Set data ini menyediakan Produktivitas Primer Kotor (GPP) berbasis EO yang tidak dikalibrasi secara global dari tahun 2000 pada resolusi spasial 30 m.
Set data saat ini, yang dihasilkan oleh inisiatif Land & Carbon Lab Global Pasture Watch, memberikan nilai Produktivitas Primer Kotor (GPP) secara global pada resolusi spasial 30 m mulai tahun 2000 dan seterusnya.
Nilai GPP dimodelkan melalui pendekatan efisiensi penggunaan cahaya (LUE),
dengan GLAD Landsat ARD (collection-2) digabungkan setiap dua bulan
(Consoli et al., 2024) dan digabungkan dengan data suhu MODIS
1 km dan Radiasi Aktif Fotosintetik CERES (PAR) 1°.
Untuk menjaga fleksibilitas set data, efisiensi penggunaan cahaya maksimum (LUEmax) ditetapkan ke 1 gC/m²/hari/MJ
untuk semua jenis tutupan lahan, sehingga pengguna dapat mengalibrasi nilai
GPP nanti sesuai dengan peta tutupan lahan tertentu atau kondisi regional.
Nilai Produktivitas Primer Bruto (GPP) yang tidak dikalibrasi dua bulanan (tersedia di STAC OpenLandMap) dirata-ratakan setiap tahun dan diakumulasikan selama periode 365 hari penuh untuk menghasilkan nilai uGPP tahunan global, yang dinyatakan dalam satuan gC/m²/tahun.
Nilai GPP Padang Rumput dihitung secara langsung menggunakan Aplikasi GEE.
Batasan:
Ketidakcocokan resolusi data input: Set data disediakan pada resolusi 30 m, tetapi variabel input utama untuk suhu (MOD11A1)
dan radiasi aktif fotosintetik (CERES PAR) berasal dari produk yang jauh lebih kasar (1 km dan ~111 km, masing-masing).
Pengecilan skala informasi ini dapat menimbulkan ketidakpastian dan mungkin tidak mencakup kondisi iklim mikro skala kecil yang memengaruhi produktivitas tanaman.
Artefak data: Set data berisi artefak visual yang diketahui, termasuk garis vertikal ("efek garis") di beberapa area, yang merupakan akibat
dari masalah pada sensor Landsat 7 (kegagalan Scan Line Corrector) dan proses pengisian kesenjangan berikutnya yang digunakan untuk membuat arsip reflektansi yang mendasarinya (Consoli et al., 2024). Artefak ini dapat mengganggu kontinuitas spasial perkiraan GPP selama periode berawan dan tertutup salju
Resolusi temporal: Data dihasilkan pada resolusi temporal dua bulanan. Jangka waktu ini mungkin tidak cukup
untuk mencatat periode pertumbuhan utama atau respons cepat tanaman (curah hujan tinggi) terhadap perubahan lingkungan, sehingga
sulit untuk mencatat puncak produktivitas dan variasi musiman secara akurat.
Kalibrasi padang rumput: Nilai GPP padang rumput dihitung menggunakan satu parameter efisiensi penggunaan cahaya maksimum (LUEmax) (0,86 gC/m²/tahun/MJ) untuk semua padang rumput global, berdasarkan algoritma MOD17. Nilai ini tidak dioptimalkan
untuk jenis padang rumput tertentu atau kondisi lokal. Akibatnya, model menunjukkan kecenderungan untuk meremehkan GPP jika dibandingkan dengan pengukuran menara fluks berbasis darat.
Ketergantungan pada akurasi peta padang rumput: Akurasi nilai GPP padang rumput bergantung pada akurasi peta padang rumput GPW yang mendasarinya.
Setiap kesalahan klasifikasi tutupan lahan dalam peta sumber (misalnya, semak belukar atau lahan pertanian yang diidentifikasi sebagai padang rumput) akan menyebabkan kesalahan yang sesuai dalam perkiraan GPP untuk lokasi tersebut.
Isik, M. S., Mesquita, V., Parente, L., & Consoli, D. (2025).
Global Pasture Watch - Kode Sumber GPP berbasis EO Global yang Tidak Dikalibrasi dan Peta GPP Padang Rumput Global pada 30 m. Zenodo.
[Kode sumber]. Zenodo
doi:https://doi.org/10.5281/zenodo.15675358
Isik MS, Parente L, Consoli D, et al. (2025).
