A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land, Journal Year: 2021, Volume and Issue: 10(3), P. 231 - 231

Published: Feb. 25, 2021

Bare soil is a critical element in the urban landscape and plays an essential role environments. Yet, separation of bare other land cover types using remote sensing techniques remains significant challenge. There are several sensing-based spectral indices for barren detection, but their effectiveness varies depending on patterns climate conditions. Within this research, we introduced modified index (MBI) shortwave infrared (SWIR) near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed was tested two different Thailand Vietnam, where there large areas during agricultural fallow period, obstructing between areas. extracted MBI achieved higher overall accuracy about 98% kappa coefficient over 0.96, compared to (BSI), normalized (NDBaI), dry (DBSI). results also revealed that considerably contributes classification. We suggest detection tropical climatic regions.

Language: Английский

---

Remote sensing of soil degradation: Progress and perspective

International Soil and Water Conservation Research, Journal Year: 2023, Volume and Issue: 11(3), P. 429 - 454

Published: March 15, 2023

Soils constitute one of the most critical natural resources and maintaining their health is vital for agricultural development ecological sustainability, providing many essential ecosystem services. Driven by climatic variations anthropogenic activities, soil degradation has become a global issue that seriously threatens environment food security. Remote sensing (RS) technologies have been widely used to investigate as it highly efficient, time-saving, broad-scope. This review encompasses recent advances state-of-the-art ground, proximal, novel RS techniques in degradation-related studies. We reviewed RS-related indicators could be monitoring properties. The direct (mineral composition, organic matter, surface roughness, moisture content soil) indirect proxies (vegetation condition land use/land cover change) evaluating were comprehensively summarized. results suggest these above are effective degradation, however, no system established date. also discussed RS's mechanisms, data, methods identifying specific phenomena (e.g., erosion, salinization, desertification, contamination). investigated potential relations between Sustainable Development Goals (SDGs) challenges prospective use assessing degradation. To further advance optimize technology, analysis retrieval methods, we identify future research needs directions: (1) multi-scale degradation; (2) availability data; (3) process modelling prediction; (4) shared dataset; (5) decision support systems; (6) rehabilitation degraded resource contribution technology. Because difficult monitor or measure all properties large scale, remotely sensed characterization related particularly important. Although not silver bullet, provides unique benefits studies from regional scales.

Language: Английский

---

Unveiling year-round cropland cover by soil-specific spectral unmixing of Landsat and Sentinel-2 time series

Remote Sensing of Environment, Journal Year: 2025, Volume and Issue: 318, P. 114594 - 114594

Published: Jan. 9, 2025

Language: Английский

---

Assessing the capability of Sentinel-2 time-series to estimate soil organic carbon and clay content at local scale in croplands

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 199, P. 40 - 60

Published: April 3, 2023

The use of remote sensing data methods is affordable for the mapping soil properties plowed layer over croplands. Carried out in framework ongoing STEROPES project European Joint H2020 Program SOIL, this work focused on feasibility Sentinel-2 based approaches high resolution topsoil clay and organic carbon (SOC) contents at within-farm or within-field scales, cropland sites contrasted climates types across Northern hemisphere. Four pixelwise temporal mosaicking methods, using a two years-Sentinel-2 time series several spectral indices (NDVI, NBR2, BSI, S2WI), were developed compared i) pure bare condition (maxBSI), ii) driest (minS2WI), iii) average (Median) iv) dry conditions excluding extreme reflectance values (R90). Three modeling approaches, bands output mosaics as covariates, tested compared: (i) Quantile Regression Forest (QRF) algorithm; (ii) QRF adding longitude latitude covariates (QRFxy); (iii) hybrid approach, Linear Mixed Effect Model (LMEM), that includes spatial autocorrelation properties. We pairs mosaic ten Türkiye, Italy, Lithuania, USA where samples collected SOC content measured lab. RPIQ best performances among test was 2.50 both (RMSE = 0.15%) 3.3%). Both accuracy level uncertainty mainly influenced by site characteristics cloud frequency, management. Generally, models including component (QRFxy LMEM) performing, while mostly Median R90. most frequent optimal combination model type R90 QRFxy SOC, LMEM estimation.

