustainability
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The rapid and accurate acquisition of rice cultivation information is very important for the management and assessment of rice agriculture and for research on food security, the use of agricultural water resources, and greenhouse gas emissions. Rice mapping methods based on phenology have been widely used but further studies are needed to clearly quantify the rice characteristics during the growth cycle. This paper selected the area where rice agriculture has undergone tremendous changes as the observation object. The rice areas were mapped in three time periods during the period from 1993 to 2016 by combining the characteristics of the harvested areas, flooded areas, and the time interval when harvesting and flooding occurred. An error matrix was used to determine the mapping accuracy. After exclusion of clouds and cloud shadows, the overall accuracy of the paddy fields was higher than 90% (90.5% and 93.5% in period 1 and period 3, respectively). Mixed pixels, image quality, and image acquisition time are important factors affecting the accuracy of rice mapping. The rapid economic development led to an adjustment of people’s diets and presumably this is the main reason why rice cultivation is no longer the main agricultural production activity in the study area.

ACS Style

Jing Liao; Yueming Hu; Hongliang Zhang; Luo Liu; Zhenhua Liu; Zhengxi Tan; Guangxing Wang. A Rice Mapping Method Based on Time-Series Landsat Data for the Extraction of Growth Period Characteristics. Sustainability 2018, 10, 2570 .

AMA Style

Jing Liao, Yueming Hu, Hongliang Zhang, Luo Liu, Zhenhua Liu, Zhengxi Tan, Guangxing Wang. A Rice Mapping Method Based on Time-Series Landsat Data for the Extraction of Growth Period Characteristics. Sustainability. 2018; 10 (7):2570.

Chicago/Turabian Style

Jing Liao; Yueming Hu; Hongliang Zhang; Luo Liu; Zhenhua Liu; Zhengxi Tan; Guangxing Wang. 2018. "A Rice Mapping Method Based on Time-Series Landsat Data for the Extraction of Growth Period Characteristics." Sustainability 10, no. 7: 2570.

Journal article
Published: 15 July 2018 in Sustainability
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Mercury is one of the five most toxic heavy metals to the human body. In order to select a high-precision method for predicting the mercury content in soil using hyperspectral techniques, 75 soil samples were collected in Guangdong Province to obtain the soil mercury content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A multiple linear regression (MLR), a back-propagation neural network (BPNN), and a genetic algorithm optimization of the BPNN (GA-BPNN) were used to establish a relationship between the hyperspectral data and the soil mercury content and to predict the soil mercury content. In addition, the feasibility and modeling effects of the three modeling methods were compared and discussed. The results show that the GA-BPNN provided the best soil mercury prediction model. The modeling R2 is 0.842, the root mean square error (RMSE) is 0.052, and the mean absolute error (MAE) is 0.037; the testing R2 is 0.923, the RMSE is 0.042, and the MAE is 0.033. Thus, the GA-BPNN method is the optimum method to predict soil mercury content and the results provide a scientific basis and technical support for the hyperspectral inversion of the soil mercury content.

ACS Style

Li Zhao; Yue-Ming Hu; Wu Zhou; Zhen-Hua Liu; Yu-Chun Pan; Zhou Shi; Lu Wang; Guang-Xing Wang. Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing. Sustainability 2018, 10, 2474 .

AMA Style

Li Zhao, Yue-Ming Hu, Wu Zhou, Zhen-Hua Liu, Yu-Chun Pan, Zhou Shi, Lu Wang, Guang-Xing Wang. Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing. Sustainability. 2018; 10 (7):2474.

Chicago/Turabian Style

Li Zhao; Yue-Ming Hu; Wu Zhou; Zhen-Hua Liu; Yu-Chun Pan; Zhou Shi; Lu Wang; Guang-Xing Wang. 2018. "Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing." Sustainability 10, no. 7: 2474.

English abstract
Published: 01 February 2010 in Ying yong sheng tai xue bao = The journal of applied ecology
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ACS Style

Jun-Ping Zhang; Yue-Ming Hu; Yuan Tian; Lu Wang; Su-Ping Liu. [Comprehensive evaluation of county-level construction land intensive utility in Guangdong province: a case study for Zijin County]. Ying yong sheng tai xue bao = The journal of applied ecology 2010, 21, 1 .

AMA Style

Jun-Ping Zhang, Yue-Ming Hu, Yuan Tian, Lu Wang, Su-Ping Liu. [Comprehensive evaluation of county-level construction land intensive utility in Guangdong province: a case study for Zijin County]. Ying yong sheng tai xue bao = The journal of applied ecology. 2010; 21 (2):1.

Chicago/Turabian Style

Jun-Ping Zhang; Yue-Ming Hu; Yuan Tian; Lu Wang; Su-Ping Liu. 2010. "[Comprehensive evaluation of county-level construction land intensive utility in Guangdong province: a case study for Zijin County]." Ying yong sheng tai xue bao = The journal of applied ecology 21, no. 2: 1.