Agricultural Land

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

Limited bio-productive land coupled with progressing land degradation, rising population, increasingly meat-prone diets and a growing demand for biofuels cause substantial land use conflicts between food, fiber and energy production. The additional pressure of on-going climate change and increasing extreme meteorological events also threatens ecosystem services like biodiversity. To protect natural ecosystems while meeting the growing demand for agricultural commodities global land productivity must be substantially increased.

The challenge of sustainably meeting global agricultural demands involves a wide range of land management aspects, including selecting suitable crops and cultivars, monitoring and improving water productivity, promotion of organic farming, fertilizer optimization and plant protection, soil conservation, and efficient irrigation systems.

EnMAP data enable the application of more complex approaches that fully exploit the continuous spectral information provided by imaging spectrometers. One important approach making use of the continuous spectral information are invertible leaf and canopy radiative transfer models (RTM). RTMs infer biochemical/biophysical parameters from continuous canopy spectral reflectance and have been successfully applied to field crops and grasslands. Parameters can be assimilated into agro-ecological models allows for an explicit simulation of crop growth, development, and yield using spatially heterogeneous hyperspectral data.

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

  • Development and improvement of accurate, robust and reliable biophysical variable retrieval methods based on inversion of improved canopy RTMs using imaging spectroscopy data and being computationally supported by machine learning algorithms (crop type, LAI, APAR, chlorophyll content, plant water content, canopy geometrical structure);
  • Development and improvement of methods for the quantitative mapping of soil variables, also taking the spectral signal of green and senescent vegetation into account, that will allow for tracing the gradual enrichment of soil organic carbon as part of soil melioration processes;
  • Development and improvement of approaches to derive complex canopy variables, e.g., canopy nitrogen/protein content, crop phenology, management intensity, yield gaps, non-photosynthetic plant tissue and crop residues, from hyperspectral remote sensing data in conjunction with ancillary remotely sensed data;
  • Development of operational methods for biomass, yield mass and yield quality estimation and forecasting based on hyperspectral remote sensing and ancillary data in combination with complex process models;
  • Mapping of crop species distribution using time series of hyperspectral information;
  • Distinguishing of crop stressors like nitrogen or other micro- and macronutrients deficiency, crop disease, insect infestation, water stress, and chlorosis; and
  • Development and improvement of approaches to assimilate remote sensing derived spatial distributions of vegetation and soil variables into dynamic agro-ecological models, which can serve as a basis for integrated information-driven agricultural management systems
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