Estimation of methane emissions based on crop yield and remote sensing data in a paddy field

2020 ◽  
Vol 10 (1) ◽  
pp. 196-207 ◽  
Author(s):  
Yifan Shi ◽  
Yunsheng Lou ◽  
Zhen Zhang ◽  
Li Ma ◽  
Moses A Ojara
2010 ◽  
Vol 15 (2) ◽  
pp. 221-224 ◽  
Author(s):  
Takashi Yamaguchi ◽  
Kazuya Kishida ◽  
Eiji Nunohiro ◽  
Jong Geol Park ◽  
Kenneth J. Mackin ◽  
...  

2012 ◽  
Vol 16 (4) ◽  
pp. 497-501
Author(s):  
Kazuma Mori ◽  
Takashi Yamaguchi ◽  
Jong Geol Park ◽  
Kenneth J. Mackin

2021 ◽  
Vol 13 (6) ◽  
pp. 1094
Author(s):  
Xingshuo Peng ◽  
Wenting Han ◽  
Jianyi Ao ◽  
Yi Wang

In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated.


2021 ◽  
Vol 895 (1) ◽  
pp. 012007
Author(s):  
K Yu Bazarov ◽  
E G Egidarev ◽  
N V Mishina

Abstract The paper presents results of the analysis of the land use map compiled for transboundary Lake Khanka Basin using remote sensing data and geoinformation systems. The map reflects the distribution of 12 land categories in Lake Khanka basin in 2017 (arable land, abandoned arable land, paddy field, abandoned paddy field, shrubs and sparse growth, forest land, open pit, settlements, meadows and pastures, wet meadows and marshes, water bodies, forest cuttings and fire sites). The data of land use structure in the whole Lake’s watershed, in its Russian and Chinese parts are given. Data on the distribution of different land categories in the administrative territories of the rank of districts (Russia) and counties (China) are also presented. The analysis of land use structure showed that about 50 % of the Chinese part of the basin is covered by anthropogenically transformed natural complexes. The share of such lands in the territory of Russia amounts to 28 %. Agriculture is the most important factor in the change of natural complexes in Lake Khanka basin. Before early 1990s, the area of farmland had increased in the basin on both sides of the border, after that there was a significant reduction in cultivated lands, which had lasted for 10 years in the territory of China and for 20 years in Russia. Over the past decade, the area of cultivated areas in the basin and adjacent territories has extended again, which indicates an increase of anthropogenic impact and requires serious attention to monitoring of the ecological state of lands in the basin.


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