{"id":20595,"date":"2026-05-20T00:49:13","date_gmt":"2026-05-19T21:19:13","guid":{"rendered":"https:\/\/vastraholding.com\/en\/?p=20595"},"modified":"2026-05-20T00:54:12","modified_gmt":"2026-05-19T21:24:12","slug":"geospatial-foundation-models-agriculture-yield-forecasting","status":"publish","type":"post","link":"https:\/\/vastraholding.com\/en\/geospatial-foundation-models-agriculture-yield-forecasting\/","title":{"rendered":"Geospatial Foundation Models for Farm Yield"},"content":{"rendered":"\t\t
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Geospatial Foundation Models in Agriculture; From Satellite Imagery to Farm Yield Forecasting<\/h1>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Agriculture today faces a challenge that cannot be understood from inside the farm alone. Shifting rainfall patterns, water stress, fragmented land parcels, yield volatility, and the need for rapid estimates of cultivated area have made decision-making dependent on data that extends beyond the farm scale. Satellite imagery has been part of this answer for years, but geospatial foundation models have shifted the path from isolated observation of land to general learning from the Earth. The core value of these models is that, instead of building a separate model for each crop, province, or season, they learn patterns from massive Earth observation archives and are then adapted to agricultural tasks.<\/p>\n\n

In agriculture, this shift is not merely a technical matter; it is tied to food security, the economics of water, and the quality of investment decisions. When a system can analyze crop type, growth status, land-use change, disaster damage, or the likelihood of yield decline through spatial and temporal data, decisions made by governments, banks, insurers, and investors can move beyond delayed reporting. However, a geospatial foundation model does not replace field data, agronomic knowledge, or local validation. Such a model gains practical value only when satellite-based outputs are connected to farm boundaries, climate data, soil data, crop management information, and a human audit mechanism.<\/p>\n\n

In Iran, the importance of this issue cannot be separated from the country\u2019s water pressure and climatic structure. According to FAO data, Iran\u2019s average annual rainfall is reported at 240 millimeters, and about 90 percent of the country\u2019s territory is arid or semi-arid. The same profile shows that agriculture accounts for 11 percent of GDP and around 30 percent of employment, while wheat, rice, and barley are produced on 70 percent of cultivated land. Therefore, any agricultural monitoring or forecasting system in Iran must be designed from the outset around the logic of water scarcity, sharp climatic variation, the distinction between irrigated and rainfed farming, and the need for local measurement.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Geospatial Foundation Models and the Changing Logic of Farm Monitoring<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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A geospatial foundation model should be understood as a large, often self-supervised model that learns general representations from spatial and temporal Earth observation data. These data may include optical imagery, Landsat and Sentinel-2 time series, radar data, LiDAR, climate data, and terrestrial layers. The main difference from classical remote sensing models lies precisely here, because a classical model is usually trained for a specific task and a specific geographic area. After general pretraining, a foundation model is adapted to tasks such as crop-type classification, crop segmentation, disaster monitoring, land-use change detection, and yield forecasting.<\/p>\n\n

This logic does not make labeled data irrelevant. In Prithvi-EO-2.0, the model\u2019s performance in the PASTIS scenario showed that when only 10 percent of the data was used, the 600M version reached an mIoU of 37.4 percent, while with 100 percent of the data, this figure increased to 53.4 percent. The message of this number for agriculture is clear: large-scale pretraining can strengthen transfer learning, but the quality of local data and reliable labels remains decisive. As a result, a foundation model becomes dependable at the farm level only when labeled crop data, parcel boundaries, and field observations remain part of the evaluation cycle.<\/p>\n\n

Prithvi-EO-2.0 is one of the formal and citable examples in this field. The model was trained on 4.2 million global time-series samples from Harmonized Landsat and Sentinel-2 at a 30-meter spatial resolution. NASA, IBM, and Forschungszentrum J\u00fclich introduced the expanded version of Prithvi as an open-source geospatial foundation model, with stated applications including land-use change monitoring, disaster monitoring, and global-scale crop yield forecasting. This combination of open-source access and training on extensive data makes Prithvi an important example for understanding both the capacity and the limitations of geospatial models in agriculture.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The Technical Chain from Satellite Images to Agricultural Embeddings<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The technical path from satellite imagery to farm yield forecasting does not begin with image acquisition, nor does it end with the model output. First, multi-temporal data must be collected, co-located, corrected, and prepared for analysis. The model then turns these data into embeddings, or numerical representations, so that each pixel, parcel, or spatial area is represented through compact and comparable features. In the next stage, this representation is combined with weather, soil, crop management, and field data so the model can be adapted and validated for a specific crop and region.<\/p>\n\n

