Assessing early season drought condition using AMSR-E soil moisture product

Abstract

Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture product was used to assess early season drought condition over Maharashtra State of India in summer monsoon (kharif). AMSR-E Soil Moisture Index (AMSR-E SMI) was calculated using AMSR-E soil moisture and soil textural information. The index had normalization to soil matrix and could be composited over a time scale. The index had statistically significant correlation (R 2 value 0.6 to 0.7) with rainfall during early kharif season (June to August). Spatiotemporal dynamics of AMSR-E SMI and its inter annual variability (2007 to 2010) were analysed at meteorological subdivision level. Persistent dryness was found in 2008 (drought year) and soil wetness was near normal in other years. Intermittent short dry spells were also monitored and quantified. Spatio-temporal variation of intensity and persistence of soil wetness as depicted by AMSR-E SMI had good correspondence with progression of sown area over Maharashtra. Geospatial information of sowing period soil wetness condition derived from AMSR-E SMI could be vital supplementary information in assessment of early season drought.

Assessing crop water stress during late kharif season using Normalized Diurnal Difference Vegetation Water Content (nddVWC) of Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E)

“…9. Vidarbha is a semiarid area with limited irrigation support and it faced major drought in the year 2009, early season drought during 2008, and normal condition in 2005 (Chakraborty et al 2013). The intensity of the crop water stress was represented by the vertical axis, and the persistence was shown in horizontal axis (Fig.…”

“…AMSR-E was one of such satellites, which has been extensively used for studying the dynamics of land surface parameters. AMSR-E soil moisture product was successfully used to assess early season drought (June-July) over Indian region, but its sensitivity was found to diminish with the increase in the above ground vegetation from August month onward (Chakraborty et al 2013(Chakraborty et al , 2012. Due to attenuation of passive microwave signal by above ground vegetation, accurate retrieval of soil moisture could be done over bare soil or moderately vegetated soil conditions (Njoku and Entekhabi 1996).…”

Retrieval of Soil Moisture from FengYun-3D Microwave Radiation Imager Operational and Recalibrated Data Using Random Forest Regression

“…Based on these algorithms, several global SM products and datasets have been published, such as SMOS, SMAP, AMSR-E, AMSR2 Advanced Scatterometer (ASCAT) and the European Space Agency Climate Change Initiative (ESA CCI). These data have brought great benefits to agricultural and hydrological monitoring, weather forecasting, and climate change research [29][30][31]. However, SM retrieval based on a radiative transfer model using L-band requires a lot of auxiliary information, such as the vegetation optical thickness, vegetation single scattering albedo, soil effective temperature, and soil surface roughness, which limits the global application of these methods [32].…”

Satellite characterization of drought over cultivated areas in Central America

“…Drought has been monitored using remotely sensed data produced by passive satellites owing to advantages of all-weather observations, such as the advanced microwave scanning radiometer (Chakraborty et al 2013;Liu et al 2017b;Mladenova et al 2014), soil moisture and ocean salinity satellite (Martínez-Fernández et al 2016;Kerr et al 2016;Kędzior and Zawadzki 2017), and soil moisture active passive (SMAP) measurements (Mishra et al 2017;Velpuri, Senay, and Morisette 2016;Liu et al 2017a). These microwave sensors reveal some advantages, including wide imaging swath (1000-1400 km) and highly revisited coverage on a daily basis.…”