Extraction of Paddy Land Area Based on NDVI Time-series Data: Taking Jiangsu Province as an Example

Jiang, Nan; Peng, Suping; Lv, Hongbo; Li, Yang; Zhang, Yu; Wang, Ni; Li, Jun

doi:10.3724/sp.j.1047.2011.00273

Cited by 5 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a long time, the normalized difference vegetation index (NDVI) time series analysis has been widely used in the extraction of rice planting areas [2] . Wang [3] conducted research on identifying cultivation patterns using MODIS-NDVI time series data, while Miao et al [4] monitored rice planting areas using NDVI time series data. Wang et al [5] extracted NDVI, RVI (Ratio Vegetation Index), and NDGI (Normalized Difference Green Index) based on Sentinel-2 time series images to study rice planting identification.…”

Section: Introductionmentioning

confidence: 99%

A method for determining rice planting areas considering the phenological characteristics of multi-phase high-resolution remote sensing images

wang,

Long,

2023

Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023)

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

A method for determining rice planting areas considering the phenological characteristics of multi-phase high-resolution remote sensing images

wang,

Long,

2023

Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023)

View full text Add to dashboard Cite

“…Phenology characteristics are effective parameters for crop classification and can be extracted from time-series images. Based on time-series optical data, previous studies have effectively identified soybeans, rice, corn, and other major crops, and the use of this type of data has improved the efficiency of crop classification [5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data

Sun

Luo

et al. 2019

Sensors

View full text Add to dashboard Cite

Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future.

show abstract

“…Zhang et al (2008) achieved the land cover classification in North China by using the decision tree algorithm based on MODIS EVI time series data. Miao et al (2011) reconstructed the MODIS NDVI time series data by S-G filtering algorithm and completed the extraction of regional rice planting area information. Yang et al (2015) remodeled the GF-1 NDVI time series data by using harmonic algorithm to study the effectiveness of various classification methods for winter wheat-summer corn, corn and rice.…”

Section: Introductionmentioning

confidence: 99%

Identification of Sugarcane with NDVI Time Series Based on HJ-1 CCD and MODIS Fusion

Chen

Feng

Mo³

et al. 2019

J Indian Soc Remote Sens

View full text Add to dashboard Cite

It is currently difficult to acquire the clear-sky data with high spatial resolutions in spring and summer in the southern region of China, making it impossible to carry out timely and fine monitoring of sugarcane planting information. Thus, Fusui, a sugarcane producing county in Guangxi, was selected in this paper to analyze the NDVI characteristics and change rules during the whole growth period of sugarcane based on MODIS and HJ-1 CCD remote sensing data, which were fused into 30 m resolution NDVI time series data with high accuracy by using the spatial and temporal fusion model of multisource remote sensing data ESTRAFM. In addition, the NDVI change rate and sample automatic training threshold were used to construct the sugarcane planting information identification model. The results showed that the fused images showed a high similarity with the observed images, indicating good fusion quality. Moreover, the correlation coefficients in the sugarcane planting area reached 0.953, and AD, AAD and SD were 0.033, 0.019 and 0.007, respectively. The NDVI change rate model was used to identify the sugarcane planting information in different time phases of 113 d, 129 d, 145 d, 193 d and 209 d in spring and summer, and the overall accuracy was 92.17%, 92.58%, 91.78%, 90.52% and 91.17%, respectively. The established model also achieved good results in 2017 with the overall accuracies are 88.44%, 87.79%, 89.79%, 88.34% for 113 d, 145 d, 193 d and 209 d.

show abstract

Extraction of Paddy Land Area Based on NDVI Time-series Data: Taking Jiangsu Province as an Example

Cited by 5 publications

References 7 publications

A method for determining rice planting areas considering the phenological characteristics of multi-phase high-resolution remote sensing images

A method for determining rice planting areas considering the phenological characteristics of multi-phase high-resolution remote sensing images

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data

Identification of Sugarcane with NDVI Time Series Based on HJ-1 CCD and MODIS Fusion

Contact Info

Product

Resources

About