Land surface remote sensing in agriculture and forest

Abstract

Agriculture brings with it a number of issues: agricultural production and food security, water and soil resource conservation, limiting the impact of farming on the quality of our environment (water, air, soil). In the context of global changes and the challenges they pose for agriculture sustainability, our ability to characterize how croplands function in terms of water, carbon and particle fluxes is crucial. Developments of agro-ecosystems modeling, including their interactions with the atmosphere and the anthropogenic factors are valuable tools for progress in this direction. Remote sensing, with the high variety of spectral ranges and the fine spatial and temporal resolution currently available, is a tool of great value for various applications in agriculture. The availability of robust inverse methods that allow surface biophysical variables to be assessed, combined with modeling approaches, makes it a high performance tool. The major contributions of remote sensing include : - its ability to cover large stretches of land and to provide information on the various land uses and practices generated by agriculture. These uses and practices are important to know, both for census purposes (agricultural statistics, agri-environmental monitoring, etc.) and for modeling the behavior of agro-hydrosystems ; - providing frequent variables characterizing soil and vegetation properties that allows us to monitor the status of crops, their production potential, their irrigation requirements. This monitoring is a highly strategic issue, both for forecasting purposes, food security and good resource management ; - the possibility, from the same information, of assessing the contribution of agricultural lands to net emissions of CO2 and other greenhouse gases (GHGs) ; this assessment is essential for proposing alternative agricultural scenarios for mitigating the contribution of croplands to climate change ; - thanks to the fine spatial and temporal resolution of information, the possibility of providing a decision support for farming activities according to the intra-field variability (precision farming). This dimension represents an important lever for enabling agricultural systems to achieve better efficiency and economical use of inputs for an agriculture that respects the environment