scholarly journals Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

2015 ◽  
Vol 8 (1) ◽  
pp. 10 ◽  
Author(s):  
Binghua Zhang ◽  
Li Zhang ◽  
Dong Xie ◽  
Xiaoli Yin ◽  
Chunjing Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Biomass Estimation ◽  
Modis Data ◽  
Ndvi Time Series

Monitoring of Sugarcane Growth Based on the Fused Remote Sensing NDVI Series and Ground Seeding Survey

2020 ◽  
Vol 63 (6) ◽  
pp. 1795-1804
Author(s):  
Yanli Chen ◽  
Weihua Mo ◽  
Jianfei Mo ◽  
Meihua Ding
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Southern China ◽  
Growth Monitoring ◽  
List Type ◽  
Fusion Model ◽  
Growth Status ◽  
Modis Data ◽  
Timing Data ◽  
Ndvi Time Series

HighlightsThe spatial and temporal fusion model ESTARFM was used to obtain NDVI timing data with high fusion accuracy and high spatial and temporal resolution.High-quality NDVI timing data could be obtained by using ESTARFM to fuse HJ-1 CCD and MODIS data.Fused NDVI data coupled with ground seeding survey data could effectively monitor sugarcane growth status.Abstract. This study addressed the instability of clear-sky remote sensing data with high spatial resolution in sugarcane growing areas in southern China and the current inconsistency between traditional survey results and remote sensing results for seedling growth. Moderate-resolution imaging spectroradiometer (MODIS) data and China land resources satellite (HJ-1 CCD) data were used to build high-resolution normalized difference vegetation index (NDVI) time series using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). Agronomic indicators of sugarcane were obtained by field sampling and were used for determining the remote sensing monitoring index (NDVI) of sugarcane growth. The method provided satisfactory results for evaluating sugarcane growth, with accuracy exceeding 90%. Moreover, sugarcane growth monitoring in a wider area was highly correlated with yield per unit area. Keywords: Growth status, HJ-1 CCD, MODIS, NDVI time series, Spatial and temporal fusion, Sugarcane.

scholarly journals Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data

2017 ◽  
Vol 9 (12) ◽  
pp. 1271 ◽  
Author(s):  
Zhanzhang Cai ◽  
Per Jönsson ◽  
Hongxiao Jin ◽  
Lars Eklundh
Keyword(s):  
Time Series ◽  
Vegetation Phenology ◽  
Modis Data ◽  
Smoothing Methods ◽  
Ndvi Time Series

scholarly journals Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades

2021 ◽  
Author(s):  
Xiaobin Guan ◽  
Huanfeng Shen ◽  
Yuchen Wang ◽  
Dong Chu ◽  
Xinghua Li ◽  
...  
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Spatiotemporal Resolution ◽  
Modis Data ◽  
Ndvi Time Series ◽  
Fusion Framework

Abstract. Satellite normalized difference vegetation index (NDVI) time-series data are an essential data source for numerous ecological and environmental applications. Although various long-term global NDVI products have been produced with different characteristics over the past decades, there is still an apparent trade-off between the spatiotemporal resolution and time coverage. The Advanced Very High-Resolution Radiometer (AVHRR) instrument can provide the only continuous time series with the longest time coverage since the early 1980s, but with the drawback of a coarse spatial resolution and poor data quality compared to the observations of later instruments. To address this issue, a spatio-temporal fusion-based long-term NDVI product (STFLNDVI) since 1982 was generated in this study, with a 1-km spatial resolution and a monthly temporal resolution. A multi-step processing fusion framework was employed to combine the superior characteristics of Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR products, respectively. Simulated and real-data assessments both confirm the ideal accuracy of the fusion result with regard to the spatial distribution and temporal variation. Only a few relatively unsatisfactory results are found due to the poor relationship between the original AVHRR and MODIS data. The evaluations also show that the proposed fusion framework can obtain stable results similar to MODIS data in different years and seasons, even when the temporal distance between the fusion data and the reference data is large. We believe that the STFLNDVI product will be of great significance to characterize the spatial patterns and long-term variations of global vegetation. The NDVI product is available at DOI: http://doi.org/10.5281/zenodo.4734593 (Guan et al., 2021).

