Abstract
Irrigation is not well represented in land surface, hydrological, and climate models. One way to account for irrigation is by assimilating satellite soil moisture data that contains irrigation signal with land surface models. In this study, the irrigation detection ability of SMAP enhanced 9 km and SMAP-Sentinel 1 (SMAP-S1), 3 km and 1 km soil moisture products are evaluated using the first moment (mean) and the second moment (variability) of soil moisture data. The SMAP enhanced 9 km soil moisture product lacks irrigation signals in an irrigated plain south of Urmia Lake, whereas SMAP-S1 products record irrigation signal in soil moisture variability. Despite observing higher variability over irrigated areas, there are only small and inconsistent wet biases observed over irrigated pixels relative to nearby nonirrigated pixels during the irrigation season. This is partly attributable to the climatology vegetation water content used in the SMAP-S1 soil moisture retrieval algorithm that is not accounting for crop rotation and land management. Thus, in the second part of this study, we updated the retrieval algorithm to use dynamic vegetation water content. The update increased vegetation water content up to 1 kg/m2 which corresponds with a 0.05 cm3/cm3 increase in soil moisture during irrigation season. The update does not notably change soil moisture retrievals off season. This study shows that irrigation signals are present in both the first and second moment of soil moisture time series, and employing dynamic vegetation water content in the SMAP-S1 algorithm can enhance the irrigation signal over agricultural regions.
Highlights
IRRIGATION is the largest human intervention in the water cycle, accounting for 70% of global freshwater withdrawals [1] that deplete groundwater reservoirs [2]–[4] while altering the terrestrial water and energy budgets [5], [6]
The impacts associated with the vegetation water content (VWC) update are assessed by i) comparing dynamic VWC with climatology VWC and ii) comparing original SMAP-S1 with SMAP-S1
This study provides background knowledge about irrigation signatures from fine-scale remotely sensed soil moisture that will inform an ongoing study where the modified (SMAP-S1 dyn VWC) product will be assimilated within an land surface models (LSM) using a particle batch smoother to estimate irrigation water use over the Mahabad Plain (Fig. 3)
Summary
IRRIGATION is the largest human intervention in the water cycle, accounting for 70% of global freshwater withdrawals [1] that deplete groundwater reservoirs [2]–[4] while altering the terrestrial water and energy budgets [5], [6]. Irrigation has been poorly represented in models [12] because parametrization of irrigation requires ancillary data—irrigation method, land use/cover, irrigation timing and frequency, and crop phenology—which is difficult to estimate [8]. Another way to include irrigation in modeling is to assimilate soil moisture (SM) data that contains irrigation signal [12]–[14]. The coarse resolution of microwave satellite data (>25km) has made it difficult to resolve irrigation at small agricultural practices where irrigation is applied periodically (a common practice in most of the irrigated cropland around the world) [15]–[18]. The footprint of irrigation application is often smaller than microwave SM resolution
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