| Collaborative estimation of aboveground biomass in grassland based on multiple vegetation index models |
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| DOI：10.7606/j.issn.1000-7601.2022.04.23 |
| Key Words: aboveground biomass vegetation index sensitivity analysis Sentinel-2 image collaborative estimation |
| Author Name | Affiliation | | GUO Chaofan | School of commerce, Quzhou University, Quzhou, Zhejiang 324000, China
College of Resources Environment and Tourism, Anyang Normal University, Anyang, Henan 455000，China | | CHEN Wenjing | College of Resources Environment and Tourism, Anyang Normal University, Anyang, Henan 455000，China | | NIU Mingyan | College of Resources Environment and Tourism, Anyang Normal University, Anyang, Henan 455000，China | | ZHANG Zhigao | College of Resources Environment and Tourism, Anyang Normal University, Anyang, Henan 455000，China |
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| Abstract: |
| This study selected Jinyintan grassland in Qinghai Province as the study area. The study used Sentinel-2 satellite images combining with ground measurement data to estimate the aboveground biomass of grassland. Specifically, the study analyzed the fitting relationship between 18 typical vegetation indices and biomass, determined the applicability of different vegetation index models through accuracy evaluation and sensitivity analysis, and then proposed a collaborative estimation scheme based on multiple vegetation index models to improve the accuracy of grassland biomass mapping. The results showed that the optimal fitting models of 18 indices presented two trends of power function relation and exponential function relation. The CIgreen (Green chlorophyll index) had the best estimation accuracy among power function models, which was the most applicable when the biomass was higher than 0.65 kg·m-2. The NDII (Normalized differential infrared index) showed the best verification accuracy in exponential function models and had the strongest applicability when the biomass was lower than 0.65 kg·m-2. Moreover, the NDII and CIgreen models had complementary applicability ranges. The R2cv and RMSEcv of the proposed method reached 0.61 and 0.226 kg·m-2, respectively. Compared with the single index models, the R2cv increased about 7.0% and the RMSEcv decreased around 3.8%, which significantly improved in precision. In summary, the proposed collaborative estimation scheme based on multiple vegetation index models fully considered the applicability scope of different index models and improved the accuracy of forage biomass estimation. |
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