HLS, or Harmonized Landsat and Sentinel-2, plays an infrastructural role in this chain because it provides Landsat and Sentinel-2 data in a harmonized 30-meter framework. Prithvi-EO-2.0 was also explicitly trained on this HLS archive. On the other hand, Sentinel-2, as part of the European Union\u2019s open data infrastructure, provides global coverage of the Earth\u2019s land surface every five days, with L1C data available from June 2015 and global L2A data available from January 2017. This temporal resolution matters for agriculture because phenology, water stress, and climatic shocks occur over time and cannot be precisely detected from a single image alone.<\/p>\n\n

Spatial resolution is equally decisive. In HLS and Prithvi-EO-2.0, the pixel size is 30 meters, while the AlphaEarth-related Satellite Embedding in Google Earth Engine is reported at a 10-meter pixel size. For small parcels, larger pixels can lead to the problem of mixed pixels, meaning a single pixel may simultaneously contain parts of multiple land covers or several farms. For this reason, choosing between a 30-meter model and a 10-meter embedding is not merely a technical decision; it is a trade-off among spatial accuracy, processing cost, cloud access, and the level at which agricultural decisions are made.<\/p>\n\n

\u2013 Geospatial Embeddings and Turning Imagery into the Numerical Language of the Farm<\/h3>\n\n

AlphaEarth Foundations from Google DeepMind is an example of the move toward multisensory embeddings. This system integrates several data streams, including optical imagery, radar, LiDAR, and other sources, and its Satellite Embedding dataset is available in Google Earth Engine. Google DeepMind has stated that AlphaEarth analyzes land and coastal waters in 10\u00d710-meter squares and represents each embedding with 64 components on a 64-dimensional sphere. For agriculture, such an embedding can provide a ready-made input for classification, monitoring, or regression, instead of requiring the manual design of numerous indices.<\/p>\n\n

\u2013 Nick Murray, Director of the Global Ecology Lab at James Cook University and Scientific Lead of the Global Ecosystems Atlas:<\/cite> \u201cThe Satellite Embedding dataset is transforming our work and helping countries map previously unknown ecosystems.\u201d<\/blockquote>\n\n

The use of such embeddings in agriculture should be understood with operational caution. Google Earth Engine states that some large-scale swath and availability artifacts remain in Satellite Embedding, although they usually create only small vector offsets. This point matters for farm-level systems because a small pixel-level error may be acceptable in a national report, but problematic in insurance, credit, or planting recommendations for a specific parcel. Therefore, a ready-made embedding is the starting point of analysis, not a definitive document about the condition of every farm.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Prithvi and AlphaEarth in the Global Test of Geospatial Models<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The Prithvi-EO-2.0 case study shows how strongly geospatial foundation models depend on precise evaluation metrics. In a multi-temporal agricultural crop segmentation test in the United States, the Prithvi-EO-2.0-600M version reached an mIoU of 50.7 percent, while U-Net was reported at 42.6 percent. This advantage is important for research comparison, but the absolute mIoU value also shows that pixel-level crop segmentation still has a considerable distance to go before it can support low-error farm-level decisions. For this reason, practical use of such models must be accompanied by confidence levels, local calibration, and decision-error control.<\/p>\n\n

In crop-type classification in Europe, Prithvi-EO-2.0-600M reached a weighted F1 score of 84.6 percent at a data ratio of 1.0, compared with 81.5 percent for ViViT. Weighted F1 matters when crop classes are imbalanced and the frequency of one class can alter the apparent performance of the model. From an agricultural perspective, the European result shows that a foundation model can deliver competitive performance in crop-type classification, but this metric should not be equated with the accuracy of farm yield forecasting. Crop classification, pixel-level segmentation, and yield prediction are three separate problems, each requiring its own reference data and evaluation metric.<\/p>\n\n

AlphaEarth Foundations, on the other hand, emphasizes the compression and generalizability of spatial representations. Google DeepMind has stated that the system\u2019s compact summaries require 16 times less storage than other tested systems. Version v2.1 in Google Earth Engine cites more than 10.1 million video sequences in the reproduction of training data, and its annual collection contains more than 1.4 trillion embedding footprints. This scale matters for agriculture because storage costs and data retrieval speed can determine the boundary between a laboratory prototype and a national operational system.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The Economics of Open Data and the Operational Cost of Agricultural Satellite Analysis<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The economics of geospatial foundation models begins with open data policy. The Landsat archive became available for download at no cost to all users worldwide on April 21, 2008, bringing the cost of access to raw data close to zero for users. This zero cost, however, does not mean that an operational system is free, because processing, storage, labeling, validation, expert labor, and infrastructure maintenance still carry costs. Even so, eliminating the cost of raw archives has been one of the preconditions for the growth of remote sensing science and the development of large-scale geospatial models.<\/p>\n\n

