{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T07:25:26Z","timestamp":1778397926646,"version":"3.51.4"},"reference-count":65,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Scientific and Technological Projects of Heilongjiang Province","award":["2021ZXJ05A05"],"award-info":[{"award-number":["2021ZXJ05A05"]}]},{"name":"Key Scientific and Technological Projects of Heilongjiang Province","award":["41601346"],"award-info":[{"award-number":["41601346"]}]},{"name":"Key Scientific and Technological Projects of Heilongjiang Province","award":["PT2022-24"],"award-info":[{"award-number":["PT2022-24"]}]},{"name":"Key Scientific and Technological Projects of Heilongjiang Province","award":["2019B020216001"],"award-info":[{"award-number":["2019B020216001"]}]},{"name":"National Natural Science Foundation of China","award":["2021ZXJ05A05"],"award-info":[{"award-number":["2021ZXJ05A05"]}]},{"name":"National Natural Science Foundation of China","award":["41601346"],"award-info":[{"award-number":["41601346"]}]},{"name":"National Natural Science Foundation of China","award":["PT2022-24"],"award-info":[{"award-number":["PT2022-24"]}]},{"name":"National Natural Science Foundation of China","award":["2019B020216001"],"award-info":[{"award-number":["2019B020216001"]}]},{"name":"Platform Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences","award":["2021ZXJ05A05"],"award-info":[{"award-number":["2021ZXJ05A05"]}]},{"name":"Platform Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences","award":["41601346"],"award-info":[{"award-number":["41601346"]}]},{"name":"Platform Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences","award":["PT2022-24"],"award-info":[{"award-number":["PT2022-24"]}]},{"name":"Platform Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences","award":["2019B020216001"],"award-info":[{"award-number":["2019B020216001"]}]},{"name":"Key Field Research and Development Program of Guangdong Province","award":["2021ZXJ05A05"],"award-info":[{"award-number":["2021ZXJ05A05"]}]},{"name":"Key Field Research and Development Program of Guangdong Province","award":["41601346"],"award-info":[{"award-number":["41601346"]}]},{"name":"Key Field Research and Development Program of Guangdong Province","award":["PT2022-24"],"award-info":[{"award-number":["PT2022-24"]}]},{"name":"Key Field Research and Development Program of Guangdong Province","award":["2019B020216001"],"award-info":[{"award-number":["2019B020216001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aboveground biomass (AGB) is an important indicator for crop-growth monitoring and yield prediction, and accurate monitoring of AGB is beneficial to agricultural fertilization management and optimization of planting patterns. Imaging spectrometer sensors mounted on unmanned aerial vehicle (UAV) remote-sensing platforms have become an important technical method for monitoring AGB because the method is convenient, rapidly collects data and provides image data with high spatial and spectral resolution. To confirm the feasibility of UAV hyperspectral remote-sensing technology to estimate AGB, this study acquired hyperspectral images and measured AGB data over the potato bud, tuber formation, tuber growth, and starch-storage periods. The canopy spectrum obtained in each growth period was smoothed by using the Savitzky\u2013Golay filtering method, and the spectral-reflection feature parameters, spectral-location feature parameters, and vegetation indexes were extracted. First, a Pearson correlation analysis was performed between the three types of characteristic spectral parameters and AGB, and the spectral parameters that reached a significant level of 0.01 in each growth period were selected. Next, the spectral parameters reaching a significance of 0.01 were optimized and screened by moving window partial least squares (MWPLS), Monte Carlo uninformative variable elimination (MC-UVE), and random frog (RF) methods, and the final model parameters were determined according to the thresholds of the root mean square error of cross-validation (RMSEcv), the reliability index, and the selected probability. Finally, the three optimal characteristic spectral parameters and their combinations were used to estimate the potato AGB in each growth period by combining the partial least squares regression (PLSR) and Gaussian process regression (GPR) methods. The results show that, (i) ranked from high to low, vegetation indexes, spectral-location feature parameters, and spectral-reflection feature parameters in each growth period are correlated with the AGB, and these correlations all first improve and then degrade in going from the budding period to the starch-storage period. (ii) The AGB estimation model based on the characteristic variables screened by the three methods in each growth period is most accurate with RF, less so with MC-UVE, and least accurate with MWPLS. (iii) Estimating the AGB with the same variables combined with the PLSR method in each growth period is more accurate than the corresponding GPR method, but the estimations produced by the two methods both show a trend of first improving and then worsening from the budding period to the starch-accumulation period. The accuracy of the estimation models constructed by PLSR and GPR from high to low is based on comprehensive variables, vegetation indexes, spectral-location feature parameters and spectral-reflection feature parameters. (iv) When combined with the RF-PLSR method to estimate AGB in each growth period, the best R2 values are 0.65, 0.68, 0.72, and 0.67, the corresponding RMSE values are 167.76, 162.98, 160.77, and 169.24 kg\/hm2, and the corresponding NRMSE values are 19.76%, 16.01%, 15.04%, and 16.84%. The results of this study show that a variety of characteristic spectral parameters may be extracted from UAV hyperspectral images, that the RF method may be used for optimizing and screening, and that PLSR regression provides accurate estimates of the potato AGB. The proposed approach thus provides a rapid, accurate, and nondestructive way to monitor the growth status of potatoes.<\/jats:p>","DOI":"10.3390\/rs14205121","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T01:44:13Z","timestamp":1665711853000},"page":"5121","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Estimation of Aboveground Biomass of Potatoes Based on Characteristic Variables Extracted from UAV Hyperspectral Imagery"],"prefix":"10.3390","volume":"14","author":[{"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"Key Laboratory of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3312-6200","authenticated-orcid":false,"given":"Haikuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jibo","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9878-3274","authenticated-orcid":false,"given":"Zhenhai","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6769-214X","authenticated-orcid":false,"given":"Xiuliang","family":"Jin","sequence":"additional","affiliation":[{"name":"Institute of Crop Sciences, Chinese Academy of Agricultural Sciences\/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiguang","family":"Fan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihang","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Banerjee, B.P., Spangenberg, G., and Kant, S. 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