Efisiensi penggunaan cahaya (LUE) berdasarkan produktivitas primer kotor (GPP) dua bulanan untuk padang rumput global pada resolusi spasial 30 m (2000–2022), PeerJ.
doi: https://doi.org/10.7717/peerj.19774
Set data ini menyediakan Produktivitas Primer Kotor berbasis EO yang tidak dikalibrasi secara global dari tahun 2000 pada resolusi spasial 30 m. Set data saat ini, yang dihasilkan oleh inisiatif Land & Carbon Lab Global Pasture Watch, memberikan nilai Produktivitas Primer Kotor (GPP) secara global pada resolusi spasial 30 m mulai tahun 2000 dan seterusnya. Nilai GPP dimodelkan melalui penggunaan ringan …
[null,null,[],[],[],null,["# GPW Annual uncalibrated Gross Primary Productivity (uGPP) v1\n\ninfo\n\n\nThis dataset is part of a Publisher Catalog, and not managed by Google Earth Engine.\n\nContact [Land \\& Carbon Lab](https://landcarbonlab.org/subscribe)\n\nfor bugs or [view more datasets](https://developers.google.com/earth-engine/datasets/publisher/global-pasture-watch)\nfrom the Global Pasture Watch Catalog. [Learn more about Publisher datasets](/earth-engine/datasets/publisher). \n[](https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring) \n\nCatalog Owner\n: Global Pasture Watch\n\nDataset Availability\n: 2000-01-01T00:00:00Z--2024-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [Land and Carbon Lab Global Pasture Watch](https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring)\n\nContact\n: [Land \\& Carbon Lab](https://landcarbonlab.org/subscribe)\n\nCadence\n: 1 Year\n\nTags\n:\n[global](/earth-engine/datasets/tags/global) [global-pasture-watch](/earth-engine/datasets/tags/global-pasture-watch) [land](/earth-engine/datasets/tags/land) [landcover](/earth-engine/datasets/tags/landcover) [landuse](/earth-engine/datasets/tags/landuse) [plant-productivity](/earth-engine/datasets/tags/plant-productivity) [publisher-dataset](/earth-engine/datasets/tags/publisher-dataset) [vegetation](/earth-engine/datasets/tags/vegetation) \n\n#### Description\n\nThis dataset provides global uncalibrated EO-based Gross Primary Productivity\nfrom 2000 at 30-m spatial resolution.\nProduced by Land \\& Carbon Lab Global Pasture Watch initiative, the current dataset provides\nGross Primary Productivity (GPP) values globally at 30-m spatial resolution from 2000 onwards.\nGPP values are modeled via a **light use efficiency (LUE)** approach,\nwhere [**GLAD Landsat ARD** (collection-2)](https://glad.umd.edu/ard/home) are aggregated every two months\n([Consoli et al., 2024](https://peerj.com/articles/18585/)) and combined with 1-km **MODIS\ntemperature** data and 1° **CERES Photosynthetically Active Radiation** (PAR).\n\nTo keep the dataset flexible, the maximum light use efficiency (LUEmax) is set to 1 gC/m²/day/MJ\nfor **all land cover types**, allowing the users to later calibrate the\nGPP values according to specific land cover maps or regional conditions.\n\n**Bi-monthly uncalibrated Gross Primary Productivity (uGPP)** values (available in [OpenLandMap STAC](https://stac.openlandmap.org/gpw_ugpp.daily-30m/collection.json)) are averaged by each year and accumulated over the full 365-day period to produce\nglobal annual uGPP values, expressed in units of gC/m²/year.\n\n**Grassland GPP** values are computed on-the-fly using [GEE App](https://global-pasture-watch.projects.earthengine.app/view/ggpp-30m).\n\n**Limitations:**\n\n- **Input data resolution mismatch**: The dataset is provided at 30 m resolution, but key input variables for temperature (MOD11A1)\n and photosynthetically active radiation (CERES PAR) were derived from much coarser products (1 km and \\~111 km, respectively).\n The downscaling of this information can introduce uncertainty and may not capture fine-scale microclimatic conditions affecting plant productivity.\n\n- **Data artifacts** : The dataset contains known visual artifacts, including vertical stripes (\"stripe effect\") in some areas, which are a result\n of issues with the Landsat 7 sensor (Scan Line Corrector failure) and the subsequent gap-filling process used to create the underlying\n reflectance archive ([Consoli et al., 2024](https://peerj.com/articles/18585/)). These artifacts can disrupt the spatial continuity\n of GPP estimates during cloudy and snow cover periods\n\n- **Temporal resolution**: The data is produced at a bimonthly temporal resolution. This timeframe may not be sufficient\n to capture key growth periods or a plant's rapid responses (intense rainfall) to environmental changes, making\n it difficult to accurately capture productivity peaks and seasonal variation.