Sampling Designs for Validating Digital Soil Maps: A Review

Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.

Deep convolutional neural network for damaged vegetation segmentation from RGB images based on virtual NIR-channel estimation

Performing accurate and automated semantic segmentation of vegetation is a first algorithmic step towards more complex models that can extract accurate biological information on crop health,weed presence and phenological state,among others.Traditionally,models based on normalized difference vegetation index(NDVI),near infrared channel(NIR)or RGB have been a good indicator of vegetation presence.However,these methods are not suitable for accurately segmenting vegetation showing damage,which precludes their use for downstream phenotyping algorithms.In this paper,we propose a comprehensive method for robust vegetation segmentation in RGB images that can cope with damaged vegetation.The method consists of a first regression convolutional neural network to estimate a virtual NIR channel from an RGB image.Second,we compute two newly proposed vegetation indices from this estimated virtual NIR:the infrared-dark channel subtraction(IDCS)and infrared-dark channel ratio(IDCR)indices.Finally,both the RGB image and the estimated indices are fed into a semantic segmentation deep convolutional neural network to train a model to segment vegetation regardless of damage or condition.The model was tested on 84 plots containing thirteen vegetation species showing different degrees of damage and acquired over 28 days.The results show that the best segmentation is obtained when the input image is augmented with the proposed virtual NIR channel(F1=0:94)and with the proposed IDCR and IDCS vegetation indices(F1=0:95)derived from the estimated NIR channel,while the use of only the image or RGB indices lead to inferior performance(RGB(F1=0:90)NIR(F1=0:82)or NDVI(F1=0:89)channel).The proposed method provides an end-to-end land cover map segmentation method directly from simple RGB images and has been successfully validated in real field conditions.