Language: Английский

---

Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites

Remote Sensing, Journal Year: 2021, Volume and Issue: 13(16), P. 3141 - 3141

Published: Aug. 8, 2021

For food security issues or global climate change, there is a growing need for large-scale knowledge of soil organic carbon (SOC) contents in agricultural soils. To capture and quantify SOC at field scale, Earth Observation (EO) can be valuable data source area-wide mapping. The extraction exposed soils from EO challenging due to temporal permanent vegetation cover, the influence moisture condition surface. Compositing techniques multitemporal satellite images provide an alternative retrieve produce source. repeatable composites, containing averaged areas over several years, are relatively independent seasonal surface conditions new EO-based that used estimate large geographical with high spatial resolution. Here, we applied Soil Composite Mapping Processor (SCMaP) Landsat archive between 1984 2014 covering Bavaria, Germany. Compared existing modeling approaches based on single scenes, 30-year SCMaP reflectance composite (SRC) resolution 30 m used. SRC spectral information correlated point using different machine learning algorithms cropland topsoils Bavaria. We developed pre-processing technique address issue combining pixels purpose modeling. methods often studies choose best prediction model. Based model accuracies performances, Random Forest (RF) showed capabilities predict Bavaria (R² = 0.67, RMSE 1.24%, RPD 1.77, CCC 0.78). further validated results dataset. comparison measured predicted mean difference 0.11% RF promising approach distribution extents (30 m).

Language: Английский

---

Soil Reflectance Composites—Improved Thresholding and Performance Evaluation

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(18), P. 4526 - 4526

Published: Sept. 10, 2022

Reflectance composites that capture bare soil pixels from multispectral image data are increasingly being analysed to model constituents such as organic carbon. These temporal used instead of single-date images account for the frequent vegetation cover soils and, thus, get broader spatial coverage pixels. Most compositing techniques require thresholds derived spectral indices Normalised Difference Vegetation Index (NDVI) and Burn Ratio 2 (NBR2) separate all other land types. However, threshold derivation is handled based on expert knowledge a specific area, statistical percentile definitions or in situ data. For operational processors, site-specific partly manual strategies not applicable. There need more generic solution derive large-scale processing without intervention. This study presents novel HIstogram SEparation Threshold (HISET) methodology deriving index testing them Sentinel-2 stack. The technique index-independent, data-driven can be evaluated quality score. We tested HISET building six reflectance (SRC) using NDVI, NBR2 new combining NDVI short-wave infrared (SWIR) band (PV+IR2). A comprehensive analysis performance accuracy resulting SRCs proves flexibility validity HISET. Disturbance effects confusion with non-photosynthetic-active (NPV) could reduced by choosing grassland crops input LC NBR2-based SRC spectra showed highest similarity LUCAS spectra, broadest least number valid observations per pixel. validated against database Integrated Administration Control System (IACS) European Commission. Validation results show PV+IR2-based outperform two indices, especially spectrally mixed areas soil, photosynthetic-active NPV. NDVI-based lowest confidence values (95%) bands. In future, shall different environmental conditions characteristics evaluate if findings this also valid.

Language: Английский

---

Dynamic evaluation of cropland degradation risk by combining multi-temporal remote sensing and geographical data in the Black Soil Region of Jilin Province, China

Applied Geography, Journal Year: 2023, Volume and Issue: 154, P. 102920 - 102920

Published: March 10, 2023

Language: Английский

---

Soil organic carbon mapping utilizing convolutional neural networks and Earth observation data, a case study in Bavaria state Germany

