Crop monitoringCrop monitoring, mapping, and yield estimationYield estimation has continued to be the focus of agricultural remote sensingRemote sensing studies. In this regard, multi-source imagery from Earth ObservationEarth observation satellites, often combined with ancillary data from ground observation, can provide repetitive and synoptic views of key crop parameters including crop nutrient and growth status. Different methods using earth observationEarth observation data have been developed for this purpose. Approaches vary from purely empirical such as optical/microwave based and the synergistic methods between optical and microwave data to more complex ones, including assimilating Earth ObservationEarth observation (EO) data with crop growth modelCrop Growth Models (CGMs) (CGMs) through continuously infusing EO data into the model system. The present review aims to offer a comprehensive and systematic review considering the different approaches of crop area mapping, monitoring, and yield estimationYield estimation methods and its possible application from the Abay basin perspective, which differ in terms of the assumptions they entail and complexity as well as their requirement for field observation and other ancillary data. In this respect, the overview of the remote sensingRemote sensing methods is timely as such method is being applied in the operational crop mapping, retrieval of crop growth status, and crop monitoringCrop monitoring by a series of satellite platforms such as Landsat 8 OLI and SentinelsSentinel-1 &2 1 and 2. In general, EO data have the potential to improve the estimation accuracy of crop area, soil propertiesSoil properties, canopy state variables, and crop yield based on quantitative remote sensingRemote sensing such as the assimilation of remote sensing variables and crop simulation models in the data-scarce smallholder agricultureAgriculture such as the Abay basin, EthiopiaEthiopia. Finally, considering the repetitive and synoptic views of key crop parameters of the EO satellites and the various methods such as empirical regressions between historical yield and in-season variables derived from remotely sensed data, assimilation of remote sensingRemote sensing and dynamicCrop Growth Models (CGMs) crop growth models (CGMs), or on integration between empirical regression model with CGMsCrop Growth Models (CGMs), we propose new methods or future directions for crop area mapping monitoring and yield estimationYield estimation improvement in the Abay basin using agricultural remote sensing approaches.

Linking Earth Observation to Crop Area Mapping and Yield Estimation in the Abay Basin

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发表日期：

2025年

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Gebeyehu Abebe,
Berhan Gessesse

摘要

Crop monitoringCrop monitoring, mapping, and yield estimationYield estimation has continued to be the focus of agricultural remote sensingRemote sensing studies. In this regard, multi-source imagery from Earth ObservationEarth observation satellites, often combined with ancillary data from ground observation, can provide repetitive and synoptic views of key crop parameters including crop nutrient and growth status. Different methods using earth observationEarth observation data have been developed for this purpose. Approaches vary from purely empirical such as optical/microwave based and the synergistic methods between optical and microwave data to more complex ones, including assimilating Earth ObservationEarth observation (EO) data with crop growth modelCrop Growth Models (CGMs) (CGMs) through continuously infusing EO data into the model system. The present review aims to offer a comprehensive and systematic review considering the different approaches of crop area mapping, monitoring, and yield estimationYield estimation methods and its possible application from the Abay basin perspective, which differ in terms of the assumptions they entail and complexity as well as their requirement for field observation and other ancillary data. In this respect, the overview of the remote sensingRemote sensing methods is timely as such method is being applied in the operational crop mapping, retrieval of crop growth status, and crop monitoringCrop monitoring by a series of satellite platforms such as Landsat 8 OLI and SentinelsSentinel-1 &2 1 and 2. In general, EO data have the potential to improve the estimation accuracy of crop area, soil propertiesSoil properties, canopy state variables, and crop yield based on quantitative remote sensingRemote sensing such as the assimilation of remote sensing variables and crop simulation models in the data-scarce smallholder agricultureAgriculture such as the Abay basin, EthiopiaEthiopia. Finally, considering the repetitive and synoptic views of key crop parameters of the EO satellites and the various methods such as empirical regressions between historical yield and in-season variables derived from remotely sensed data, assimilation of remote sensingRemote sensing and dynamicCrop Growth Models (CGMs) crop growth models (CGMs), or on integration between empirical regression model with CGMsCrop Growth Models (CGMs), we propose new methods or future directions for crop area mapping monitoring and yield estimationYield estimation improvement in the Abay basin using agricultural remote sensing approaches.