The World Programme for the Census of Agriculture 2030 (WCA 2030) marks a paradigm shift in how countries design and implement agricultural censuses. It explicitly encourages the integration of Earth Observation (EO) and geospatial data to enhance efficiency, accuracy, and comparability. This paper presents a methodological synthesis of how EO can be embedded across the census cycle—from the preparation of geospatial reference layers and georeferencing of holdings to validation and area estimation. Drawing on lessons from FAO's EOSTAT programme, the UN Handbook of Remote Sensing for Agricultural Statistics, and innovative examples such as Brazilian Institute of Geography and Statistics's (IBGE) AI-based parcel delineation in Brazil, this article illustrates practical pathways for operationalization. The analysis emphasizes institutional readiness, quality assurance, and emerging AI-based approaches that enable scalable, cost-effective census operations aligned with WCA 2030 standards.

Earth Observation (EO) data are widely used in agricultural statistics production. However, the accuracy of EO-based land use classification is limited because of the limitations of using in situ census or survey data as training sets for EO applications. In this work, we provide recommendations for National Statistical Offices (NSO) to design in situ data collection campaigns that benefit both conventional statistics and EO-based assessments. Our recommendations are supported by a case study done by Chile's NSO (Instituto Nacional de Estadistica).

The United Nations Food and Agriculture Organization (FAO) Hand-in-Hand (HiH) initiative is deploying a set of novel approaches, data sources, and analytical tools to drive agricultural innovation. Currently, there are limited resources to collect, analyse and disseminate agricultural statistics on a regular basis. Earth Observation (EO) data is an ideal solution to fill this gap and strengthen the capacity to generate crop statistics at national and subnational levels. However, access, storage, pre-processing and analysis of EO data is limiting its use and uptake.

Remote sensing offers a scalable and low cost solution for the production of large-scale crop maps, which can be used to extract relevant crop statistics. However, despite considerable advances in the new generation of satellite sensors and the advent of cloud computing, the use of remote sensing for the production of accurate crop maps and statistics remain dependent on the availability of ground truth data.

This paper presents an innovative methodology that has allowed the production of five standardized annual land cover maps (2017–2021) using only a single in situ dataset gathered in the field for the reference year, 2021. A total of 10 land cover classes are represented in the maps, including specific features, such as gullies, which are under close monitoring. The aim of this work is to demonstrate a suitable solution for operational land cover mapping that can cope with the scarcity of in situ data, which is a common challenge in almost every developing country.

This study describes the new approach to estimate the MGCI indicator using ESA’s CCI-LC and products, assesses the accuracy of the new approach; reviews the limitations of the current SDG indicator definition to monitor progress towards SDG 15.4; and reflects on possible further adjustments of the indicator methodology in order to address them. This is an open access article published externally in ISPRS International Journal of Geo-Information Volume 10, Issue 7.