Deriving Field Scale Soil Moisture from Satellite Observations and Ground Measurements in a Hilly Agricultural Region

Abstract

Agricultural and hydrological applications could greatly benefit from soil moisture (SM) information at sub-field resolution and (sub-) daily revisit time. However, current operational satellite missions provide soil moisture information at either lower spatial or temporal resolution. Here, we downscale coarse resolution (25–36 km) satellite SM products with quasi-daily resolution to the field scale (30 m) using the random forest (RF) machine learning algorithm. RF models are trained with remotely sensed SM and ancillary variables on soil texture, topography, and vegetation cover against SM measured in the field. The approach is developed and tested in an agricultural catchment equipped with a high-density network of low-cost SM sensors. Our results show a strong consistency between the downscaled and observed SM spatio-temporal patterns. We found that topography has higher predictive power for downscaling than soil texture, due to the hilly landscape of the study area. Furthermore, including a proxy of vegetation cover results in considerable improvements of the performance. Increasing the training set size leads to significant gain in the model skill and expanding the training set is likely to further enhance the accuracy. When only limited in-situ measurements are available as training data, increasing the number of sensor locations should be favored over expanding the duration of the measurements for improved downscaling performance. In this regard, we show the potential of low-cost sensors as a practical and cost-effective solution for gathering the necessary observations. Overall, our findings highlight the suitability of using ground measurements in conjunction with machine learning to derive high spatially resolved SM maps from coarse-scale satellite products.

Evaluating the suitability of the consumer low-cost Parrot Flower Power soil moisture sensor for scientific environmental applications

“…In general, these characteristics hold the potential for many scientific applications in which a large number and high density of sensors are otherwise not feasible with professional sensors, e.g. Zappa et al (2020), and Zappa et al (2019).…”

A New Approach for Soil Moisture Downscaling in the Presence of Seasonal Difference

“…What is more, the in-situ measurements are at point scale, while remote sensing products are at the 1-km-pixel scale. In the study of verifying remote sensing data with measured data, there is always unreasonable matching in the space scale [20]. These factors may all affect the accuracy of validation.…”

“…Machine learning methods can express well this regression correlation [16]. The machine learning algorithms used for downscaling include random forest (RF), support vector machines (SVM), K-nearest neighbor (KNN) [16][17][18][19][20]. These algorithms have their own characteristics, so it is necessary to choose the most suitable algorithm for the research area [19].…”

Reply on CC1

“…Similarly, one can deploy a temporary low-cost sensors network to identify the most suitable location(s) for long-term monitoring. Such locations could then be equipped with professional sensors, while moving the low-cost network to other sites (Zappa et al, 2019). Another exciting opportunity offered by lowcost sensors is the deployment of networks in low-income countries.…”