Feature-based algorithm for large-scale rice phenology detection based on satellite images

Zhao，Xin1; Nishina，Kazuya1; Akitsu，Tomoko Kawaguchi2; Jiang，Liguang3; Masutomi，Yuji4; Nasahara，Kenlo Nishida5

2023-02-15

10.1016/j.agrformet.2022.109283

AGRICULTURAL AND FOREST METEOROLOGY   影响因子和分区

0168-1923

1873-2240

Knowledge of rice phenology is essential for understanding the agricultural practices and studying its impact on ecosystem services. However, so far, available global-scale rice phenology maps do not provide fine spatiotemporal details of rice phenology in a consistent framework because they rely on the compilation of statistical data. Thus, this paper proposes an algorithm that combines the complementary advantages of Sentinel-1 and Sentinel-2 satellite images to produce large-scale maps that depict rice phenology dynamics. The novelty of this algorithm lies in the correlation with rice phenology features, i.e., rice in water condition and rice color change. The time series of backscattering at Vertical-Horizontal (VH) polarization and Enhanced Vegetation Index (EVI) are proposed to recognize rice planting and heading dates, respectively. For the same time, the Normalized Difference Yellow Index (NDYI) is utilized to detect the rice harvest date for the first time. The proposed algorithm is applied to multiple spatial scales (prefecture, 0.5° gridcell, and site scales) and to multiple rice cropping systems (single, double, and triple croppings) in monsoon Asia. Results reveal that the algorithm is able to accurately detect the rice planting and harvest dates across two rice paddy field distribution maps with moderate-to-high spatial resolution, different validation data, and different rice cropping systems. The bias values of detected planting dates are 2, 0, and 4 days, while that of harvest dates are -2, -5, and -13 days at the prefecture, 0.5° gridcell, and site scales, respectively. These results highlight the potential of this algorithm to generate national, continental, or even global maps of rice phenology dynamics in an efficient manner, which can facilitate research on the impact of rice phenology on rice ecosystem services that echoes environmental and climate change.

Enhanced Vegetation Index (EVI) Google Earth Engine (GEE) Normalized Difference Yellow Index (NDYI) Rice phenology Sentinel-1 & -2 Vertical-Horizontal (VH) polarization backscattering

SCI

英语

其他

New Energy and Industrial Technology Development Organization (NEDO)[JPNP18016]

Agriculture ; Forestry ; Meteorology & Atmospheric Sciences

Agronomy ; Forestry ; Meteorology & Atmospheric Sciences

WOS:000906189800001

ELSEVIER

AGRICULTURAL SCIENCES

2-s2.0-85144291716

Scopus

1.Biogeochemical Cycle Modeling and Analysis Section,Earth System Division,National Institute for Environmental Studies,Tsukuba,16-2 Onogawa, Ibaraki,305-8506,Japan
2.Earth Observation Research Center,Japan Aerospace Exploration Agency,Tsukuba,2-1-1 Sengen, Ibaraki,305-8505,Japan
3.School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
4.Asia-Pacific Climate Change Adaptation Research Section,Center for Climate Change Adaptation,National Institute for Environmental Studies,Tsukuba,16-2 Onogawa, Ibaraki,305-8506,Japan
5.Faculty of Life and Environmental Sciences,University of Tsukuba,Tsukuba,1-1-1 Tennodai, Ibaraki,305-8572,Japan

Zhao，Xin,Nishina，Kazuya,Akitsu，Tomoko Kawaguchi,et al. Feature-based algorithm for large-scale rice phenology detection based on satellite images[J]. AGRICULTURAL AND FOREST METEOROLOGY,2023,329.

Zhao，Xin,Nishina，Kazuya,Akitsu，Tomoko Kawaguchi,Jiang，Liguang,Masutomi，Yuji,&Nasahara，Kenlo Nishida.(2023).Feature-based algorithm for large-scale rice phenology detection based on satellite images.AGRICULTURAL AND FOREST METEOROLOGY,329.

Zhao，Xin,et al."Feature-based algorithm for large-scale rice phenology detection based on satellite images".AGRICULTURAL AND FOREST METEOROLOGY 329(2023).