Identification of Agricultural Drought Extent Based on Vegetation Health Indices of Landsat Data: Case of Subang and Karawang, Indonesia

Rojas, 2011, Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery, Remote sensing of Environment, 115, 343, 10.1016/j.rse.2010.09.006

Gu Y, Brown JF, Verdin JP, Wardlow B. A five-years analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters 2007; 34:L06407, doi:10.1029/2006GL029127.

Mishra, 2015, Anatomy of a local-scale drought: Application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study,, Journal of Hydrology, 526, 15, 10.1016/j.jhydrol.2014.10.038

Ji, 2003, Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices, Remote Sensing of Environment, 87, 85, 10.1016/S0034-4257(03)00174-3

Rhee, 2010, Monitoring agricultural drought for arid and humid regions using multi sensor remote sensing data, Remote Sensing of Environment, 114, 2875, 10.1016/j.rse.2010.07.005

Sruthi, 2015, Agricultural drought analysis using the NDVI and land surface temperature data; a case study of Raichur District, Aquatic Procedia, 4, 1258, 10.1016/j.aqpro.2015.02.164

Choi, 2013, Evaluation of drought indices via remote sensed data with hydrological variables, Journal of Hydrology, 476, 265, 10.1016/j.jhydrol.2012.10.042

Bhuiyan, 2006, Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data, International Journal of Applied Earth Observation and Geoinformation, 8, 289, 10.1016/j.jag.2006.03.002

Kogan, 1995, Application of vegetation index and brightness temperature for drought detection, Advances in Space Research., 15, 91, 10.1016/0273-1177(95)00079-T

Ghulam, 2007, Modified perpendicular drought index (MPDI): a real time drought monitoring method, ISPRS Journal of Photogrammetry & Remote Sensing, 62, 150, 10.1016/j.isprsjprs.2007.03.002

Nichol JE, Abbas S. Integration of remote sensing datasets for local scale assessment and prediction of drought. Science of the Total Environment 2015;505:503-7.

Chander G, Markham BL, Helder DL. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 2009;113:893-903.

Barsi JA, Barker JL, Schott JR. An atmospheric correction parameter calculator for a single thermal band earth-sensing instrument. Geoscience and Remote Sensing Symposium 2003;5:3014-6.

Coll C, Galve JM, Sanchez JM, Caselles V. Validation of Landsat-7/ETM+ thermal-band calibration and atmospheric correction with ground-based measurements. IEEE Transactions on Geoscience and Remote Sensing 2010;48(1):547-55.

Wang, 2014, Remotely sensed drought index and its responses to meteorological drought in Southwest China, Remote Sensing Letters, 5, 413, 10.1080/2150704X.2014.912768

Kogan, 2001, Operational space technology for global vegetation assessment, Bulletin of the American Meterological Society, 82, 1949, 10.1175/1520-0477(2001)082<1949:OSTFGV>2.3.CO;2

Kogan, 2002, World droughts in the new millenium from AVHRR-based Vegetation Health Indices, Eos Transaction of American Geophysical Union, 83, 562