Geoderma, Journal Year: 2024, Volume and Issue: 444, P. 116867 - 116867

Published: March 28, 2024

The Copernicus Sentinel-2 multispectral imagery data may be aggregated to extract large-scale, bare soil, reflectance composites, which enable soil mapping applications. In this paper, approach was tested in the German federal state of Bavaria, provide estimations for organic carbon (SOC). Different temporal ranges were considered generation including multi-annual and seasonal ranges. A novel multi-channel convolutional neural network (CNN) is proposed. By leveraging advantages deep learning techniques, it utilizes complementary information from different spectral pre-treatment techniques. SOC predictions indicated little dissimilarity amongst with best performance attained six-year composite containing only spring months (RMSE = 12.03 g C · kg−1, R2 0.64, RPIQ 0.89). It has been demonstrated that these outcomes outperform other well-known machine An ablation analysis accordingly performed evaluate interplay CNN's components disentangle each aspect proposed framework. Finally, a DUal inPut LearnIng architecture, named DUPLICITE, proposed, concatenates features derived CNN mentioned earlier, as well topographical environmental covariates through an artificial (ANN) exploit their complementarity. improvement overall prediction 11.60 gC 0.67, 0.92).

Language: Английский

---

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

Land, Journal Year: 2023, Volume and Issue: 12(4), P. 855 - 855

Published: April 10, 2023

In order to ensure the sustainability of production from agricultural lands, degradation processes surrounding fertile land environment must be monitored. Human-induced risk and status soil (SD) were assessed in Northern-Eastern part Nile delta using trend analyses for years 2013 2023. SD hotspot areas identified time-series analysis satellite-derived indices as a small fraction difference between observed geostatistical projected data. The method operated on assumption that negative photosynthetic capacity plants is an indicator independently climate variability. Combinations soil, water, vegetation’s integrated achieve goals study. Thirteen profiles dug hotspots areas. was affected by salinity alkalinity risks ranging slight strong, while compaction waterlogging ranged moderate. According GIS-model results, 30% soils subject threats, 50% strong risks, 20% moderate risks. primary human-caused sources are excessive irrigation, poor conservation practices, improper utilisation heavy machines, insufficient drainage. Electrical conductivity (EC), exchangeable percentage (ESP), bulk density (BD), water table depth main causes area. Generally, chemical low, physical very high Trend remote sensing (RSI) proved effective accurate tools monitor environmental dynamic changes. Principal components used compare prioritise among RSI. RSI pixel-wise residual indicated related spatial temporal trends region followed patterns drought, salinity, moisture, difficulties separating impacts drought submerged vegetation capacity. Therefore, future studies desertification should proceed factor predictor analysis.

Language: Английский

---

Synergistic estimation of soil salinity based on Sentinel-1 image texture and Sentinel-2 salinity spectral indices

Journal of Applied Remote Sensing, Journal Year: 2023, Volume and Issue: 17(01)

Published: Jan. 17, 2023

Soil salinization, one of the important factors leading to global land degradation, seriously affects sustainable agricultural development. Accurate and frequent monitoring soil salt content (SSC) contributes management restoration salinized soil. Our study aimed evaluate potential synthetically estimate salinity in bare with different remote sensing sensors (medium-resolution Sentinel-1 Sentinel-2). The data 134 surface samples were collected Shahaoqu irrigation area (SIA) Hetao Irrigation District Inner Mongolia. Simultaneously, images Sentinel-2 SIA obtained. A total 46 predictors, including 12 (10 multispectral bands, 1 VV, VH), 8 polarization combination indices, 16 spectral 10 texture features, obtained calculated from image data. Three machine learning algorithms, random forest (RF), support vector (SVM), extreme (ELM), used for construction prediction models based on combinations predictors. results showed that multiple could be more accurately than a single sensor. Among three regression (RF, SVM, ELM) our study, RF was best model (Rv2=0.71, RMSEv = 0.140 % , MAE 0.094 ). combining SAR imagery most effective revealing spatial distribution salinity. importance analysis predictor variables features main explanatory SSC prediction, contrast being predictor. verified predict by multisource arid semiarid regions, which provide some reference salinization control

Language: Английский

---