论文信息 - Generating continuous information products on land use and the intensity of agricultural production from high resolution satellite data

Generating continuous information products on land use and the intensity of agricultural production from high resolution satellite data

For a sustainable and efficient land management, spatially distributed and up-to-date information on the land surface is of central importance. A continuous flow of land management information is the basis to improve decisions on use, cultivation intensity and allocation of resources (e.g. water for irrigation). Remote sensing is in a unique position to contribute to this task as it is globally available and provides specific information about current crop status. The M4Land concept is designed to derive information products for a sustainable management of the land surface. In this paper, the methodology and the results for the autonomous development of three products, i.e. land use, crop cycle and intensity of agricultural use, is presented for two test sites and for several years. In the M4Land system, a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal and multi-sensoral satellite images.

[1] W. Mauser,et al. PROMET - large scale distributed hydrological modelling to study the impact of climate change on the water flows of mountain watersheds. , 2009 .

[2] Wolfram Mauser,et al. Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe , 2015, Remote. Sens..

[3] W. Verhoef,et al. Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data , 2007 .

[4] W. Mauser,et al. Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity , 2015 .

[5] Wolfram Mauser,et al. Inversion of a canopy reflectance model using hyperspectral imagery for monitoring wheat growth and estimating yield , 2009, Precision Agriculture.

[6] W. Verhoef,et al. Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models , 2003 .

[7] Wolfram Mauser,et al. Land management monitoring of near-natural areas through an integrated analysis of multi-temporal satellite data in a model framework , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).