Agricultural Land

Image: Arcachon (France), simulated EnMAP data based on Sentinel-2 RGB (658 nm, 569 nm, 479 nm)
Source: ESA, GFZ, DLR

Limited land resources, increasing land degradation, rising population numbers, an increasingly meatprone diet, a growing demand for biofuels, and on-going climatic change coupled with more frequent extreme events cause substantial land use conflicts between food- and energy production versus ecosystem services including biodiversity conservation. To sustain the benefits of natural ecosystems, a growing demand for agricultural commodities can only be met by sustainable increases in land productivity.

Modern farming practices try to integrate the identification, analysis, and management of spatial and temporal variability within regions and agricultural fields for optimum profitability, sustainability, and protection of the environment. Therefore, understanding of farming related land heterogeneity management has progressed towards site-specific management to support sustainable productivity.

EnMAP can substantially support site-specific farming management and decisions. However, to fully exploit the complete spectral information content provided by EnMAP, a more thorough approach is needed. Invertible vegetation canopy reflectance models infer biochemical/biophysical parameters, such as chlorophyll and water content, from continuous canopy spectral reflectance signatures and have previously been applied to field crops and grasslands. The parameters that control the productivity and health of vegetation can be estimated through model inversion using remote optical measurements such as those retrieved from EnMAP. Furthermore, time series of remotely sensed plant parameters account for spatiotemporal heterogeneity in agricultural production models, which can also be used to explore the suitability of different management options.

The following major scientific and application tasks have been identified in agriculture:

  • Develop and improve accurate, robust and reliable crop parameter retrieval methodologies based on inversion of improved canopy reflectance models using imaging spectroscopy data (retrieval of crop type, LAI, APAR, chlorophyll content, plant water content, canopy geometrical structure)
  • Develop and improve methods for the quantitative mapping of soil parameters, also taking the spectral signal of vegetation into account
  • Develop and improve approaches to derive complex canopy parameters, e.g. crop phenology, management intensity, and yield gap, from hyperspectral remote sensing data, in conjunction with ancillary remote sensing data.
  • Develop operational methodologies for yield and biomass estimation and forecasting based on EnMAP and ancillary data.
  • Map crop species distribution using hyperspectral temporal information;
  • Distinguish crop stressors like nitrogen deficiency, crop disease, insect infestation, water stress, and chlorosis, and
  • Develop and improve approaches to assimilate remot-sensing-derived spatial distributions of vegetation and soil parameters into dynamic agro-ecological models.
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