Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China

Soil moisture content (SMC), as a pivotal component in the energy and matter exchange processes within the soil-plant-atmosphere continuum, plays a crucial role in surface water dynamics, energy fluxes, and carbon cycling within ecosystems. The development of remote sensing technology has offered new perspectives for monitoring soil moisture at regional scales. Unmanned aerial vehicles (UAV) equip with multispectral have distinct advantages for vegetation monitoring, including rapidity and cost-effectiveness, which has superior applicability and practicality. Therefore, in a 5a "Daya" late-maturing citrus orchard, the vegetation index (VI) and texture feature (TF) information of citrus canopy based on UAV multi-spectral images were extracted, and soil and plant analyzer development (SPAD) of citrus was collected. These different data sources were integrated into the framework of the random forest algorithm (RF) and genetic algorithm-optimized random forest (GA-RF) to evaluate the accuracy of surface SMC (SSMC) estimation in citrus orchard. The Biswas model was utilized to simulate the root zone SMC (RSMC). The spatiotemporal variations of SMC in citrus orchard were analyzed, and the potential of low-cost sensor-equipped drones in rapidly acquiring spatial and temporal distribution information of SMC at a large regional scale was explored. The results indicated that the GA-RF models outperformed the RF models in estimating citrus orchard SMC (with R 2 ranging from 0.502 to 0.949 and RMSE ranging from 0.552 % to 3.166 % for GA-RF, compared to R 2 ranging from 0.430 to 0.936 and RMSE ranging from 0.587 % to 3.449 % for the RF). The GA-RF models using VI+SPAD as inputs exhibited the best performance for SMC at depths of 5 cm, 10 cm, 20 cm and 40 cm (SMC5, SMC10, SMC20 and SMC40) across citrus growth stages (R 2 ranging from 0.793 to 0.949 at 5 cm, R 2 ranging from 0.702 to 0.938 at 10 cm, R 2 ranging from 0.714 to 0.927 at 20 cm). In bud bust to flowering, young fruit and fruit maturation stages (stage Ⅰ, ⅠⅠ and ⅠⅤ), all models demonstrated good accuracy in estimating SMC at depth of 10 cm (R 2 ranging from 0.567 to 0.908 in stage Ⅰ, with R 2 ranging from 0.681 to 0.916 in stage ⅠⅠ and R 2 ranging from 0.579 to 0.938 in stage ⅠⅤ). In fruit expansion stage (stage III), the models performed best in predicting SMC5 (R 2 ranging from 0.698 to 0.861). The Biswas model was constructed to simulate SMC40 by utilizing the inverted SMC10 and SMC20, thereby generating spatiotemporal distribution maps of SMC at different depths in citrus orchard. The SSMC was susceptible to environmental factors, exhibiting significant spatiotemporal heterogeneity. In summary, this study illustrated that the integration of multiple data sources into GA-RF enhanced the estimation performance of SMC at different growth stages of late-maturing citrus orchard in the Southwest China. Additionally, it enabled the rapid and efficient monitoring of spatiotemporal variations in SMC, providing an effective method and practical foundation for precision irrigation and improved water use efficiency. • The GA-RF models exhibited promising potential for estimating surface soil moisture content (SSMC). • Introducing citrus SPAD into machine learning framework can improve the inversion accuracy of SSMC. • The Biswas model has good accuracy in estimating root surface soil moisture content for citrus. • The GA-RF model using vegetation index +SPAD as inputs have practical applicability in the monitoring of citrus.