In a context of concern about the impact of agriculture on GHGs emissions, new techniques for monitoring agro-ecosystems are necessary and subject to scientific interest. Regarding their active participation in global warming and their impact on the modification of natural cycles (water, carbon, nitrogen, etc.), there is a need to manage greenhouse gases (GHG) emissions and store carbon (C) in the soil. National expertise 4 per 1000 aims to neutralize the annual increase in atmospheric carbon in the soil. This makes it more fertile by participating in larger amounts of organic matter (Pellerin et al. 2019). According to Pique et al. (2020), croplands are part of the problem but also part of the solution: they contribute to GHG emission but thanks to the soil ability to store carbon, they can also have a potential to mitigate climate change. Some methods in agriculture allow C to be stored in the soil. For example, reasoned practices that conserve and improve the qualities of the soil, and that use fewer inputs in their cropping systems as resonating surface tillage. Also, the introduction of intermediate covers between the main crop rotations to avoid bare soil and to enrich the soil by burying these covers. This last solution is assumed to be the most efficient to store C. To estimate these carbon budgets, we can use flow towers or meteorological towers to obtain specific data, or satellite data to obtain crop phenological data. The use of such tools requires constant availability. However, the measurements considered depend on climatic factors, instrumented tools and visibility for the satellites (clouds). Thus, an approach combining the use of all these resources was developed in this project. In Europe, agricultural areas cover 38% of the territory. The first European productions are represented by horticultural plants (13,6%), milk (13%) and cereals (11,4%) (Ledroit, 2021, in touteleurope.eu). In France, agriculture occupy 46% of the land (from statistical data collected in 2018 by Agreste) and the share of crops in the used agricultural area (UAA) is predominant in certain departments of the North and Southwest. Maize represents 10% of the UAA in France. On a European scale, maize production is around 6 million of tons (from statistical data collected in 2019 by Agreste). Maize is originated from Central America, an came to Europe around the XVIth century. The diversity of maize varieties allows this crop to growth under various climates, and China is actually the second global producer. Since the XIXe century, maize as the other plants, took benefits from the genetics and has a lot of hybrid varieties with higher yield, stronger resistance to disease and higher reliability due to their adaptations (from the technical website www.arvalis.fr, 2021). We can distinguish three types of maize: forage maize, grain maize (sweat maize, popcorn maize..), and mixed maize. However, maize needs water during its development stages and its maturity because stresses can disturb its growth and final yield. Irrigated plot represents 40,6% of the total area of maize crop in France. Popcorn maize varieties are part of irrigated grain maize. In France, popcorn maize is mainly cultivated in Charente-Maritime and in the Gers on more than 9000 hectares each year, by around 400 producers. It represents 0.61% of the global maize crop (from the technical website www.passioncereales.fr). Soil models or inventory approaches to estimate carbon budget, yield or biomass are existing. They require specific information, often very precise, about the soil dynamic (nutrients, water, organic matter...) or the plant dynamic (photosynthesis). Despite the intervention of precise, complex and numerous data, the models are always limited. For instance, these approaches offer inconclusive results in the application on a larger scale in time and space, due to their demand for information applicable to one year of culture only and specific to a study area. To extend these methods at larger scales, it would be necessary to know certain practices such as the crop type, the presence of intermediate cover or tillage, and the amendments applied to the crop. The contribution of remote sensing data into agronomic models is a way to improve these uncertainties by using another approach. The model studied here is defined as simple, since it uses a small number of non-complex equations, with the contribution of remote sensing data for monitoring the development of vegetation via simple indices (i.e. GAI or NDVI).