Crop Drought Stress Monitoring by Remote Sensing

Extreme temperatures and water shortage can cause drought stress of crops. Drought effects an important crops are being studied and methods of monitoring and early detection of this and other stress factors are being developed at many places. The aim is to allow to plan long and short term agro-technical measures (e.g. in crop rotation, fertilization, soil cultivation, irrigation scheduling) to avoid reduction in crop production. Additional concern is caused by eventual consequences of climatic change an agricultural production, which is becoming one of the key issues of climate change impact research. Earth observation from satellites (remote sensing) is one of the key techniques for crop state monitoring over large areas. New sensor systems have been developed and brought into orbit in recent time, opening up new possibilities also for crop drought stress monitoring. The main new features of optical sensors are high spectral resolution (small bandwidth down to 10 nm high number of spectral bands up to a few hundred, which in effect facilitates spectroscopy from space), high spatial resolution (pixel sizes an the ground down to 60 cm), and high temporal resolution (up to daily revisit frequency of every spot of the globe). The aim of this project is to adapt and develop remote sensing based methods of detection and monitoring of drought stress of agricultural crops exploiting these new Potentials of optical remote sensing and the synergetic effects of the different sensor types. To this end, physical vegetation canopy models describing the relationship between drought stress level and reflectance characteristics of the plants are being adapted and improved. Methods for analysing remotely sensed images making use of the vegetation canopy models are being developed. Both reflected radiation and emitted (thermal) infrared radiation are used. As there is no sensor available today that can simultaneously fulfill the three above-mentioned requirements of high spectral, spatial and temporal resolution, special attention is paid to the Problem of combining data from different sensors (image information fusion). The methods are applied and tested for selected crops (wheat and maize) under agricultural conditions in Austria and in Germany.

Extreme temperatures and water shortage can cause drought stress of crops. Drought effects an important crops are being studied and methods of monitoring and early detection of this and other stress factors are being developed at many places. The aim is to allow to plan long and short term agro-technical measures (e.g. in crop rotation, fertilization, soil cultivation, irrigation scheduling) to avoid reduction in crop production. Additional concern is caused by eventual consequences of climatic change an agricultural production, which is becoming one of the key issues of climate change impact research. Earth observation from satellites (remote sensing) is one of the key techniques for crop state monitoring over large areas. New sensor systems have been developed and brought into orbit in recent time, opening up new possibilities also for crop drought stress monitoring. The main new features of optical sensors are high spectral resolution (small bandwidth down to 10 nm high number of spectral bands up to a few hundred, which in effect facilitates spectroscopy from space), high spatial resolution (pixel sizes an the ground down to 60 cm), and high temporal resolution (up to daily revisit frequency of every spot of the globe). The aim of this project is to adapt and develop remote sensing based methods of detection and monitoring of drought stress of agricultural crops exploiting these new Potentials of optical remote sensing and the synergetic effects of the different sensor types. To this end, physical vegetation canopy models describing the relationship between drought stress level and reflectance characteristics of the plants are being adapted and improved. Methods for analysing remotely sensed images making use of the vegetation canopy models are being developed. Both reflected radiation and emitted (thermal) infrared radiation are used. As there is no sensor available today that can simultaneously fulfill the three above-mentioned requirements of high spectral, spatial and temporal resolution, special attention is paid to the Problem of combining data from different sensors (image information fusion). The methods are applied and tested for selected crops (wheat and maize) under agricultural conditions in Austria and in Germany.