Estimation of soil water holding capacity with Random Forests for drought monitoring

Soil moisture is a key variable for drought monitoring but soil moisture measurements networks are very scarce. Land-surface models can provide a valuable alternative to simulate soil moisture dynamics, but only a few countries have such modelling schemes implemented for monitoring soil moisture at high spatial resolution. In this study, a soil moisture accounting model (SMA) was regionalized over the Iberian Peninsula, taking as a reference the soil moisture simulated by a high-resolution land surface model. To estimate soil water holding capacity, the parameter required to run the SMA model, two approaches were compared: the direct estimation from European soil maps using pedotransfer functions, or an indirect estimation by a Machine Learning approach, Random Forests, using as predictors altitude, temperature, precipitation, evapotranspiration and land use. Results showed that the Random Forest model estimates are more robust, especially for estimating low soil moisture levels. Consequently, the proposed approach can provide an efficient way to simulate daily soil moisture and therefore monitor soil moisture droughts, in contexts where high-resolution soil maps are not available, as it relies on a set of covariates that can be reliably estimated from global databases.

Evaluating tree growth factors into species-specific functional soil maps for improved agroforestry system efficiency

Agroforestry systems play an important role in sustainable agroecosystems. However, accurately and adequately quantifying the relationships between environmental factors and tree growth in these systems are still lacking. Objectives of this study were to quantify environmental factors affecting growth of four tree species and to develop functional soil maps (FSM) for each species in an agroforestry site. The diameter at breast height, absolute growth rate (AGR), and neighborhood competition index of 259 trees from four species (northern red oak [Quercus rubra], pecan [Carya illinoinensis], cottonwood [Populus deltoides], and sycamore [Platanus occidentalis]) were determined. A total of 51 topsoil samples were collected and analyzed, and 12 terrain attributes were derived from the digital elevation model. The relationships between AGR, soil, topography, and tree size were analyzed using Spearman correlation. Based on correlation analysis, FSM for each species were generated using the k-means cluster method by overlaying correlated soil and terrain attribute maps. Results showed tree size and terrain attributes were driving factors affecting tree growth rate relative to soil properties. The spatial variations in AGR among functional units were statistically compared within tree species and the areas with larger AGR were identified by the FSM. This study demonstrated that FSM could delineate areas with different AGR for the oak, cottonwood, and sycamore trees. The AGR of pecan trees did not vary among functional units. The generated FSM may allow land managers to more precisely establish and manage agroforestry systems.

Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (R2), and less effective for AK at only 8% and 6% (R2). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (R2). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels.

Monitoring and Analysis of Degraded Land in Rasulabad Block of Kanpur Dehat District Using Remote Sensing and GIS Techniques

Land degradation is seen as a development or additional that reduces current and/or potential soil capability to produce products and goods. This implies a decline from a higher to a lower state due to a decline in land capacity, productivity, and biodiversity loss. This can be both natural and human-induced. Natural causes embody earthquakes, tsunamis, droughts, avalanches, landslides, volcanic eruptions, floods, tornadoes, and wildfires. Whereas human-induced soil degradation results from land clearing and deforestation, inappropriate agricultural practices, improper management of industrial effluents and wastes, over-grazing, careless management of forests, surface mining, urban sprawl, and commercial/industrial development. Inappropriate agricultural practices embody excessive tillage and use of heavy machineries, excessive and unbalanced use of inorganic fertilizers, poor irrigation and water management techniques, chemical or pesticide overuse, inadequate crop residue and organic carbon inputs, and poor crop cycle planning. Some underlying social causes of soil degradation in Asian nation square measure land shortage, decline in per capita land handiness, economic pressure onto land, land occupancy, poverty, and population increase.. The aim of the current study is to prepare baseline data to combat land degradation and conserve land resources in an economical and efficient manner. To assess land degradation with the help of Remote Sensing (RS) and Geographical Information System (GIS) – in Rasulabad Block of Kanpur Dehat district, Uttar Pradesh, different levels of analysis were performed to estimate the extent of land. Degradation to assess saline or salt-free soils and calcareous or sodium soils and to match this data with satellite studies. The spatial variability of these soil parameters was shown in soil maps created in a GIS environment. A temporary study of the 2017 and 2021 Sentinel satellite datasets was done to find the parameters that are responsible for land degradation. The severity of land degradation was calculable quantitatively by analyzing the physico-chemical parameters within the laboratory to see salinity and sodicity of soils and further correlating them with satellite-based studies. The pH varied between 7.1 and 8.2, electrical conductivity (EC) between 0.23 and 0.6 miliSiemens/m and the methyl orange or total alkalinity between 0.095 and 0.225 (HCO3 ) gL-1 as CaCO3. The spatial variability in these soil parameters was pictured through soil maps generated in a GIS environment with the help of IDW Interpolation. The results revealed that the soil in the study area was exposed to salt intrusion, most of the soil samples of the study area were slightly or moderately saline with a few salt-free sites. Moreover, the majority of the soil samples were calcareous and a few samples were alkaline or sodic in nature. Keyword: Land degradation, Sodic land, Saline land, GIS, IDW Interpolation.