乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T01:06:51Z","timestamp":1728176811214},"reference-count":95,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T00:00:00Z","timestamp":1611532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass expressed as compressed sward height (CSH) from readily accessible meteorological, optical (Sentinel-2) and radar satellite data (Sentinel-1). This study assumed that combining heterogeneous data sources, data transformations and machine learning methods would improve the robustness and the accuracy of the developed models. A total of 72,795 records of CSH with a spatial positioning, collected in 2018 and 2019, were used and aggregated according to a pixel-like pattern. The resulting dataset was split into a training one with 11,625 pixellated records and an independent validation one with 4952 pixellated records. The models were trained with a 19-fold cross-validation. A wide range of performances was observed (with mean root mean square error (RMSE) of cross-validation ranging from 22.84 mm of CSH to infinite-like values), and the four best-performing models were a cubist, a glmnet, a neural network and a random forest. These models had an RMSE of independent validation lower than 20 mm of CSH at the pixel-level. To simulate the behavior of the model in a decision support system, performances at the paddock level were also studied. These were computed according to two scenarios: either the predictions were made at a sub-parcel level and then aggregated, or the data were aggregated at the parcel level and the predictions were made for these aggregated data. The results obtained in this study were more accurate than those found in the literature concerning pasture budgeting and grassland biomass evaluation. The training of the 124 models resulting from the described framework was part of the realization of a decision support system to help farmers in their daily decision making.<\/jats:p>","DOI":"10.3390\/rs13030408","type":"journal-article","created":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T14:59:40Z","timestamp":1611586780000},"page":"408","source":"Crossref","is-referenced-by-count":12,"title":["Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations"],"prefix":"10.3390","volume":"13","author":[{"given":"Charles","family":"Nickmilder","sequence":"first","affiliation":[{"name":"TERRA Research Centre Passage des D\u00e9port\u00e9s 2, 5030 Gembloux, Belgium"}]},{"given":"Anthony","family":"Tedde","sequence":"additional","affiliation":[{"name":"TERRA Research Centre Passage des D\u00e9port\u00e9s 2, 5030 Gembloux, Belgium"}]},{"given":"Isabelle","family":"Dufrasne","sequence":"additional","affiliation":[{"name":"Centre des Technologies Agronomiques, rue de la Charmille, 16, 4577 Str\u00e9e-Modave, Belgium"},{"name":"FARAH Center-D\u00e9partement de Gestion V\u00e9t\u00e9rinaire des Ressources Animales (DRA), Nutrition des Animaux Domestiques B\u00e2t. B43 Nutrition des Animaux Domestiques, Quartier Vall\u00e9e 2, Avenue de Cureghem 6, 4000 Li\u00e8ge, Belgium"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4062-6927","authenticated-orcid":false,"given":"Fran\u00e7oise","family":"Lessire","sequence":"additional","affiliation":[{"name":"FARAH Center-D\u00e9partement de Gestion V\u00e9t\u00e9rinaire des Ressources Animales (DRA), Nutrition des Animaux Domestiques B\u00e2t. B43 Nutrition des Animaux Domestiques, Quartier Vall\u00e9e 2, Avenue de Cureghem 6, 4000 Li\u00e8ge, Belgium"}]},{"given":"Bernard","family":"Tychon","sequence":"additional","affiliation":[{"name":"D\u00e9partement des Sciences et Gestion de L\u2019environnement (Arlon Campus Environnement), Eau, Environnement, D\u00e9veloppement Sph\u00e8res B\u00e2t. BE-009 Eau, Environnement, D\u00e9veloppement, Avenue de Longwy 185, 6700 Arlon, Belgium"}]},{"given":"Yannick","family":"Curnel","sequence":"additional","affiliation":[{"name":"Centre Wallon de Recherches Agronomiques (CRA-W), D\u00e9partement \u2018Productions Agricoles\u2019 (D2), Unit\u00e9 \u2018Agriculture, Territoire et Int\u00e9gration Technologique\u2019 (U6) Rue de Liroux, 9, 5030 Gembloux, Belgium"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6974-4313","authenticated-orcid":false,"given":"J\u00e9rome","family":"Bindelle","sequence":"additional","affiliation":[{"name":"TERRA Research Centre Passage des D\u00e9port\u00e9s 2, 5030 Gembloux, Belgium"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9883-9047","authenticated-orcid":false,"given":"H\u00e9l\u00e8ne","family":"Soyeurt","sequence":"additional","affiliation":[{"name":"TERRA Research Centre Passage des D\u00e9port\u00e9s 2, 5030 Gembloux, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hennessy, D., Delaby, L., van den Pol-van Dasselaar, A., and Shalloo, L. 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