Tucker, C. J., & Choudhury, B. J. (1987). Satellite remote sensing of drought conditions. Remote
Sensing of Environment, 23(2), 243–251. https://doi.org/10.1016/0034-4257(87)90040-X.
Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S., Figa-Saldaña, J., et al.
(2013). The ASCAT soil moisture product: A review of its specifications, validation results, and
emerging applications. Meteorologische Zeitschrift, 22(1), 5–33. https://doi.org/10.1127/09412948/2013/0399.
Wang, K., Li, Z., & Cribb, M. (2006). Estimation of evaporative fraction from a combination of day
and night land surface temperatures and NDVI: A new method to determine the Priestley–
Taylor parameter. Remote Sensing of Environment, 102(3–4), 293–305. https://doi.org/10.1016/
j.rse.2006.02.007.
Wilhite, D. (2005). Drought. In J. E. Oliver (Ed.), Encyclopedia of world climatology (Encyclopedia of world climatology) (1st ed.). Dordrecht: Springer. https://doi.org/10.1007/1-4020-32668_70.
Yagci, A. L. (2015). The effect of corn–soybean rotation on the NDVI-based drought indicators: A
case study in Iowa, USA, using vegetation condition index. GIScience & Remote Sensing, 52(3),
290–314. https://doi.org/10.1080/15481603.2015.1038427.
Yagci, A. L., Di, L., Deng, M., Han, W., & Peng, C. (2011). Agricultural drought monitoring from
space using freely available MODIS data. In Proceedings of 18th William T. Pecora Memorial
Remote Sensing Symposium. Herndon: American Society of Photogrammetry and Remote
Sensing (ASPRS)ASPRS.
Yagci, A. L., Di, L., Deng, M., Yu, G., & Peng, C. (2012). Global Agricultural Drought Mapping:
Results for the Year 2011. In 2012 IEEE International Geoscience and Remote Sensing
Symposium (pp. 3764–3767). Munich: IEEE. https://doi.org/10.1109/IGARSS.2012.6350498.
Yagci, A. L., Di, L., & Deng, M. (2013). The effect of land-cover change on vegetation greennessbased satellite agricultural drought indicators: A case study in the southwest climate division of
Indiana, USA. International Journal of Remote Sensing, 34(20), 6947–6968. https://doi.org/10.
1080/01431161.2013.810824.
Yagci, A. L., Santanello, J. A., Rodell, M., Deng, M., & Di, L. (2016). Detecting the 2012 drought
in the southeastern us with Modis- and grace-based drought indicators. In P. George (Ed.),
Remote sensing of hydro-meteorological hazards. Petropoulos and Tanvir Islam: CRC Press
(in Press).
Yagci, A. L., Santanello, J. A., Jones, J. W., & Barr, J. (2017). Estimating evaporative fraction from
readily obtainable variables in mangrove forests of the Everglades, U.S.A. International Journal
of Remote Sensing, 38(14), 3981–4007. https://doi.org/10.1080/01431161.2017.1312033.
Yilmaz, M. T., DelSole, T., & Houser, P. R. (2011). Improving land data assimilation performance
with a water budget constraint. Journal of Hydrometeorology, 12(5), 1040–1055. https://doi.
org/10.1175/2011JHM1346.1.
Yilmaz, M., Tugrul, W., Crow, T., Anderson, M. C., & Hain, C. (2012). An objective methodology
for merging satellite- and model-based soil moisture products. Water Resources Research, 48
(11), W11502. https://doi.org/10.1029/2011WR011682.
320
A. L. Yagci and M. T. Yilmaz
Sensing of Environment, 23(2), 243–251. https://doi.org/10.1016/0034-4257(87)90040-X.
Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S., Figa-Saldaña, J., et al.
(2013). The ASCAT soil moisture product: A review of its specifications, validation results, and
emerging applications. Meteorologische Zeitschrift, 22(1), 5–33. https://doi.org/10.1127/09412948/2013/0399.
Wang, K., Li, Z., & Cribb, M. (2006). Estimation of evaporative fraction from a combination of day
and night land surface temperatures and NDVI: A new method to determine the Priestley–
Taylor parameter. Remote Sensing of Environment, 102(3–4), 293–305. https://doi.org/10.1016/
j.rse.2006.02.007.
Wilhite, D. (2005). Drought. In J. E. Oliver (Ed.), Encyclopedia of world climatology (Encyclopedia of world climatology) (1st ed.). Dordrecht: Springer. https://doi.org/10.1007/1-4020-32668_70.
Yagci, A. L. (2015). The effect of corn–soybean rotation on the NDVI-based drought indicators: A
case study in Iowa, USA, using vegetation condition index. GIScience & Remote Sensing, 52(3),
290–314. https://doi.org/10.1080/15481603.2015.1038427.
Yagci, A. L., Di, L., Deng, M., Han, W., & Peng, C. (2011). Agricultural drought monitoring from
space using freely available MODIS data. In Proceedings of 18th William T. Pecora Memorial
Remote Sensing Symposium. Herndon: American Society of Photogrammetry and Remote
Sensing (ASPRS)ASPRS.
Yagci, A. L., Di, L., Deng, M., Yu, G., & Peng, C. (2012). Global Agricultural Drought Mapping:
Results for the Year 2011. In 2012 IEEE International Geoscience and Remote Sensing
Symposium (pp. 3764–3767). Munich: IEEE. https://doi.org/10.1109/IGARSS.2012.6350498.
Yagci, A. L., Di, L., & Deng, M. (2013). The effect of land-cover change on vegetation greennessbased satellite agricultural drought indicators: A case study in the southwest climate division of
Indiana, USA. International Journal of Remote Sensing, 34(20), 6947–6968. https://doi.org/10.
1080/01431161.2013.810824.
Yagci, A. L., Santanello, J. A., Rodell, M., Deng, M., & Di, L. (2016). Detecting the 2012 drought
in the southeastern us with Modis- and grace-based drought indicators. In P. George (Ed.),
Remote sensing of hydro-meteorological hazards. Petropoulos and Tanvir Islam: CRC Press
(in Press).
Yagci, A. L., Santanello, J. A., Jones, J. W., & Barr, J. (2017). Estimating evaporative fraction from
readily obtainable variables in mangrove forests of the Everglades, U.S.A. International Journal
of Remote Sensing, 38(14), 3981–4007. https://doi.org/10.1080/01431161.2017.1312033.
Yilmaz, M. T., DelSole, T., & Houser, P. R. (2011). Improving land data assimilation performance
with a water budget constraint. Journal of Hydrometeorology, 12(5), 1040–1055. https://doi.
org/10.1175/2011JHM1346.1.
Yilmaz, M., Tugrul, W., Crow, T., Anderson, M. C., & Hain, C. (2012). An objective methodology
for merging satellite- and model-based soil moisture products. Water Resources Research, 48
(11), W11502. https://doi.org/10.1029/2011WR011682.
320
A. L. Yagci and M. T. Yilmaz