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The analysis and policymaking of national salt production require quality Salt Land maps with more detailed object information. This study successfully classified Salt Land into Active and Inactive based on pansharpening images using random forest algorithm. The results show: 1) the best pansharpening method is UNB PanSharp; 2) the texture variables have more significant contributions to enhance classification accuracy; 3) scheme 5 is the most efficient and recommended scheme in research, achieves an OA of 66.699% and a Kappa coefficient of 39.4%. This study can provide a good reference for developing Salt Land mapping and policies for practitioners or stakeholders. [ABSTRACT FROM AUTHOR]

Pearl millet (Pennisetum glaucum L.R.Br.), is the most widely cultivated food crop after rice, wheat, and maize. The aim of the project is to determine the crop acreage of Pearl millet (Bajra) using Sentinel-1A SAR data and Machine Learning Algorithm to determine the yield estimation of the Pearl millet crop at the tehsil level using the Monteith approach. The classification overall accuracy is found to be 86.48% for Agra district and 80.15% for Firozabad district. The Relative Deviation of yield estimation for the Agra and Firozabad districts is found to be 10.14 and 6, respectively. [ABSTRACT FROM AUTHOR]

The efficient monitoring of foliar nutrient such as the C:N ratio is important for sustainable rangeland productivity. Recent advances in Earth observation technology provide multispectral sensors with high spatial, spectral and temporal resolution suitable for modelling foliar nutrients. This study explored the fusion of Sentinel-2 and PlanetScope datasets using the pixel fusion technique and the random forest algorithm. The result in this study is vital in understanding the value of image fusion in enhancing rangeland nutrient estimation for optimal productivity. The study concludes that the fusion of optical sensors requires further investigation. [ABSTRACT FROM AUTHOR]

Rice is one of the most essential food crops in the world and accurate estimation of rice yield is a major content of agricultural research. Recently, scholars have used machine learning algorithms for rice yield estimation. However, there are few studies on rice yield prediction based on rice phenological stages. In this study, a method for rice yield prediction on the basis of rice phenology analysis was proposed. For this study, the cumulative NDVI and EVI based Logistic regression curves were carried out to determine the phenological period. Comparing several regression models, the results of the random forest regression model developed using phenology-based regression analysis performed better. The R2 of training and validation samples were 0.96 and 0.95, respectively, with RMSE of 0.06 ton/ha. This method is feasible for governments to predict rice yield and make farm risk management decisions. [ABSTRACT FROM AUTHOR]

The landscape-level estimation of nitrogen will constitute a significant input in global change models. High resolution broadband satellite technology is a promising solution to the limitation of spatial coverage. This study sought to identify an optimal spatial resolution for estimating foliar nitrogen in dry miombo woodlands. The contrast texture measure, calculated at increasing window size on Sentinel-2 imagery, was related to field measured nitrogen via random forest regression models. Our results show that the optimum size for estimating nitrogen is 50m. This result is significant in understanding the scale at which dynamics in foliar nitrogen concentration can be remotely measured. [ABSTRACT FROM AUTHOR]

Accurate spatial modelling of forest characteristics is one of the most important challenges in remote sensing applications. In this study, we compared the ability of Multiple Linear Regression (MLR), Geographically weighted regression (GWR), and Random Forest (RF) to estimate different forest attributes based on field sample data and Landsat 8 image. CA was modelled with the highest accuracy compared to other variables using GWR. GWR outperformed other methods. The highest and the lowest values of RMSE were for BA using RF (31.0%) and CA using GWR (12.0%), respectively. [ABSTRACT FROM AUTHOR]

In the context of climate change, large-scale net primary productivity (NPP) estimation and its impact feature variables are drawing more and more attention. Traditional process-based and empirical models are limited by their model structure and input variable design. The available field measurement data are limited by their small coverage. We propose to train an NPP calculation model using random forest and quantify the influence of multiple meteorological features on NPP. The calculated NPP correlates well with the MODIS product (correlation coefficient higher than 0.8). The importance rankings of multiple features are related to the local economy and development strategy of research areas. In developed areas, vegetation indexes are the most important, while in developing areas, land classification type influences NPP the most. The experiments suggest random forest is promising for estimating NPP and useful in analysing the impact features in terms of global change. [ABSTRACT FROM AUTHOR]

This paper describes an approach to using the Random Forest classification algorithm to quantitatively evaluate a range of potential image segmentation scale alternatives in order to identify the segmentation scale(s) that best predict land cover classes of interest. The image segmentation scale selection process was used to identify three critical image object scales that when combined produced an optimal level of land cover classification accuracy. Following segmentation scale optimization, the Random Forest classifier was then used to assign land cover classes to 11 scenes of SPOT satellite imagery in North and South Dakota with an average overall accuracy of 85.2 percent. [ABSTRACT FROM AUTHOR]