\u2013 Curtis Woodcock, Professor at Boston University:<\/cite> \u201cThis decision was, apart from the launch of individual sensors, the biggest change in the entire history of the Landsat program.\u201d<\/blockquote>\n\n

Sentinel-2 has reinforced the same open-data logic through frequent global coverage. Global land-surface coverage every five days makes it possible to observe crop conditions throughout the growing season, rather than limiting analysis to a single static image. For agriculture, this capability is especially important in monitoring vegetation changes, detecting seasonal disruptions, and distinguishing growth stages. Limitations such as cloud cover, the need for atmospheric correction, and spatial resolution relative to small parcels still remain, but the combination of Sentinel-2 with Landsat and ground data provides an important foundation for agricultural models.<\/p>\n\n

By contrast, the economics of cloud processing is the hidden part of the story. Google Cloud prices Earth Engine based on a monthly platform fee, Earth Engine Compute Units, and GB-month storage. In addition, Satellite Embedding data in Google Cloud Storage is provided in a Requester Pays bucket, meaning the user must have a billing project for retrieval and egress. For an investor or a government institution, the conclusion is clear: open data reduces entry costs, but the operational costs of modeling, processing, exporting, auditing, and maintenance must still be included in the economic plan.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Standards and Data Governance in Agricultural Geospatial Systems<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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A geospatial foundation model cannot become an operational system without data standards. Cloud Optimized GeoTIFF version 1.0, as an OGC standard, relies on tiles, overviews, GeoTIFF keys, and HTTP range requests, enabling partial access to the required sections of an image. This capability matters for agriculture because a national or corporate system does not always need to download and process the entire image. When the goal is periodic monitoring of cultivated area or crop stress, such a standard reduces the cost and time required to process large rasters.<\/p>\n\n

Metadata standards are equally important. ISO 19115-1:2014 defines the schema required to describe geographic information and services, covering identification, extent, quality, spatial and temporal aspects, spatial reference, and distribution information. STAC, or SpatioTemporal Asset Catalog, provides a common language for describing the metadata of geospatial assets so that images, embeddings, and farm-level data can be more effectively indexed, discovered, and queried. OGC API Features also provides a RESTful interface for discovering, querying, and retrieving spatial feature data, and can connect parcel boundaries, farm features, and management layers to the analytical system.<\/p>\n\n

Data governance is not only a matter of file format; it is also related to the impact of decisions. Regulation (EU) 2024\/1689, or the EU AI Act, establishes a unified framework for the development, deployment, and use of artificial intelligence systems in the European Union. Article 22 of the GDPR also establishes the right not to be subject to fully automated decision-making that produces legal effects or similarly significant effects. If the output of a geospatial model is used in loss insurance, farmer credit, or subsidy allocation, explainability, the ability to appeal, and human involvement in the decision loop move from a technical margin to an operational requirement.<\/p>\n\n

The EU Data Act is also important for connected agriculture because it establishes a framework for data generated by connected equipment and the right to access and use such data. In a digital farm, tractors, sensors, drones, weather stations, and irrigation systems can generate data that is valuable for adapting a geospatial model. If ownership, access, transfer, and use of these data are not clear, even the best satellite imagery will face friction at the implementation stage. Therefore, the technical architecture must be co-designed from the beginning with the legal architecture of data rights, consent, security, and auditability.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The Localization Path for Geospatial Models in Iranian Agriculture<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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For Iran, a cautious path does not begin with the claim of precise farm yield prediction. The official FAO and Ministry of Agriculture Jihad project under the code TCP\/IRA\/3602 addressed a related issue, with the aim of improving the agricultural monitoring system through satellite imagery, including crop status checks, area estimates, and yield forecasting. The project budget was reported at USD 489,000, and its implementation period ran from November 2016 to November 2018. For Iran, this experience is more a sign of capacity-building and operational method design than an estimate of the full cost of a national geospatial foundation model system.<\/p>\n\n

Water scarcity is the most important contextual layer in localizing this technology. FAO AQUASTAT reported that Iran\u2019s total agricultural, municipal, and industrial water withdrawal in 2004 was about 93.3 cubic kilometers, with agriculture accounting for around 92 percent of total withdrawals. The same source estimated groundwater depletion at about 3.8 cubic kilometers per year. These data do not mean that a geospatial model can reduce water consumption on its own, but they do show that outputs such as cultivated-area monitoring, irrigated and rainfed classification, and water-stress alerts can serve as important inputs for water decision-making.<\/p>\n\n

The structure of Iran\u2019s agricultural land also makes model design more sensitive. FAO has reported that only 12 percent of Iran\u2019s total land is under crops, orchards, or vineyards, and that less than one-third of cultivated land is irrigated. This pattern means that a single general model, without distinguishing climate, crop, irrigation, and land quality, cannot serve as a basis for precise decision-making. A geospatial model for Iran must account from the outset for differences between rainfed and irrigated farming, growing seasons, cropping patterns, and the gap between small parcels and national-level reporting.<\/p>\n\n

