{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:24:04Z","timestamp":1763202244984,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T00:00:00Z","timestamp":1560988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671333"],"award-info":[{"award-number":["41671333"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Net primary productivity (NPP) is a key vegetation parameter and ecological indicator for tracking natural environmental change. High-quality Moderate Resolution Imaging Spectroradiometer Net primary productivity (MODIS-NPP) products are critical for assuring the scientific rigor of NPP analyses. However, obtaining high-quality MODIS-NPP products consistently is challenged by factors such as cloud contamination, heavy aerosol pollution, and atmospheric variability. This paper proposes a method combining the discrete wavelet transform (DWT) with an extended Kalman filter (EKF) for generating high-quality MODIS-NPP data. In this method, the DWT is used to remove noise in the original MODIS-NPP data, and the EKF is applied to the de-noised images. The de-noised images are modeled as a triply modulated cosine function that predicts the NPP data values when excessive cloudiness is present. This study was conducted in South China. By comparing measured NPP data to original MODIS-NPP and NPP estimates derived from combining the DWT and EKF, we found that the accuracy of the NPP estimates was significantly improved. The MODIS-NPP estimates had a mean relative error (RE) of 13.96% and relative root mean square error (rRMSE) of 15.67%, while the original MODIS-NPP had a mean RE of 23.58% and an rRMSE of 24.98%. The method combining DWT and EKF provides a feasible approach for generating new, high-quality NPP data in the absence of high-quality original MODIS-NPP data.<\/jats:p>","DOI":"10.3390\/rs11121458","type":"journal-article","created":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T10:49:59Z","timestamp":1561027799000},"page":"1458","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Prediction of High-Quality MODIS-NPP Product Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Zhenhua","family":"Liu","sequence":"first","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5940-5764","authenticated-orcid":false,"given":"Yonghua","family":"Qu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications of CAS, Beijing 100875, China"},{"name":"Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China"},{"name":"School of Geography, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiming","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/S0378-1127(02)00312-2","article-title":"Net primary productivity in forests of China: Scaling-up of national inventory data and comparison with model predictions","volume":"176","author":"Ni","year":"2003","journal-title":"For. 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