Detection and Modeling of Vegetation Phenology Spatiotemporal Characteristics in the Middle Part of  the Huai River Region in China

Xu, Di; Fu, Meichen

doi:10.3390/su7032841

Cited by 11 publications

(6 citation statements)

References 37 publications

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“…Thus, the accurate acquisition of rice phenological information is an important component of the farming management system [3][4][5]. This type of information provides an accurate knowledge on the status of rice plants, leading to different cultivation practices (e.g., irrigation, fertilization, or harvest) [6][7][8]. Rice phenology is also used as an input in the rice growth and yield prediction models, ecosystem productivity models, and land surface process models [6,9,10].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Yong

et al. 2018

Remote Sensing

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Accurate estimation and monitoring of rice phenology is necessary for the management and yield prediction of rice. The radar backscattering coefficient, one of the most direct and accessible parameters has been proved to be capable of retrieving rice growth parameters. This paper aims to investigate the possibility of monitoring the rice phenology (i.e., transplanting, vegetative, reproductive, and maturity) using the backscattering coefficients or their simple combinations of multi-temporal RADARSAT-2 datasets only. Four RADARSAT-2 datasets were analyzed at 30 sample plots in Meishan City, Sichuan Province, China. By exploiting the relationships of the backscattering coefficients and their combinations versus the phenology of rice, HH/VV, VV/VH, and HH/VH ratios were found to have the greatest potential for phenology monitoring. A decision tree classifier was applied to distinguish the four phenological phases, and the classifier was effective. The validation of the classifier indicated an overall accuracy level of 86.2%. Most of the errors occurred in the vegetative and reproductive phases. The corresponding errors were 21.4% and 16.7%, respectively.

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Section: Introductionmentioning

confidence: 99%

Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Yong

et al. 2018

Remote Sensing

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“…However, modern remote-sensing techniques provide a promising option and new opportunities for phenological studies [11], since it allows the usage of global coverage data at various spatial and temporal scales, making it easy to study phenology and its drivers. Phenological products have been proven to be useful and have been applied in many fields, like biomass monitoring [12,13], farm management [14,15], and climate change [16][17][18][19][20]. In the past two decades, the usage of satellites to determine vegetation phenology has been an active area of research [8].…”

Section: Introductionmentioning

confidence: 99%

Variability of Satellite Derived Phenological Parameters across Maize Producing Areas of South Africa

et al. 2018

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Changes in phenology can be used as a proxy to elucidate the short and long term trends in climate change and variability. Such phenological changes are driven by weather and climate as well as environmental and ecological factors. Climate change affects plant phenology largely during the vegetative and reproductive stages. The focus of this study was to investigate the changes in phenological parameters of maize as well as to assess their causal factors across the selected maize-producing Provinces (viz: North West, Free State, Mpumalanga and KwaZulu-Natal) of South Africa. For this purpose, five phenological parameters i.e., the length of season (LOS), start of season (SOS), end of season (EOS), position of peak value (POP), and position of trough value (POT) derived from the MODIS NDVI data (MOD13Q1) were analysed. In addition, climatic variables (Potential Evapotranspiration (PET), Precipitation (PRE), Maximum (TMX) and Minimum (TMN) Temperatures spanning from 2000 to 2015 were also analysed. Based on the results, the maize-producing Provinces considered exhibit a decreasing trend in NDVI values. The results further show that Mpumalanga and Free State Provinces have SOS and EOS in December and April respectively. In terms of the LOS, KwaZulu-Natal Province had the highest days (194), followed by Mpumalanga with 177 days, while North West and Free State Provinces had 149 and 148 days, respectively. Our results further demonstrate that the influences of climate variables on phenological parameters exhibit a strong space-time and common covariate dependence. For instance, TMN dominated in North West and Free State, PET and TMX are the main dominant factors in KwaZulu-Natal Province whereas PRE highly dominated in Mpumalanga. Furthermore, the result of the Partial Least Square Path Modeling (PLS-PM) analysis indicates that climatic variables predict about 46% of the variability of phenology indicators and about 63% of the variability of yield indicators for the entire study area. The goodness of fit index indicates that the model has a prediction power of 75% over the entire study area. This study contributes towards enhancing the knowledge of the dynamics in the phenological parameters and the results can assist farmers to make the necessary adjustment in order to have an optimal production and thereby enhance food security for both human and livestock.

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“…Different images can be processed separately, allowing variability at seasonal and directional scales to be accounted for [22][23][24][25][26]. Many methods for mapping large areas with a high level of accuracy have been studied [1,27], and one of the most widespread is the Artificial Neural Network (ANN) approach, which can simulate the decision making processes of the human brain.…”

Section: Landsat Data Processingmentioning

confidence: 99%

Land Use Change Impact on Flooding Areas: The Case Study of Cervaro Basin (Italy)

et al. 2016

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Abstract:The main goal of this paper is to study the effect of the spatio-temporal changes of Land Use/Land Cover (LULC) within the hydrologic regime of the Cervaro basin in Southern Italy. LANDSAT Thematic Mapper (TM) imagery acquisition dates from 1984, 2003, 2009, and 2011 were selected to produce LULC maps covering a time trend of 28 years. Nine synthetic bands were processed as input data identified as the most effective for the Artificial Neural Network (ANN) classification procedure implemented in this case study. To assess the possible hydrological effects of the detected changes during rainfall events, a physically-based lumped approach for infiltration contribution was adopted within each sub-basin. The results showed an increase in flood peak and a decrease of the rangelands, forests, and bare lands between 1984 and 2011, indicating a good correlation between flooding areas and land use changes, even if it can be considered negligible in basins of large dimensions. These results showed that the impact of land use on the hydrological response is closely related to watershed scale.

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Detection and Modeling of Vegetation Phenology Spatiotemporal Characteristics in the Middle Part of the Huai River Region in China

Cited by 11 publications

References 37 publications

Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Variability of Satellite Derived Phenological Parameters across Maize Producing Areas of South Africa

Land Use Change Impact on Flooding Areas: The Case Study of Cervaro Basin (Italy)

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