scholarly journals Remote Sensing Index for Mapping Canola Flowers Using MODIS Data

2020 ◽  
Vol 12 (23) ◽  
pp. 3912
Author(s):  
Yunze Zang ◽  
Xuehong Chen ◽  
Jin Chen ◽  
Yugang Tian ◽  
Yusheng Shi ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Flowering Period ◽  
Yield Data ◽  
Yellowness Index ◽  
Modis Data ◽  
Ndvi Time Series

Mapping and tracing the changes in canola planting areas and yields in China are of great significance for macro-policy regulation and national food security. The bright yellow flower is a distinctive feature of canola, compared to other crops, and is also an important factor in predicting canola yield. Thus, yellowness indices were previously used to detect the canola flower using aerial imagery or median-resolution satellite data like Sentinel-2. However, it remains challenging to map the canola planting area and to trace long-term canola yields in China due to the wide areal extent of cultivation, different flowering periods in different locations and years, and the lack of high spatial resolution data within a long-term period. In this study, a novel canola index, called the enhanced area yellowness index (EAYI), for mapping canola flowers and based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, was developed. There are two improvements in the EAYI compared with previous studies. First, a method for estimating flowering period, based on geolocation and normalized difference vegetation index (NDVI) time-series, was established, to estimate the flowering period at each place in each year. Second, the EAYI enhances the weak flower signal in coarse pixels by combining the peak of yellowness index time-series and the valley of NDVI time-series during the estimated flowering period. With the proposed EAYI, canola flowering was mapped in five typical canola planting areas in China, during 2003-2017. Three different canola indices proposed previously, the normalized difference yellowness index (NDYI), ratio yellowness index (RYI) and Ashourloo canola index (Ashourloo CI), were also calculated for a comparison. Validation using the samples interpreted through higher resolution images demonstrated that the EAYI is better correlated with the reference canola coverage with R2 ranged from 0.31 to 0.70, compared to the previous indices with R2 ranged from 0.02 to 0.43. Compared with census canola yield data, the total EAYI was well correlated with actual yield in Jingmen, Yili and Hulun Buir, and well correlated with meteorological yields in all five study areas. In contrast, previous canola indices show a very low or even a negative correlation with both actual and meteorological yields. These results indicate that the EAYI is a potential index for mapping and tracing the change in canola areas, or yields, with MODIS data.

scholarly journals Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

Forests ◽  
2016 ◽  
Vol 7 (12) ◽  
pp. 169 ◽  
Author(s):  
Mikhail Urbazaev ◽  
Christian Thiel ◽  
Mirco Migliavacca ◽  
Markus Reichstein ◽  
Pedro Rodriguez-Veiga ◽  
...  
Keyword(s):  
Time Series ◽  
Aboveground Biomass ◽  
Forest Inventory ◽  
Biomass Estimation ◽  
Ndvi Time Series

Analysis on Three NDVI Time-series Reconstruction Methods and Their Applications in North Tibet

2011 ◽  
Vol 13 (1) ◽  
pp. 133-143 ◽  
Author(s):  
Chunqiao SONG ◽  
Songcai YOU ◽  
Linghong KE ◽  
Gaohuan LIU
Keyword(s):  
Time Series ◽  
Reconstruction Methods ◽  
Ndvi Time Series

A clustering method for inter-annual NDVI time series

2021 ◽  
Vol 12 (8) ◽  
pp. 819-826
Author(s):  
Zhen Yang ◽  
Yingying Shen ◽  
Jing Li ◽  
Huawei Jiang ◽  
like Zhao
Keyword(s):  
Time Series ◽  
Clustering Method ◽  
Ndvi Time Series
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