Recipient Organization
MISSISSIPPI STATE UNIV
(N/A)
MISSISSIPPI STATE,MS 39762
Performing Department
(N/A)
Non Technical Summary
Soil sampling is a key practice for gathering field-specific data that supports decisions in soil health assessment and management. Successful stratified sampling design relies on the amount and quality of the preliminary data to determine the management zones, but this site-specific information is difficult and costly to acquire, and spatially limited over time, especially for small and medium-sized farmers. There is a critical need for an innovative, science-driven, and easy-to-use decision tool to support soil sampling plans. This research project will develop and validate a new operational large-area Satellite-based Soil Sampling Design Tool (S3DTool) using historical crop variability analysis and auxiliary data at field-scale. The proposed research will include four tasks: (1) generate multi-temporal composites of spectral vegetation index from 10-m Sentinel-2 MSI for sub-field crop variability analysis, (2) extract crop fields from 1-m NAIP imagery using automated deep learning algorithm, (3) develop a S3DTool algorithm to efficiently derive site-specific management zones, and (4) demonstrate and validate the S3DTool in Mississippi State University Farms. Project deliverables are the annual 10-m crop peak growth composite database for sub-field variability analysis; high-resolution quality-controlled field boundaries database; a validated soil sampling design tool that provides operational zone management delineation; and distribution of collected soil analysis results in different soil conditions. These proposal outcomes will contribute for soil health and agricultural sustainability by promoting a robust, validated, timely and significant tool that can enhance soil assessment and management decisions and increase the economic feasibility of soil testing in heterogeneous areas.
Animal Health Component
50%
Research Effort Categories
Basic
0%
Applied
50%
Developmental
50%
Goals / Objectives
The major goal of this project is todevelop and validate a new satellite-based soil sampling design tool, called S3DTool, using historical crop variability analysis from contemporaneous satellite observations and auxiliary data. The prototype tool will be developed and validated in the state of Mississippi. The specificobjectives of this project are:Objective1: Generate multi-temporal composites of spectral vegetation index from 10-m Sentinel-2 MSI for sub-field crop variability analysisObjective2: Extract crop fields from 1m NAIP imagery using automated deep learning algorithmObjective3: Develop a satellite-based soil sampling design tool (S3DTool) to efficiently derive site-specific management zonesObjective4: Demonstrate and validate the S3DTool in Mississippi State University Farms
Project Methods
There are four main objectives and each one has a specific method.Objective 1:Generate multi-temporal composites of spectral vegetation index from 10-m Sentinel-2 MSI for sub-field crop variability analysisThe annual Sentinel-2 EVI composite will be generated between the start and end dates of peak growth in each year (2015 - 2022). First, we will construct a cloud-free time series data of EVI for each pixel. Second, we will fit a harmonic model in the EVI time series, and then derive the start of peak (SOP) and end of peak (EOP) dates from fitted harmonic values, considering only values >75% EVI amplitude.We will generate a cloud-free median EVI composite between SOP and EOP at each gridded pixel location to create a set of temporal information-embedded metric image.Annual EVI composites will be generated for entire Mississippi between 2015 and 2022.Objective 2:Extract crop fields from 1-m NAIP imagery using automated deep learning algorithmWe will develop a deep learning instance segmentation for field extraction from annual 1-m NAIP imagery.The U-Net-ID model will be trained with input variables as annual cloud-free NAIP false-color bands and output layer as field boundaries that will be manually generated by the research team.We will perform a quality assessment of classified field boundary results by visual inspection, and if needed, more samples will be included to re-train the model in landscape conditions of low model confidence.After a few iterations, the minimum error is expected in the final result, and the field boundary maps will be integratedfor sub-field analysis.Objective 3:Develop a satellite-based soil sampling design tool (S3DT) to efficiently derive field-specific management zonesWe will develop a S3DTool at field level by integrating the multi-temporal peak growth EVI composites and auxiliary soil and topographic attributes for management zone delimitation and random sampling point per zone.First, Principal Component Analysis (PCA) will be applied to annual EVI composites, soil, and topographic variables, and the first principal component, which typically has >90% of data variance, will be used to characterize the spatial variability of aggregated variables.Second, the first principal component will be the input in K-means clustering algorithm for management zone delineation.The resulting clusters can be used to identify management zones (MZs), i.e. within-field areas with similar characteristics (Moral et al., 2010). Optimal number of management zones will be defined using Silhouette analysis with different number of clusters.The Silhouette method estimates the clustering performance by measuring the similarity of the data within its cluster by comparing it to the data of other clusters.Once we defined the management zones, random sampling point is generated within each zone. The S3DTool intends to be effective and simple, and a minimum set of input parameters will be proposed: (i) minimum distance between sampling points (default: 50 m), (ii) minimum distance from field edge (default: 10 m), (iii) minimum area of each management zone (default: 2.5 acres), and (iv) number of sampling point per zone (default: 1). Once we have all verified, sampling locations are projected in the field area and exported in shapefile and excel formats.Objective 4:Demonstrate and validate the S3DTool in Mississippi State University FarmsWe will demonstrate and validate the new S3DTool across Mississippi State University research farms. A very dense soil sampling survey of soil texture and chemical properties (0 to 15 cm depth) will be sampled across a grid with a regular distance of 50 m for different soil and crop types, typically, corn, soybean, and cotton. Careful sampling will be performed to maximize the representation of soil properties, and each sampling location will be geo-referenced using a GPS device with a locational error <2 m. We will follow the recommended soil health indicators in NRCS-2018-0006 technical note, and soil testing analysis will include wet aggregate stability, soil organic C content, pH, moisture content, texture, bulk density, nitrogen, and phosphorus concentration (Co-I Wijewardane expertise). The ground-truth soil result variables will be interpolated with kriging method and the clustering analysis will be performed with k-means. The confusion matrix and accuracy metrics of S3DTool and ground-truth management zones will be estimated, and ANOVA to test the difference among the management zones to inform the ability of the proposed tool in representing soil variability.