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This study presents a novel Spectral Triad feature selection (STfs) technique based on music theory and compares it to the entire Sentinel-2 feature space and Random Forest-Recursive Feature Elimination (RF-RFE). The optimal subsets were evaluated with Random Forest for retrieving Leaf Area Index (LAI), Leaf Chlorophyll Content (LCab), and Canopy Chlorophyll Content (CCC) in a semi-arid agricultural landscape. The results indicated that the proposed STfs algorithm obtained equivalent or better (i.e. by 1 - 3%) retrieval accuracies for LAI (R2 cv of 66%, root mean squared error of cross-validation [RMSEcv] of 0.53 m2 m-2), LCab (R2 cv: 74%, RMSEcv: 7.09 mg cm-2) and CCC (R2 cv: 77%, RMSEcv: 33.69 mg cm-2), using only 5, 7 and 7 variables, respectively, when compared to RF-RFE and entire Sentinel-2 feature space. Overall, the proposed STfs algorithm has great potential to optimize the spectral feature space of quasi-hyperspectral sensors for rapid crop biophysical and biochemical parameter retrieval. [ABSTRACT FROM AUTHOR]

Rot in commercial timber reduces the value of the wood substantially and estimating the occurrence, severity, and volume of heartwood rot would be a useful tool in decision-making to minimize economic losses. Remotely sensed data has recently been used for mapping rot on a single-tree level, and although the results have been relatively poor, some potential has been shown. This study applied area-based approaches to predict rot occurrence, rot severity, and rot volume , at an area level. Ground reference data were collected from harvester operations in 2019–2021. Predictor variables were calculated from multi-temporal remotely sensed data together with environmental variables. Response variables from the harvester data and predictor variables from remotely sensed data were aggregated to grid cells and to forest stands. Random Forest models were built for the different combinations of response variables and predictor subsets, and validated with both random- and spatial cross-validation. The results showed that it was not possible to estimate rot occurrence and rot severity with the applied modeling procedure (pR2: 0.00–0.16), without spatially close training data. The better performance of rot volume models (pR2: 0.12–0.37) was mainly due to the correlation between timber volume and rot volume. [ABSTRACT FROM AUTHOR]

Coastal dunes are considered some of the most threatened and vulnerable habitats in the European Union. Mapping the spatial distribution of these habitats is an essential task for their conservation. Advances in Unoccupied Aerial Vehicles (UAVs) facilitate the flexible acquisition of high-resolution imagery for identifying detailed spatial distributions of habitats within dune systems. This study aimed to assess the effectiveness of UAV remote sensing for mapping these habitat types. Specifically, we determined the impact of temporally acquired UAV-derived spectral and topographic information on classification accuracy. The work combined the multi-temporal UAV imagery with field observation data and used the Random Forest machine learning algorithm to classify dune habitats. Results showed that using multi-temporal UAV imagery increased classification accuracy compared to using uni-temporal UAV imagery (92.37% vs. 84.09%, respectively). Also, including topographic information consistently improved accuracy, regardless of the number of image sets used (the highest accuracy increased from 84.81% to 92.57% for a uni-temporal model). Temporal analyses showed that the data acquired in the middle period of the growing season were better than those acquired in the early or late periods. The methodology presented here demonstrates the potential of using UAV data for detailed mapping and monitoring of habitat types. [ABSTRACT FROM AUTHOR]

Remote sensing and land resource surveys have been used in recent decades for land use/land cover (LULC) mapping; however, keeping the developed LULC up-to-date and consistent with land survey statistics remains challenging. This study developed a practical and effective framework to automatically update existing LULC products and bridge the gap between remote sensing classification results and land survey data. This study employed Landsat imagery time series, change detection algorithms, sample migration, and random forests to develop a framework for updating existing LULC products in China from 1980–2015 to 1980–2022. The updated LULC maps reflect the post-2015 LULC changes well and maintain continuity with the pre-2015 products. Additionally, a statistical space allocation method based on the minimum cross-entropy strategy was proposed to optimize the LULC maps, increasing the correlation coefficient (r) with China's second and third national land survey statistics from 0.41–0.89 to 0.86–0.99. Thus, the framework and products developed in this study provide valuable tools for sustainable land use and policy planning. [ABSTRACT FROM AUTHOR]

The extraction of large dam candidate regions is critical for broad-scale efforts to rapidly detect large-area dams. The framework proposed in this paper attempts to combine random forest classification models and spatial analysis methods with large dam candidate area extraction methods for large-scale areas. First, we studied the combination of optical, microwave, texture, and topographic features of the dam and constructed a multisource remote sensing and topographic feature vector of the dam. Secondly, we constructed random forest classifiers in different study areas and evaluate their performance. Then we explored the geographic characteristics of the dams and their relationships with other features. Finally, we introduced the spatial analysis method to constrain the large dam candidate area. The proposed framework was tested in a total area of 968,533 km2 in five countries and achieved promising results, which constrained the candidate area to less than 1.06% of the total area. We calculated the completeness rate of large dams using the multi-source dam datasets. The framework achieved a completeness rate of more than 97.62%. Our results show that the entire framework is reliable for automated and fast large dam candidate area acquisition based on data from open remote sensing products. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a model, called the Daily Downward Shortwave Radiation Random Forest Model over Rugged Terrain (DSRMT), to accurately calculate the downward shortwave radiation over a terrain without bright surface on clear days at a daily scale (DSRdaily−rugged). It was built by using the random forest method based on the comprehensive samples from CERES4_SYN1deg_Ed4A within 17 typical mountainous regions. DSRMT could directly estimate DSRdaily-rugged from the instantaneous direct and diffuse solar radiation on a flat surface during 10:30–14:30hrs on each day by comparing with the terrain factors from a digital elevation model, broadband albedo from the Global Land Surface Satellite, and ancillary information. The in-situ validation results showed that it generally delivered superior performance in estimating DSRdaily-rugged at any time during 10:30–14:30hrs, especially at noon, yielding a validated root mean-squared error (RMSE) of 24.90–29.22 Wm−2 and mean absolute error (MAE) of 19.16–22.94 Wm−2, and the average weighted DSRdaily-rugged were usually more accurate with the RMSE and MAE of 21.63 and 17.14 Wm−2. Overall, DSRMT was found to deliver satisfactory performance because of its high accuracy, robustness, ease of implementation, and efficiency, so it has the strong potential to be widely used in practice. [ABSTRACT FROM AUTHOR]

