Mapping Rice Growth Stages Employing MODIS NDVI and ALOS AVNIR-2

2020 ◽  
pp. 185-203
Author(s):  
Dyah R. Panuju ◽  
David J. Paull ◽  
Amy L. Griffin ◽  
Bambang H. Trisasongko
Keyword(s):  
Growth Stages ◽  
Modis Ndvi ◽  
Rice Growth

scholarly journals Succession of Composition and Function of Soil Bacterial Communities During Key Rice Growth Stages

2019 ◽  
Vol 10 ◽  
Author(s):  
Wenhui Wang ◽  
Xue Luo ◽  
Yang Chen ◽  
Xianfeng Ye ◽  
Hui Wang ◽  
...  
Keyword(s):  
Bacterial Communities ◽  
Growth Stages ◽  
Soil Bacterial Communities ◽  
Rice Growth ◽  
Soil Bacterial ◽  
And Function

scholarly journals The effects of rice growth stages on the ovarian development and take-off ofNilaparvata lugensandSogatella furcifera

2012 ◽  
Vol 32 (5) ◽  
pp. 1546-1552 ◽  
Author(s):  
陈宇 CHEN Yu ◽  
傅强 FU Qiang ◽  
赖凤香 LAI Fengxiang ◽  
罗举 LUO Ju ◽  
张志涛 ZHANG Zhitao ◽  
...  
Keyword(s):  
Ovarian Development ◽  
Growth Stages ◽  
Rice Growth

scholarly journals Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5354
Author(s):  
Chin-Ying Yang ◽  
Ming-Der Yang ◽  
Wei-Cheng Tseng ◽  
Yu-Chun Hsu ◽  
Guan-Sin Li ◽  
...  
Keyword(s):  
Irrigation System ◽  
Irrigation Management ◽  
Growth Period ◽  
Water Shortage ◽  
Water Saving ◽  
Growth Stages ◽  
Rice Growth ◽  
Final Yield ◽  
Uav Images ◽  
Sequential Images

Rice is one of the three major crops in the world and is the major crop in Asia. Climate change and water resource shortages may result in decreases in rice yields and possible food shortage crises. In this study, water-saving farming management was tested, and IOT field water level monitoring was used to regulate water inflow automatically. Plant height (PH) is an important phenotype to be used to determine difference in rice growth periods and yields using water-saving irrigation. An unmanned aerial vehicle (UAV) with an RGB camera captured sequential images of rice fields to estimate rice PH compared with PH measured on site for estimating rice growth stages. The test results, with two crop harvests in 2019, revealed that with adequate image calibration, the correlation coefficient between UAV-PH and field-PH was higher than 0.98, indicating that UAV images can accurately determine rice PH in the field and rice growth phase. The study demonstrated that water-saving farming is effective, decreasing water usage for the first and second crops of 2019 by 53.5% and 21.7%, respectively, without influencing the growth period and final yield. Coupled with an automated irrigation system, rice farming can be adaptive to water shortage situations.

scholarly journals Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1

2020 ◽  
Vol 12 (21) ◽  
pp. 3613 ◽  
Author(s):  
Fadhlullah Ramadhani ◽  
Reddy Pullanagari ◽  
Gabor Kereszturi ◽  
Jonathan Procter
Keyword(s):  
Remote Sensing ◽  
Food Security ◽  
Prediction Models ◽  
Spatial Information ◽  
Rice Production ◽  
Growth Stages ◽  
Time Lags ◽  
Cropping Pattern ◽  
Rice Growth ◽  
Sentinel 2

