MCCN Case Study 2 - Spatial projection via modelled data

The MCCN project is to deliver tools to assist the agricultural sector to understand crop-environment relationships, specifically by facilitating generation of data cubes for spatiotemporal data. This repository contains Jupyter notebooks to demonstrate the functionality of the MCCN data cube components.The dataset contains input files for the case study (source_data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (results), and Jupyter Notebook (MCCN-CASE 2.ipynb)<b>Research Activity Identifier (RAiD)</b>RAiD: https://doi.org/10.26292/8679d473<b>Case Studies</b>This repository contains code and sample data for the following case studies. Note that the analyses here are to demonstrate the software and result should not be considered scientifically or statistically meaningful. No effort has been made to address bias in samples, and sample data may not be available at sufficient density to warrant analysis. All case studies end with generation of an RO-Crate data package including the source data, the notebook and generated outputs, including netcdf exports of the datacubes themselves.<b>Case Study 2 - Spatial projection via modelled data</b><b>Description</b>Estimate soil pH and electrical conductivity at 45 cm depth across a farm based on values collected from soil samples. This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the pykrige and rioxarray packages.<b>Data sources</b><b>BradGinns_SOIL2004_SoilData.csv</b> - Soil measurements from the University of Sydney Llara Campey farm site from 2004, corresponding to sites L1, L3 and L4 describing mid-depth, soil apparent electrical conductivity (ECa), GammaK, Clay, Silt, Sand, pH and soil electrical conductivity (EC)<b>Llara_Campey_field_boundaries_poly.shp</b> - Field boundary shapes for the University of Sydney Llara Campey farm site<b>Dependencies</b>This notebook requires Python 3.10 or higherInstall relevant Python libraries with: <b>pip install mccn-engine rocrate rioxarray pykrige</b>Installing mccn-engine will install other dependencies<b>Overview</b>Select soil sample measurements for pH or EC at 45 cm depthSplit sample measurements into 80% subset to model interpolated layers and 20% to test interpolated layersGenerate STAC metadata for layersLoad data cubeInterpolate pH and EC across site using the 80% subset and three different 2D interpolation methods from rioxarray (nearest, linear and cubic) and one from pykrige (linear)Calculate the error between each layer of interpolated values and measured values for the 20% setaside for testingCompare the mean and standard deviation of the errors for each interpolation methodClean up and package results as RO-Crate<b>Notes</b>The granularity of variability in soil data significantly compromises all methodsDepending on the 80/20 split, different methods may appear more reliable, but the pykrige linear method is most often best<br>

MCCN项目旨在开发工具以助力农业领域理解作物与环境的关联，具体可辅助生成时空数据的数据立方体（data cube）。本仓库包含用于演示MCCN数据立方体组件功能的Jupyter笔记本（Jupyter Notebook）。本数据集包含用于案例研究的输入文件（source_data）、RO-Crate元数据（ro-crate-metadata.json）、案例研究的结果（results）以及Jupyter笔记本（MCCN-CASE 2.ipynb）。
<b>研究活动标识符（Research Activity Identifier, RAiD）</b>
RAiD: https://doi.org/10.26292/8679d473
<b>案例研究</b>
本仓库包含以下案例研究的代码与示例数据。请注意，此处的分析仅用于演示软件功能，所得结果不具备科学或统计学层面的参考价值。本项目未针对样本偏差进行处理，且示例数据的采样密度可能不足以支撑有效分析。所有案例研究最终都会生成RO-Crate数据包，其中包含源数据、笔记本以及生成的输出结果，包括数据立方体的netCDF（Network Common Data Form, netCDF）导出文件。
<b>案例研究2——基于建模数据的空间投影</b>
<b>描述</b>
基于采集的土壤样本数据，估算某农场45厘米深度处的土壤pH值与电导率。本研究演示以下内容：1）使用时空资产目录（SpatioTemporal Asset Catalog, STAC）描述空间资产；2）将异构数据源加载至数据立方体中；3）借助pykrige与rioxarray库提供的不同算法，在xarray中实现空间投影。
<b>数据来源</b>
<b>BradGinns_SOIL2004_SoilData.csv</b>——2004年悉尼大学Llara Campey农场站点的土壤测量数据，对应L1、L3、L4站点，测量指标包括中等深度土壤表观电导率（ECa）、GammaK、黏土含量、粉砂含量、砂土含量、pH值以及土壤电导率（EC）。
<b>Llara_Campey_field_boundaries_poly.shp</b>——悉尼大学Llara Campey农场站点的田界矢量形状文件。
<b>依赖项</b>
本笔记本需使用Python 3.10及以上版本。可通过以下命令安装相关Python库：<b>pip install mccn-engine rocrate rioxarray pykrige</b>。安装mccn-engine将自动安装其余依赖项。
<b>概述</b>
选取45厘米深度处的pH值或电导率土壤样本测量值；将样本测量数据划分为80%的建模子集与20%的测试子集，分别用于构建插值图层与验证插值图层；为各图层生成STAC元数据；加载数据立方体；使用80%建模子集，通过rioxarray提供的三种二维插值方法（最邻近、线性、三次插值）以及pykrige提供的线性插值方法，对站点区域的pH值与电导率进行空间插值；针对预留的20%测试子集，计算各插值图层的预测值与实测值之间的误差；对比各插值方法的误差均值与标准差；整理结果并打包为RO-Crate数据包。
<b>注意事项</b>
土壤数据的变异性粒度会显著削弱所有插值方法的性能；根据80/20划分的子集不同，各方法的表现可靠性可能存在差异，但pykrige线性插值方法通常表现最优。