Satellite images are widely used for crop yield estimation, but their coarse spatial resolution means that they often fail to provide detailed information at the field scale. Recently, a new generation of high-resolution satellites and CubeSat platforms has been launched. In this study, satellite data sources including PlanetScope and Sentinel-2 were combined with topographic and climatic variables, and the improvement in wheat yield estimation was evaluated. Wheat yield data from a combine harvester were used to train and validate a yield estimation model based on random forest regression. Nine vegetation indices (NDVI, GNDVI, MSAVI2, MTVI2, MTCI, reNDVI, SAVI, EVI and WDVI) and spectral bands were tested. During the model training, the Sentinel-2 data realized a slightly higher estimation accuracy than the PlanetScope data. However, combining environmental data with the PlanetScope data realized the highest estimation accuracy. For the validated models, adding the topographic and climatic datasets to the satellite data sources improved the estimation accuracy, and the results were slightly better with the Sentinel-2 data than with the PlanetScope data. Observation data of the milk and dough stages provided the highest estimation accuracy of the wheat yield at 210–225 days after sowing. [ABSTRACT FROM AUTHOR]

Groundwater contamination is a serious health problem affected by land use/land cover (LULC) changes. Therefore, this study aims to investigate the impacts of LULC on groundwater levels and quality in the Emirate of Abu Dhabi over the past two decades. Two sets of Landsat images were used to classify LULC using the random forest (RF) classifier, while an image-different tool was used to monitor changes in LULC from 2000 to 2020. After that, a spatial analysis was performed by comparing LULC maps against the hydrological maps. The results show a rapid increase in all LULC classes, whereas the groundwater level has depleted by 40 m. The results also show that the area of high-water quality across the study area was reduced by 479 km2 (0.79%). The results reveal significant hydrological changes in response to rapid urbanization. [ABSTRACT FROM AUTHOR]

Mapping the spatial distribution of soil organic carbon (SOC) in lands covered by tropical forests is important to understand the relationship and dynamics of SOC in this type of ecosystem. In this study, the Random Forest (RF) algorithm was used to map SOC stocks of topsoil (0–15 cm) in forest lands of the Dominican Republic. The methodology was developed using geospatial datasets available in the Google Earth Engine (GEE) platform combined with a set of 268 soil samples. Twenty environmental covariates were analyzed, including climate, topography, and vegetation. The results indicate that Model A (combining all 20 covariates) was only marginally better than Model B (combining topographic and climatic covariates), and Model C (only combining multispectral remote sensing data derived from Landsat 8 OLI images). Model A and Model B yielded SOC mean values of 110.35 and 110.87 Mg C ha−1, respectively. Model A reported the lowest prediction error and uncertainty with an R2 of 0.83, an RMSE of 35.02 Mg C ha−1. There was a strong dependence of SOC stocks on multispectral remote sensing data. Therefore, multispectral remote sensing proved accurate to map SOC stocks in forest ecosystems in the region. [ABSTRACT FROM AUTHOR]

This study aims to predict channel unit types (CUTs) by combining remotely sensed data with morphological variables using machine learning algorithms (random forest, support vector machines, multiple adaptive regression splines, extreme gradient boosting and adaptive boosting) within the Upper Ogun River Basin, Southwestern Nigeria. In achieving the aim of this study, we identified the most important variable(s) in CUT discrimination using the random forest – recursive feature elimination (RF-RFE). A total of 249 cross-sections across 83 reaches were sampled during the fieldwork. Landsat 8 and Sentinel-1 bands were retrieved for days the fieldwork was carried and mosaiced using the Google Earth Engine platform. The RF-RFE identified five top variables (accuracy: 0.79 ± 0.14; kappa: 0.39) discriminating the CUT as dimensionless stream power, slope, width, wetted perimeter and Band 4. In essence, there is much hope in the use of remote sensing in CUT mapping at the reach scale. [ABSTRACT FROM AUTHOR]

Snow and cloud discrimination is a main factor contributing to errors in satellite-based snow cover. To address the error, satellite-based snow cover performs snow reclassification tests on the cloud pixels of the cloud mask, but the error still remains. Machine Learning (ML) has recently been applied to remote sensing to calculate satellite-based meteorological data, and its utility has been demonstrated. In this study, snow and cloud discrimination errors were analyzed for GK-2A/AMI snow cover, and ML models (Random Forest and Deep Neural Network) were applied to accurately distinguish snow and clouds. The ML-based snow reclassified was integrated with the GK-2A/AMI snow cover through post-processing. We used the S-NPP/VIIRS snow cover and ASOS in situ snow observation data, which are satellite-based snow cover and ground truth data, as validation data to evaluate whether the snow/cloud discrimination is improved. The ML-based integrated snow cover detected 33–53% more snow compared to the GK-2A/AMI snow cover. In terms of performance, the F1-score and overall accuracy of the GK-2A/AMI snow cover was 73.06% and 89.99%, respectively, and those of the integrated snow cover were 76.78–78.28% and 90.93–91.26%, respectively. [ABSTRACT FROM AUTHOR]