{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T01:10:23Z","timestamp":1733706623848,"version":"3.30.1"},"reference-count":53,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T00:00:00Z","timestamp":1720742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32171777"],"award-info":[{"award-number":["32171777"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Softw Pract Exp"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Net primary productivity (NPP) is essential for sustainable resource management and conservation, and it serves as a primary monitoring target in smart forestry systems. The predominant method for NPP inversion involves data collection through terrestrial and satellite sensing systems, followed by parameter estimation using models such as the Carnegie\u2010Ames\u2010Stanford Approach (CASA). While this method benefits from low costs and extensive monitoring capabilities, the data derived from multisource sensing systems display varied spatial scale characteristics, and the NPP inversion models cannot detect the impact of data heterogeneity on the outcomes sensitively, reducing the accuracy of fine\u2010grained NPP inversion. Therefore, this paper proposes a modular system for fine\u2010grained data processing and NPP inversion. Regarding data processing, a two\u2010stage spatial\u2010spectral fusion model based on non\u2010negative matrix factorization (NMF) is proposed to enhance the spatial resolution of remote sensing data. A spatial interpolation model based on stacking generalization with residual correction is introduced to get raster meteorological data compatible with remote sensing images. Furthermore, we optimize the CASA model with the kernel method to enhance model sensitivity and enrich the spatial details of the inversion results with high resolution. Through validation using real datasets, the proposed fusion and interpolation models have significant advantages over mainstream methods. Furthermore, the correlation coefficient () between the estimated NPP using our improved inversion model and the field\u2010measured NPP is 0.69, demonstrating the feasibility of this platform in detailed forest NPP monitoring tasks.<\/jats:p>","DOI":"10.1002\/spe.3365","type":"journal-article","created":{"date-parts":[[2024,7,13]],"date-time":"2024-07-13T05:36:40Z","timestamp":1720849000000},"page":"79-99","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fine\u2010grained forest net primary productivity monitoring: Software system integrating multisource data and smart optimization"],"prefix":"10.1002","volume":"55","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-2834-4289","authenticated-orcid":false,"given":"Weitao","family":"Zou","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering Northeast Forestry University  Harbin China"},{"name":"Laboratory of Forestry Data Science and Cloud Computing of State Forestry Administration Northeast Forestry University  Harbin China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering Northeast Forestry University  Harbin China"},{"name":"Laboratory of Forestry Data Science and Cloud Computing of State Forestry Administration Northeast Forestry University  Harbin China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangyu","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering Northeast Forestry University  Harbin China"},{"name":"Laboratory of Forestry Data Science and Cloud Computing of State Forestry Administration Northeast Forestry University  Harbin China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering Northeast Forestry University  Harbin China"},{"name":"Laboratory of Forestry Data Science and Cloud Computing of State Forestry Administration Northeast Forestry University  Harbin China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering Northeast Forestry University  Harbin China"},{"name":"Laboratory of Forestry Data Science and Cloud Computing of State Forestry Administration Northeast Forestry University  Harbin China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weipeng","family":"Jing","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering Northeast Forestry University  Harbin China"},{"name":"Laboratory of 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