Pixel-based spatiotemporal statistics from remotely sensed imagery improves spatial soil predictions and sampling strategies of alluvial soils

Authors: MANCINI, MARCELO - University Of Arkansas; Winzeler, Hans - Edwin; Blackstock, Joshua; Owens, Phillip; MILLER, DAVID - University Of Arkansas; SILVA, SERGIO - Federal University Of Lavras; Ashworth, Amanda

Submitted to: Geoderma
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/19/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: The sun's radiation reflecting off the earth's surface and captured and recorded by satellites can be useful for estimating soil properties that influence the success of agriculture. We studied the relationships between this reflected radiation and the amount of carbon in a topsoil in agricultural fields in the Mississippi floodplain. We found that the relationship between the amount of radiation reflected by the earth in these locations was related to the amount of carbon in the soils and the effectiveness with which the soils hold plant nutrients (CEC). We explored the spatial variability of the carbon and the CEC over the landscape we studied. We found that the carbon and the CEC changed within and between agricultural fields, but that both soil properties could be predicted by examining the radiation reflectance data recorded by the satellites with some success. This research will improve the ways estimates of soil carbon are made, helping to account for the influence of terrestrial carbon pertaining to climate change. It will also improve estimates of soil fertility and field variability, helping farmers to produce more feed, fuel, and fiber per acre of land.

Technical Abstract: Alluvial plains are vexing landscapes for soil mapping and spatial soil property predictions. Alluvial sediments often exhibit unpredictable spatial variability from both fluvial and anthropogenic disturbance. The determination of optimal number of soil sampling points for capturing soil variability remains a persistent issue for mapping and monitoring soil conditions. Here, soil organic matter (SOM) and cation exchange capacity (CEC) were estimated in an alluvial plain from a dense array of 2145 soil samples over 250 ha. The primary goals were to i) use pixel-based statistics from time-series Sentinel-2 satellite reflectance data to estimate SOM and CEC; ii) evaluate use of images with vegetation cover versus bare soil images; and, iii) investigate the optimal number of sampling points to map alluvial soils using different sampling strategies. The optimal sample density was 1 sample per 2.5 ha based on the overlapping of prediction distributions (OV > 0.9). Conditioned Latin Hypercube Sampling (cLHS) was the most efficient sampling strategy. Random grid sampling provided the least consistent results. The use of cLHS coupled with the mean and standard deviation bands resulted in optimal sampling strategy. Pixel-based statistics from readily available satellite imagery captured persistent soil-reflectance relationships that enabled the use of row crops as proxies to predict soil properties. The combined use of pixel-based statistics as inputs to cLHS can reduce the chances of oversampling and associated costs. Pixel-based statistics provided persistent spatial information that can enhance precision agriculture management, carbon mapping, and is likely applicable to other soil properties and remotely sensed products. Use of pixel-based statistics from Sentinel-2 and similar global-coverage imagery could provide readily-calculable inputs for improved soil mapping and more optimal site sample selections in other agricultural row crop areas, globally.