TY - GEN
T1 - A comprehensive agricultural drought stress monitoring method integrating MODIS and weather data
T2 - 2013 2nd International Conference on Agro-Geoinformatics: Information for Sustainable Agriculture, Agro-Geoinformatics 2013
AU - Peng, Chunming
AU - Di, Liping
AU - Deng, Meixia
AU - Han, Weiguo
AU - Yagci, Ali
PY - 2013
Y1 - 2013
N2 - This article aims to propose a new method that detects the occurrence and severity of agricultural drought by evaluating and investigating vegetation performance of and drought stress upon croplands using remote sensing techniques combined with station based observations. It is based on the assumption that crops suffer from agricultural drought stress only when vegetation and water stresses exist at the same time. Merely using NDVI or VCI can hardly determine whether a vegetation stress is drought related, because a lower NDVIIVCI could mean that the crop land is suffering from vegetation stress due to many reasons - drought, flood, extreme temperature, wild fre, pesticides or lack of fertilizers, etc. In conclusion, this research is designed to combine visible/near-infrared/shortwave-infrared remote sensing approaches (namely, from MODIS products) for vegetation drought stress estimation, based on a pre-knowledge of the crop type per pixel, and to validate the estimated result with the observed temperature, precipitation, and soil moisture levels. The new approach is to provide more accurate result than a single vegetation index used alone.
AB - This article aims to propose a new method that detects the occurrence and severity of agricultural drought by evaluating and investigating vegetation performance of and drought stress upon croplands using remote sensing techniques combined with station based observations. It is based on the assumption that crops suffer from agricultural drought stress only when vegetation and water stresses exist at the same time. Merely using NDVI or VCI can hardly determine whether a vegetation stress is drought related, because a lower NDVIIVCI could mean that the crop land is suffering from vegetation stress due to many reasons - drought, flood, extreme temperature, wild fre, pesticides or lack of fertilizers, etc. In conclusion, this research is designed to combine visible/near-infrared/shortwave-infrared remote sensing approaches (namely, from MODIS products) for vegetation drought stress estimation, based on a pre-knowledge of the crop type per pixel, and to validate the estimated result with the observed temperature, precipitation, and soil moisture levels. The new approach is to provide more accurate result than a single vegetation index used alone.
KW - Agricultural drought
KW - Crop mask
KW - False drought signals
KW - GDD
KW - NDDI
KW - NDVI
KW - NDWI
UR - https://www.scopus.com/pages/publications/84888864288
U2 - 10.1109/Argo-Geoinformatics.2013.6621898
DO - 10.1109/Argo-Geoinformatics.2013.6621898
M3 - Conference contribution
AN - SCOPUS:84888864288
SN - 9781479908684
T3 - 2013 2nd International Conference on Agro-Geoinformatics: Information for Sustainable Agriculture, Agro-Geoinformatics 2013
SP - 147
EP - 152
BT - 2013 2nd International Conference on Agro-Geoinformatics
Y2 - 12 August 2013 through 16 August 2013
ER -