Assessment of land productivity potential in Belarus

Note: This project was completed with the support of the Alliance of International Science Organizations (ANSO) Joint Research Cooperation Program under the Belt and Road Initiative.

Project Background

Data preparation and fusion were performed using Sentinel Hub and Google Earth Engine. Training samples were labeled using publicly available datasets, publications, and visual interpretation. The supervised learning method OBIA-RF (Object-Oriented Random Forest) was then used to classify and extract land cover features from the agricultural and forestry datasets. Finally, mapping and statistical analysis were conducted. Soil ecological potential was assessed by combining meteorological and soil data.

This article mainly introduces the assessment of land productivity potential in Belarus using a light-temperature factorial model, and it is also the final part of this project.

Based on the basic model, land use classification and vegetation index data were added as supplementary variables, and the weights of each variable were reallocated according to the actual data obtained.

△Figure 1. Flowchart for Land Productivity Potential Assessment

Basic Project Process

Land productivity potential assessment model:

Light-temperature factorial model

The photosynthetic production potential is assessed by combining meteorological, soil, and crop parameters.

Photosynthetic production potential:

Huang Bingwei's official:

*Huang Bingwei (1978)

YQ represents photosynthetic production potential (kg/h㎡).

Q represents the total solar radiation (cal/cm²).

ε represents the proportion of visible light in the 0.38–0.71 μm wavelength range of solar radiation.

α is the reflectivity

β is the leakage rate

ρ represents the ineffective uptake rate of non-photosynthetic organs, taken as 10%.

γ is the light saturation limit

φ is the quantum efficiency, taken as 0.224.

ω represents respiratory efficiency

H represents the energy required to produce 1g of dry matter, with an average value of 4.25kcal.

8% represents the content of inorganic nutrients in the plant.

Photothermal production potential:

R represents precipitation (mm), and E0 represents evaporation (mm).

Land productivity potential:

Nu is the soil nutrient index, and Om is the soil organic matter index.

In the formula: Au, Ap, Ak are the contents of available nitrogen, available phosphorus and available potassium in the soil (mg/kg), respectively, and Om is the organic matter content in the soil.

The model is divided into two main parts: crop assessment and soil assessment. Crop assessment incorporates topographic, meteorological, and vegetation index data, while soil assessment primarily focuses on soil nutrient content. Historical meteorological data is sourced from the ERA5-Land dataset, while soil data comes from the Global Soil Dataset for use in Earth System Models (GSDE). NDVI is calculated using Sentinel-2 and Landsat 7/8 satellites. Annual accumulated temperature and precipitation are calculated based on hourly historical meteorological data and then rasterized. NDVI is synthesized based on the year. Finally, the data is aligned using a standard raster and a light-temperature factorial model is applied to calculate and map the annual land productivity potential index.

Project Results

△ Figure 2. Distribution of land productivity potential in Belarus, 2014–2023

Figure 3. Changes in land productivity potential in various regions of Belarus, 2014–2023

As shown in Figures 2 and 3, the spatial distribution of land productivity potential shows relatively little variation. Between 2014 and 2019, the land productivity potential in each region showed a gradual downward trend. Areas experiencing an increase after 2019 are likely due to increased precipitation. However, the land productivity potential declined significantly in 2022 and 2023, possibly because increased cloud cover affected the quality and availability of satellite imagery, leading to missing or abnormally low NDVI values ​​in some areas, as well as abnormal precipitation in other regions.

Combining land productivity potential distribution data with land use and cover data can provide more in-depth, comprehensive and specific references for regional forest development, the formulation of regional agricultural policies, long-term land use planning, and the implementation of soil remediation and protection projects.