Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images
نویسندگان
چکیده
Accurate estimation of the leaf or canopy chlorophyll content is crucial for monitoring crop growth conditions. Remote sensing a non-destructive, large-area, and real-time method that requires reliable retrieval models satellite data. High-resolution imagery generally has better object recognition capabilities. However, influence spectral spatial resolution medium- high-spatial-resolution on currently unexplored, especially in conjunction with radiative transfer (RTMs). This important implications accurate quantification over large areas. Therefore, objectives this study were to establish an RTM maize compare capability model using images. We constructed hybrid consisting PROSAIL Gaussian process regression (GPR) algorithm retrieve contents (LCC CCC). In addition, active learning (AL) strategy was incorporated into enhance model’s accuracy efficiency. Sentinel-2 10 m 3 m-resolution Planet utilized LCC CCC retrieval, respectively, model. The verified field-measured data obtained Dajianchang Town, Wuqing District, Tianjin City, 2018. results showed AL increased retrieval. 10-band without had R2 0.567 RMSE 5.598, 0.743 3.964. Incorporating improved performance (R2 = 3.964). provided than 4-band but worse imagery. Additionally, we tested from Youyi Farm Heilongjiang Province 2021 evaluate robustness scalability. test used images achieved good area (LCC: 0.792, 2.8; CCC: 0.726, 0.152). optimal applied distinct periods map spatiotemporal distribution content. uncertainties different relatively low, demonstrating temporal Our research can provide support precise management growth.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2023
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs15071784