Remote sensing of canopy water status of the irrigated winter wheat fields and the paired anomaly analyses on the spectral vegetation indices and grain yields

Agriculture is the world's largest consumer of freshwater resources, particularly in semi-arid regions where crop production is reliant on both irrigation and rainfall. Therefore, proper irrigation management is critical in achieving sustainable agriculture by increasing crop yield while conserving water resources. Remote sensing has demonstrated a great promise in monitoring crop status including crop water status based on the spectral vegetation index (VI). Therefore, the vegetation growth (normalized difference vegetation index, NDVI), vegetation water status (shortwave crop reflectance index, SCRI), and vegetation drought stress (moisture stress index, MSI) indices were calculated to assess the impact of irrigation management on canopy water status in a cluster of winter wheat fields in a semi-arid area in three non-consecutive growing seasons with different seasonal rainfall amounts and distributions. The winter wheat fields were typically irrigated six times in each growing season according to a fixed phenological-based irrigation scheduling. Results showed that NDVI, SCRI, and MSI had a high correlation with the remotely sensed locally calibrated leaf area index (LAI), among which NDVI had the strongest correlation (r = 0.9). The analysis revealed that the combined use of VIs succeeded in detecting spatial and temporal crop drought stress levels during the growing seasons. Furthermore, the normalized difference water index (NDWI) was interpreted to quantitatively classify the level and extent of drought stress in the winter wheat fields. The results of the NDWI analysis revealed that 62%, 100%, and 72% of the irrigated winter wheat fields have experienced some levels of drought stress during the normal growing season with wet spring, normal growing season with wet autumn, and dry growing season, respectively. The drought stress was basically due to the lack of effective rainfall during spring in March and April when crops have the highest vegetative growth, irrespective of the rainfall amount during autumn and winter. This revealed the importance of timely irrigation management during spring time. In addition, paired anomaly analysis of the NDVI, MSI, and SCRI with the wheat grain yields could identify good and poor wheat fields and recognize proper management zones of the wheat fields in terms of potential grain production. The findings of this study demonstrated that remote sensing is a strong and reliable tool in irrigation management to help sustain food production in arid or semi-arid areas with limited water resources.