\u2013 Iran\u2019s Operational Priorities Between Crop Monitoring and Yield Forecasting<\/h3>\n\n

Under Iran\u2019s conditions, a realistic initial focus could be placed on cultivated-area estimation, irrigated and rainfed classification, water-stress alerts, and disaster-damage monitoring. These priorities are aligned with the goals of the FAO project in Iran and with the global applications of Prithvi and AlphaEarth. Farm yield forecasting requires field data, local calibration, and defensible error metrics, and without these elements it should not be presented as a definitive system output. The correct approach is for the model to begin at the national and provincial reporting level, and then move closer to farm-level decisions as the quality of parcel-boundary data and ground truth improves.<\/p>\n\n

The scientific study by Mesgaran and colleagues on Iran\u2019s land suitability also reinforces the importance of spatial differentiation. In that study, 0.4 percent of the land, equivalent to 0.6 million hectares, was classified as very good; 2.2 percent, equivalent to 3.6 million hectares, as good; and 7.9 percent, equivalent to 12.8 million hectares, as moderate. This distribution shows that agricultural decision-making in Iran deals with severe territorial heterogeneity. If a geospatial model is to be useful for agricultural investment, it must reflect differences in land quality, water, climate, and crop type at an actionable spatial level.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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A Practical Investment Decision for Farm Yield Forecasting<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Investment in a geospatial foundation model for agriculture is defensible only when its output is connected to a specific decision. If the goal is merely to produce an attractive map, even an advanced model will not create sustainable economic value. Value is created when the system output is used in index-based insurance, farmer credit monitoring, crop planning, production estimation, stress alerts, or disaster-damage management. In such applications, metrics such as mIoU, weighted F1, RMSE, MAE, and R\u00b2 must be translated from the language of research into the language of decision risk.<\/p>\n\n

The key trade-off lies between accuracy and scale. A 10-meter model such as AlphaEarth is more attractive for small parcels, but the costs of cloud computing, egress, and data management may be higher. A 30-meter model such as Prithvi relies on HLS infrastructure and has advantages at larger scales, but it faces the mixed-pixel problem in small parcels. The right choice depends on the purpose of the system, because national cultivated-area monitoring, parcel-level loss insurance, water-stress alerts, and yield forecasting each require a different data architecture and error threshold.<\/p>\n\n

For Iran, the decision-making path should follow a staged architecture. The first stage is to create a standardized data catalog using STAC, COG, ISO 19115-1, and the connection of parcel boundaries to OGC API Features. The second stage is to use open Landsat and Sentinel-2 data alongside ready-made embeddings and open-source models for monitoring cultivated area and crop status. The third stage is to collect field and labeled data to adapt the model for selected crops and climates, so that yield forecasting can move from a technical claim to a testable decision-level capability.<\/p>\n\n

Geospatial foundation models are moving the future of agricultural monitoring toward a combination of open data, compact embeddings, local validation, and data governance. This technology is neither a shortcut for eliminating fieldwork nor merely a research tool detached from implementation. Its value lies in turning Earth observation into a language of decision-making and reducing the gap between satellite imagery, water risk, crop conditions, and agricultural investment. For Iranian agriculture, the credible starting point is to build an accurate and auditable monitoring system; farm yield forecasting should be the next step and should depend on reliable local data.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Geospatial\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Geospatial foundation models combine satellite imagery, embeddings, and field data to improve crop monitoring and water management, but farm yield forecasting still requires local validation and auditable data.<\/p>\n","protected":false},"author":1,"featured_media":20603,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[275,126],"tags":[],"class_list":["post-20595","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-ag-iot","category-vastra-article"],"_links":{"self":[{"href":"https:\/\/vastraholding.com\/en\/wp-json\/wp\/v2\/posts\/20595","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vastraholding.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vastraholding.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vastraholding.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/vastraholding.com\/en\/wp-json\/wp\/v2\/comments?post=20595"}],"version-history":[{"count":4,"href":"https:\/\/vastraholding.com\/en\/wp-json\/wp\/v2\/posts\/20595\/revisions"}],"predecessor-version":[{"id":20623,"href":"https:\/\/vastraholding.com\/en\/wp-json\/wp\/v2\/posts\/20595\/revisions\/20623"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/vastraholding.com\/en\/wp-json\/wp\/v2\/media\/20603"}],"wp:attachment":[{"href":"https:\/\/vastraholding.com\/en\/wp-json\/wp\/v2\/media?parent=20595"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vastraholding.com\/en\/wp-json\/wp\/v2\/categories?post=20595"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vastraholding.com\/en\/wp-json\/wp\/v2\/tags?post=20595"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}