A Data Driven Approach using the SMAP-Sentinel High-Resolution Soil Moisture Product for Agricultural Application

Abstract:

Effective agricultural water management requires timely and optimal information on the availability and use of irrigation water. However, most existing information that show irrigation need lack the objectivity and spatial representativeness required for operational water management and meaningful identification of specific regions/farmlands that demand irrigation. The SMAP mission has started providing high-resolution (1 km) soil moisture product by using the high-resolution Sentinel 1A/1B SAR data in the active-passive algorithm. The current temporal resolution (revisit interval) of the SMAP-Sentinel high-resolution soil moisture product (L2SMSP) is 12 days in most parts of the world except over Europe where it is 6 days due to better Sentinel-1A/1B temporal resolution. The high spatial resolution (1 km) of the L2SMSP product enables many agricultural applications so far not possible through other SMAP soil moisture products. We demonstrate an agricultural application of the L2SMSP product over the California Central Valley with mixed farming and Iowa agricultural landscapes. Besides the SMAP-Sentinel L2SMSP soil moisture at 1 km, the agricultural application also uses other important high resolution (≤ 1 km) data, such as 1) crop growing-degree-day (GDD); 2) normalized difference vegetation index (NDVI) data, and; 3) Landcover map. These datasets (L2SMSP, GDD, and NDVI) are combined to obtain a water demand map for agricultural fields in the study regions. The water demand maps can guide the resource managers to focus on high water-stressed regions or specific agricultural fields to allocate water for irrigation to improve crop health and ultimately maximize the crop yield.