\n\n- **Grassland calibration**: Grassland GPP values are calculated using a single maximum light use efficiency (LUEmax)\n parameter (0.86 gC/m²/year/MJ) for all global grasslands, based on the MOD17 algorithm. This value is not optimized\n for specific grassland types or local conditions. As a result, the model shows a tendency to underestimate GPP when\n compared to ground-based flux tower measurements.\n\n- **Dependence on grassland maps accuracy** : The accuracy of the grassland GPP values is contingent on the accuracy of the\n underlying [GPW grassland maps](https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_grassland_c).\n Any misclassification of land cover in the source maps (e.g., shrublands or croplands identified as grassland) will\n lead to corresponding errors in the GPP estimates for those locations.\n\n**For more information see [Isik et. al, 2025](https://doi.org/10.7717/peerj.19774),\n[Zenodo](https://doi.org/10.5281/zenodo.15675358) and\n[Global Pasture Watch GitHub site](https://github.com/wri/global-pasture-watch)**\n\n### Bands\n\n**Bands**\n\n| Name | Min | Max | Pixel Size | Description |\n|---------|-----|------|------------|--------------------------------------------------------|\n| `gc_m2` | 0 | 4000 | 30 meters | Grams of carbon per square meter per year (gC/m²/year) |\n\n### Image Properties\n\n**Image Properties**\n\n| Name | Type | Description |\n|---------|------|-----------------|\n| version | INT | Product version |\n\n### Terms of Use\n\n**Terms of Use**\n\n[CC-BY-4.0](https://spdx.org/licenses/CC-BY-4.0.html)\n\n### Citations\n\nCitations:\n\n- Isik, M. S., Mesquita, V., Parente, L., \\& Consoli, D. (2025).\n Global Pasture Watch - Source Code of the Global Uncalibrated EO-based GPP and\n Grassland GPP Maps at 30m. Zenodo.\n \\[Source code\\]. Zenodo\n [doi:https://doi.org/10.5281/zenodo.15675358](https://doi.org/10.5281/zenodo.15675358)\n- Isik MS, Parente L, Consoli D, et al. (2025).\n Light use efficiency (LUE) based bimonthly gross primary\n productivity (GPP) for global grasslands at 30 m spatial\n resolution (2000--2022), PeerJ.\n [doi: https://doi.org/10.7717/peerj.19774](https://doi.org/10.7717/peerj.19774)\n\n### DOIs\n\n- \u003chttps://doi.org/10.5281/zenodo.13890401\u003e\n- \u003chttps://doi.org/10.7717/peerj.19774\u003e\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nMap.setCenter(-49.265188, -16.602052, 4);\n\nvar ugppVis = {min: 0, max: 4000, palette: \"faccfa,f19d6b,828232,226061,011959\"}\nvar ugpp = ee.ImageCollection(\n \"projects/global-pasture-watch/assets/ggpp-30m/v1/ugpp_m\"\n)\n\nvar ugpp2024 = ugpp.filterDate('2024-01-01', '2025-01-01').first();\nMap.addLayer(ugpp2024, ugppVis, 'Uncalibrated GPP (2024)');\n\nvar ugpp2000 = ugpp.filterDate('2000-01-01', '2001-01-01').first();\nMap.addLayer(ugpp2000, ugppVis, 'Uncalibrated GPP (2000)');\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/global-pasture-watch/projects_global-pasture-watch_assets_ggpp-30m_v1_ugpp_m) \n[GPW Annual uncalibrated Gross Primary Productivity (uGPP) v1](/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggpp-30m_v1_ugpp_m) \nThis dataset provides global uncalibrated EO-based Gross Primary Productivity from 2000 at 30-m spatial resolution. Produced by Land \\& Carbon Lab Global Pasture Watch initiative, the current dataset provides Gross Primary Productivity (GPP) values globally at 30-m spatial resolution from 2000 onwards. GPP values are modeled via a light use ... \nprojects/global-pasture-watch/assets/ggpp-30m/v1/ugpp_m, global,global-pasture-watch,land,landcover,landuse,plant-productivity,publisher-dataset,vegetation \n2000-01-01T00:00:00Z/2024-01-01T00:00:00Z \n-90 -180 90 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [https://doi.org/10.7717/peerj.19774](https://doi.org/https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring)\n- [https://doi.org/10.7717/peerj.19774](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggpp-30m_v1_ugpp_m)"]]