Rice (Oryza sativa L.) is a staple food crop for more than half of the world’s population. Rice production is facing a myriad of problems, including water shortage, climate, and land-use change. Accurate maps of rice growth stages are critical for monitoring rice production and assessing its impacts on national and global food security. Rice growth stages are typically monitored by coarse-resolution satellite imagery. However, it is difficult to accurately map due to the occurrence of mixed pixels in fragmented and patchy rice fields, as well as cloud cover, particularly in tropical countries. To solve these problems, we developed an automated mapping workflow to produce near real-time multi-temporal maps of rice growth stages at a 10-m spatial resolution using multisource remote sensing data (Sentinel-2, MOD13Q1, and Sentinel-1). This study was investigated between 1 June and 29 September 2018 in two (wet and dry) areas of Java Island in Indonesia. First, we built prediction models based on Sentinel-2, and fusion of MOD13Q1/Sentinel-1 using the ground truth information. Second, we applied the prediction models on all images in area and time and separation between the non-rice planting class and rice planting class over the cropping pattern. Moreover, the model’s consistency on the multitemporal map with a 5–30-day lag was investigated. The result indicates that the Sentinel-2 based model classification gives a high overall accuracy of 90.6% and the fusion model MOD13Q1/Sentinel-1 shows 78.3%. The performance of multitemporal maps was consistent between time lags with an accuracy of 83.27–90.39% for Sentinel-2 and 84.15% for the integration of Sentinel-2/MOD13Q1/Sentinel-1. The results from this study show that it is possible to integrate multisource remote sensing for regular monitoring of rice phenology, thereby generating spatial information to support local-, national-, and regional-scale food security applications.

scholarly journals Abundance of arthropod insect pests and natural enemies in rice field as influenced by rice growth stages and neighboring crops

2017 ◽  
Vol 42 (2) ◽  
pp. 309-319
Author(s):  
P Mukherjee ◽  
MMH Khan
Keyword(s):  
Natural Enemies ◽  
Insect Pests ◽  
Ground Beetle ◽  
Stem Borer ◽  
Seedling Stage ◽  
Growth Stages ◽  
Stink Bug ◽  
Rice Growth ◽  
Rice Bug ◽  
Tree Habitat

Studies were conducted to record the abundance of arthropod insect pests and natural enemies in rice fields as influenced by rice growth stages and neighboring crops at the experimental farm of Patuakhali Science and Technology University (PSTU), Dumki, Patuakhali during 2012 in Boro rice season following randomized complete block design. Results indicated that rice-tree habitat showed the highest abundance of leafhoppers (100.75) followed by cricket (16.50), grasshoppers (15.25) and stink bugs (15.25). The lowest abundance of all insect pests was in rice-sesame habitat. No significant differences were found on the abundance of rice bug, rice hispa and stem borer populations. At seedling stage, the highest abundance of leafhopper (94.25) was recorded followed by grasshopper (47.00) and stink bug (26.50) while the lowest was stem borer (0.57) and rice hispa (6.00). At early tillering stage, maximum number of grasshopper (17.25) was recorded followed by cricket (7.00). At maximum tillering stage, the highest abundance of leafhoppers (122.5) was obtained followed by rice bug (62.00) and the lowest was the stink bug (7.00). At panical initiation stage, the highest abundance of rice bug (334.00) was recorded which was followed by leafhoppers (65.25) and the lowest was the cricket (15.75). No population of rice hispa and stem borer was recorded at maximum tillering and panical initiation stages. In case of natural enemies, the highest abundance of lady bird beetle (45.27) and damselfly (16.73) was found in rice-rice habitat. The highest abundance of ichneumonid wasp (57.53) was in rice-tree habitat and ground beetle (28.80) was in rice-sesame habitat. No significant differences were observed on the abundance of dragonfly, spider and dipteran fly among different habitats. Among different growth stages of rice plant, significantly the highest abundance of lady bird beetle was recorded at maximum tillering stage. The highest abundance of ichneumonid wasp and ground beetle was recorded at seedling stage. The highest abundance of damselfly, spider and dipteran fly was at early tillering stage. No significant difference was observed on the abundance of dragonfly among different rice growth stages.Bangladesh J. Agril. Res. 42(2): 309-319, June 2017

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