“Remote sensing can improve food production.”

This review examines the synergistic application of these technologies in enhancing agricultural efficiency, resource optimization, and environmental sustainability. UAVs enable high-resolution, real-time monitoring of crop health, soil conditions, and pest infestations, while satellite remote sensing provides scalable, large-scale agricultural data for comprehensive landscape analysis. Case studies demonstrate that integrating UAV and satellite data with machine learning improves crop yield prediction accuracy and resource use efficiency, reducing irrigation costs by 20–25% and nitrogen application by up to 31 kg ha⁻¹, without compromising productivity.

Remote sensing has become an indispensable tool for monitoring crop growth and predicting yields at field to global scales. Numerous studies have demonstrated strong relationships between vegetation indices derived from multispectral satellite data and crop yield. However, although remote sensing-based approaches provide valuable information for management decisions, they are still subject to uncertainties related to sensor characteristics, atmospheric conditions, and model generalizability, which can limit their operational impact on food production if not properly addressed.

The article notes that the integration of remote sensing and precision agriculture tools allows for more accurate monitoring of crop growth and stress. It explains that by using satellite and UAV (drone) imagery alongside machine learning, farmers can optimize management practices, which "can lead to improved yield prediction and better-informed decisions that ultimately enhance crop productivity and resource-use efficiency." The paper presents remote sensing as a key component of precision agriculture systems designed to improve agricultural production.

Remote sensing can be used to monitor crop condition, estimate crop area and yield, and support agricultural decision-making. FAO materials on land and water management describe remote sensing as a tool for improving information available to farmers and planners, which can increase efficiency in agricultural production.

New Michigan State University research found that incorporating in-season water deficit information into remote sensing-based crop models significantly improves corn yield predictions. The researchers used Landsat Analysis Ready Dataset products to calculate the green chlorophyll vegetation index and combined this with a crop drought index. They concluded that by including these remote sensing indicators in crop yield predictions, predictions could be improved substantially, enabling farmers to make more informed management decisions at the subfield level.

Advancements in remote sensing technologies—including drones and high-resolution satellite imagery—and the advent of improved data processing and machine learning all hold promise to revolutionize the cost, resolution, and accuracy of collecting agricultural information. RTI’s internally funded Grand Challenge project in Rwanda is applying recent advances in remote sensing to determine how insights and data from these technologies can be harnessed to promote food security in areas of need. By enabling better crop monitoring and yield prediction, these tools can inform targeted interventions that help stabilize and increase food production in vulnerable regions.

The overall scientific goal of the project was to develop a new algorithm and products for agriculture monitoring, namely crop yield assessment and mapping, by combining moderate spatial resolution images acquired by Landsat-8, Sentinel-2 and Sentinel-1/SAR remote sensing satellites. The project explored an increased temporal frequency of observations and coverage as well as combination of optical and microwave (SAR) imagery to generate new products that provided improved spatially explicit crop yield mapping at regional and field scales. The results indicated that multi-spectral satellite data can be used to assess crop yield at regional to field scale, and that temporal frequency and spatial resolution of satellite data are critical for explaining yield variability at field scale.

Remote sensing and precision agriculture technologies are frequently promoted as solutions to increase food production and sustainability. However, our review highlights that evidence of large-scale yield gains directly attributable to these technologies is still limited and context-dependent. Many studies report improved monitoring and management potential, but relatively few document statistically significant yield increases once confounding factors such as weather and input use are controlled, indicating that remote sensing alone is not a guarantee of improved food production.

The article explains that modern precision agriculture "relies heavily on sensor technology, and among these, remote sensing is the most widely used tool." Remote sensing imagery can detect early signs of crop stresses such as nutrient deficiency, moisture issues, disease and pests, and this information "can be used as a guide for site-specific nitrogen, irrigation, or pesticide applications by applying inputs only in the areas where it is needed." It concludes that remote sensing technology helps farmers make "more timely and precise decisions" on their farms, which is aimed at improving crop performance and efficiency.

Satellite and drone images of fields can reveal the growth rate and health of crops and improve yield prediction. The UPSCALE project focuses on crops such as timothy, wheat, barley and potato, and establishes methods to accurately estimate leaf area index and chlorophyll content both at leaf and canopy levels across crop growth stages using imaging methods. The spatially accurate quantification of these crop canopy parameters is an important input in developing better yield prediction models, which in turn can support crop performance predictions and breeding programs under changing climate conditions.

This webinar explores the intersection of machine learning and satellite imagery, focusing on innovations that enhance monitoring, planning, and climate resilience in food systems. The session will demonstrate how satellite data and machine learning models can improve the accuracy of yield forecasting and support the identification of invasive species to support decision-making in rangeland management, which threaten native vegetation and pastoral livelihoods. The discussion emphasizes how advances in AI and remote sensing can translate remote-sensing insights into practical decision-support tools for farmers and policymakers.

The blog states that "remote sensors in agriculture play a crucial role in boosting crop yields" by enabling efficient and optimized farm management. It explains that sensors monitor soil moisture, temperature and nutrient levels so farmers can time irrigation and fertilizer applications precisely, which "reduces water waste and increases irrigation efficiency" and "enhances nutrient uptake by plants". It adds that remote sensing of crop health allows early detection of stress from pests, diseases or insufficient nutrients, enabling timely interventions that help keep crops productive.

Remote sensing in agriculture is the process of acquiring information about agronomic factors at a specific point, or an area, from a distance. Once the data has been retrieved, it can be arranged in ways that provide informative representations of, and insights on, the agronomic factors you were sensing. This information and insights can then be used to support in making more data-informed management decisions. Grower Josh Barton notes that remote sensing 'provides evidence for optimal inputs,' and water use efficiency can be improved because with the data provided 'you can see when you get it right.'

Remote sensing imagery can be used for mapping soil properties, classification of crop species, detection of crop water stress, monitoring of weeds and crop diseases, and mapping of crop yield. The page also says growers can use it to estimate yield, manage irrigation, detect weeds, and identify crop stress, all of which are described as actions that can positively influence crop yield.

Remote sensing can improve food production only when its information is turned into effective management decisions, and benefits vary by crop, region, data quality, and farmer adoption. In practice, studies often report improved monitoring, input efficiency, or yield prediction rather than automatic yield gains in every setting.

Across agronomy literature, remote sensing is widely recognized as a key enabling technology for precision agriculture, supporting variable-rate input application, early warning of crop stress, and better yield forecasting. Nonetheless, meta-analyses and reviews often emphasize that the translation from improved information to measurable gains in food production depends heavily on farmers’ ability and incentives to act on that information, access to inputs, and institutional support. This means that remote sensing can improve food production in principle and in many documented cases, but its impact is not automatic or uniform across regions and farm types.

Remote sensing techniques are applied to crop growth monitoring, including plant populations, nutrient deficiencies, diseases, and water stress. The page presents remote sensing as a management tool for agriculture, but it does not itself provide rigorous experimental evidence that it increases food production.