Soil Line Influences on Two-Band Vegetation Indices and Vegetation Isolines: A Numerical Study

Extraction of vegetation information from remotely sensed images has remained a long-term challenge due to the influence of soil background. To reduce this effect, the slope and intercept of the soil line (SL) should be known to calculate SL-related vegetation indices (VIs). These VIs can be used to estimate the biophysical parameters of agricultural crops. However, it is a difficult task to retrieve the SL parameters under the vegetation canopy. A feasible method for retrieving these parameters involves extracting the bottom boundary line in two-dimensional spectral spaces (i.e., red and near-infrared bands). In this study, the slope and intercept of the SL was extracted from Landsat 8 OLI images of a test site in northeastern Germany. Different statistical methods, including the Red-NIR min method, quantile regression method (using a floating tau with the smallest p-value), and a new approach proposed in this paper using a fixed quantile tau known as the diffuse non-interceptance (DIFN) value, were applied to retrieve the SL parameters. The DIFN value describes the amount of light visible below the canopy that reaches the soil surface. Therefore, this value can be used as a threshold for retrieving the bottom soil line. The simulated SLs were compared with actual ones extracted from ground truth data, as recorded by a handheld spectrometer, and were also compared with the SL retrieved from bare soil pixels of the Landsat 8 image collected after harvest. Subsequently, the SL parameters were used to separately estimate the dry biomasses of winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.) at the local and field scales using different SL-related vegetation indices. The SL can be retrieved more accurately at the local scale compared with the field scale, and its simulation can be critical in the field due to significant differences from the actual SL. Moreover, the slope and intercept of the simulated SLs found using the floating and fixed quantile tau (slope ≈ 1.1 and intercept ≈ 0.05) show better agreement with the actual SL parameters (slope ≈ 1.2 and intercept ≈ 0.03) in the late growing stages (i.e., end of ripening and senescence stages) of crops. The slope and intercept of the soil line extracted from bare soil pixels of the Landsat 8 OLI data after harvest (slope = 1.3, intercept = 0.03, and R 2 = 0.94) are similar to those of the simulated SL. The correlation coefficient (R 2 ) of the simulated SLs are greater than 0.97 during different growing stage and all of the SL parameters are statistically significant (p < 0.05) at the local scale. The results also imply the need for different vegetation indices to best retrieve the crop biomass depending on the growing stage, but relatively small differences in performances were observed in this study.

Section: Discussionmentioning

confidence: 48%

A New Concept of Soil Line Retrieval from Landsat 8 Images for Estimating Plant Biophysical Parameters

Ahmadian

Demattê

et al. 2016

“…The value of M changes according to various soil types; in this paper, it was set to 1.16 based on previous studies [34,35,[45][46][47][48][49]. The extraction of Pixels B and C occurs via the following simplified steps: (1) exclude the water bodies and fully cloud-covered pixels in the study area; (2) make sure the remaining pixels are successfully radiometrically corrected; (3) traverse the remaining pixels, compute their distances to L0 (Figure 3b) and extract the reflectance of each pixel with the minimum and maximum distance; and (4) terminate if the ratio of R N IR relating to R red is less than the threshold value (set to 2 based on the maximum slope of soil line in NIR-red triangle space); otherwise, repeat Step (3).…”

Section: Nsmi: Normalized Soil Moisture Indexmentioning

confidence: 99%

NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on OzNet in Southeastern Australia

Chen

Zhang

2017

Abstract:To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. This NRSD method was validated with the data from in situ OzNet network in May and September 2015. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m 3 /m 3 and 0.12 m 3 /m 3 in May and September, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies.

“…Another uncertainty from this study is that there are two soil types (chernozemic soil and solonetzic soil) in the study area. As Yoshioka, et al [40] mentioned in their research, the general soil line of the local area caused slight difference for each soil type with an unique soil line. However, the positive aspect is that there are two soil types in this study area and chernozemic soil is the main soil type.…”

Section: Comparison Of Soil Line Extracted From Images With Soil Linementioning

confidence: 87%

“…In this study, PVI is not suitable for either green biomass or green cover estimation. Based on the research of Yoshioka, et al [40], the influence of soil background on NDVI decreases when Leaf Area Index (LAI) increases. The average LAI from the research conducted is higher than 1.0, which means the effects of soil background on NDVI is quite low.…”

Section: Relatively Better Time Period For Extracting Soil Linementioning

confidence: 99%

A Study of Soil Line Simulation from Landsat Images in Mixed Grassland

Guo

2013

Abstract:The mixed grassland in Canada is characterized by low to medium green vegetation cover, with a large amount of canopy background, such as non-photosynthetic vegetation residuals (litter), bare soil, and ground level biological crust. It is a challenge to extract the canopy information from satellite images because of the influence of canopy background. Therefore, this study aims to extract a soil line, a representation of bare soil with litter and soil crust in the surface, from Landsat images to reduce the background effect. Field work was conducted in the West Block of Grasslands National Park (GNP) in Canada, which represents the northern mixed grassland from late June to early July 2005. Six TM images with either no or only a small amount of cloud content were collected in 2005. In this study, soil lines were extracted directly from images by quantile regression and the (R, NIR min ) method. The results show that, (1) both cloud and cloud shadow have obvious influence on simulating soil line automatically from images; (2) green up and late senescence seasons are relatively better for soil line simulation; (3) the (R, NIR min ) method is better for soil line simulation than quantile regression to extract green biomass or green cover information.