A simple approach to estimate coastal soil salinity using digital camera images

Soil Research ◽  
2020 ◽  
Vol 58 (8) ◽  
pp. 737
Author(s):  
Lu Xu ◽  
Raphael A. Viscarra Rossel ◽  
Juhwan Lee ◽  
Zhichun Wang ◽  
Hongyuan Ma
Keyword(s):  
Soil Salinity ◽  
Precision Agriculture ◽  
Mean Squared Error ◽  
Rapid Development ◽  
Salt Content ◽  
Digital Camera ◽  
Soil Surface ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
High Concentration

Soil salinisation is a global problem that hinders the sustainable development of ecosystems and agricultural production. Remote and proximal sensing technologies have been used to effectively evaluate soil salinity over large scales, but research on digital camera images is still lacking. In this study, we propose to relate the pixel brightness of soil surface digital images to the soil salinity information. We photographed the surface of 93 soils in the field at different times and weather conditions, and sampled the corresponding soils for laboratory analyses of soil salinity information. Results showed that the pixel digital numbers were related to soil salinity, especially at the intermediate and higher brightness levels. Based on this relationship, we employed random forest (RF) and partial least-squares regression (PLSR) to model soil salt content and ion concentrations, and applied root mean squared error, coefficient of determination and Lin’s concordance correlation coefficient to evaluate the accuracy of models. We found that ions with high concentration were estimated more accurately than ions with low concentrations, and RF models performed overall better than PLSR models. However, the method is only suitable for bare land of coastal soil, and verification is needed for other conditions. In conclusion, a new approach of using digital camera images has good potential to predict and manage soil salinity in the context of precision agriculture with the rapid development of unmanned aerial vehicles.

scholarly journals A PLSR model to predict soil salinity using Sentinel-2 MSI data

2021 ◽  
Vol 13 (1) ◽  
pp. 977-987
Author(s):  
Ghada Sahbeni
Keyword(s):  
Soil Salinity ◽  
Spectral Characteristics ◽  
Management Strategies ◽  
Salt Content ◽  
Soil Surface ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Remotely Sensed Data ◽  
Sampling Campaign ◽  
Sentinel 2

Abstract Salinization is one of the most widespread environmental threats in arid and semi-arid regions that occur either naturally or artificially within the soil. When exceeding the thresholds, salinity becomes a severe danger, damaging agricultural production, water and soil quality, biodiversity, and infrastructures. This study used spectral indices, including salinity and vegetation indices, Sentinel-2 MSI original bands, and DEM, to model soil salinity in the Great Hungarian Plain. Eighty-one soil samples in the upper 30 cm of the soil surface were collected from vegetated and nonvegetated areas by the Research Institute for Soil Sciences and Agricultural Chemistry (RISSAC). The sampling campaign of salinity monitoring was performed in the dry season to enhance salt spectral characteristics during its accumulation in the subsoil. Hence, applying a partial least squares regression (PLSR) between salt content (g/kg) and remotely sensed data manifested a highly moderate correlation with a coefficient of determination R 2 of 0.68, a p-value of 0.000017, and a root mean square error of 0.22. The final model can be deployed to highlight soil salinity levels in the study area and assist in understanding the efficacy of land management strategies.

scholarly journals The soil-adjusted vegetation index for soil salinity assessment in Uzbekistan

2020 ◽  
Vol 26 (3) ◽  
pp. 324-333
Author(s):  
Nozimjon Teshaev ◽  
Bunyod Mamadaliyev ◽  
Azamjon Ibragimov ◽  
Sayidjakhon Khasanov
Keyword(s):  
Soil Salinity ◽  
Information Technologies ◽  
Vegetation Index ◽  
Arable Land ◽  
Salt Content ◽  
Secondary Data ◽  
Soil Surface ◽  
Soil Salinization ◽  
Arid Zones ◽  
Agronomic Practices

Soil salinization, as one of the threats of land degradation, is the main environmental issue in Uzbekistan due to its aridic climate. One of the most vulnerable areas to soil salinization is Sirdarya province in Uzbekistan. The main human-induced causes of soil salinization are the insufficient operation of drainage and irrigation systems, irregular observations of the agronomic practices, and non-efficient on-farm water use. All of these causes considerably interact with the level of the groundwater, leading to an increase in the level of soil salinity. The availability of historical data on actual soil salinity in agricultural lands helps in formulating validated generic state-of-the-art approaches to control and monitor soil salinization by remote sensing and geo-information technologies. In this paper, we hypothesized that the Soil-Adjusted Vegetation Index-based results in soil salinity assessment give statistically valid illustrations and salinity patterns. As a study area, the Mirzaabad district was taken to monitor soil salinization processes since it is the most susceptible territory of Sirdarya province to soil salinization and provides considerably less agricultural products. We mainly formulated this paper based on the secondary data, as we downloaded satellite images and conducted an experiment against the in-situ method of soil salinity assessment using the Soil-Adjusted Vegetation Index. As a result, highly saline areas decreased by a factor of two during the studied period (2005–2014), while non-saline areas increased remarkably from a negligible value to over 10 000 ha. Our study showed that arable land suitability for agricultural purposes has been improving year by year, and our research held on this district also proved that there was a gradual decrease in high salt contents on the soil surface and land quality has been improved. The methodology has proven to be statistically valid and significant to be applied to other arid zones for the assessment of soil salinity. We assume that our methodology is surely considered as a possible vegetation index to evaluate salt content in arable land of either Uzbekistan or other aridic zones and our hypothesis is not rejected by this research.

scholarly journals Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 229-241
Author(s):  
Marcelo Chan Fu Wei ◽  
Leonardo Felipe Maldaner ◽  
Pedro Medeiros Netto Ottoni ◽  
José Paulo Molin
Keyword(s):  
Precision Agriculture ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Spectral Band ◽  
Model Performance ◽  
Important Predictor ◽  
Predictor Variables ◽  
Coefficient Of Determination ◽  
Statistical Parameters ◽  
Entire Dataset

Carrot yield maps are an essential tool in supporting decision makers in improving their agricultural practices, but they are unconventional and not easy to obtain. The objective was to develop a method to generate a carrot yield map applying a random forest (RF) regression algorithm on a database composed of satellite spectral data and carrot ground-truth yield sampling. Georeferenced carrot yield sampling was carried out and satellite imagery was obtained during crop development. The entire dataset was split into training and test sets. The Gini index was used to find the five most important predictor variables of the model. Statistical parameters used to evaluate model performance were the root mean squared error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). The five most important predictor variables were the near-infrared spectral band at 92 and 79 days after sowing (DAS), green spectral band at 50 DAS and blue spectral band at 92 and 81 DAS. The RF algorithm applied to the entire dataset presented R2, RMSE and MAE values of 0.82, 2.64 Mg ha−1 and 1.74 Mg ha−1, respectively. The method based on RF regression applied to a database composed of spectral bands proved to be accurate and suitable to predict carrot yield.

Study on Soil Salinization Based on GIS and RS

2013 ◽  
Vol 333-335 ◽  
pp. 81-85 ◽  
Author(s):  
Chi Ma
Keyword(s):  
Soil Salinity ◽  
Research Result ◽  
Atmospheric Correction ◽  
Salt Content ◽  
Soil Salinization ◽  
Least Squares Regression ◽  
Correction Model ◽  
First Order ◽  
Gis And Rs ◽  
Songliao Plain

This paper obtained the surface reflectance image by using FLAASH atmospheric correction model to conduct atmospheric correction to the HSI image. After the reflectance image was processed with multi mathematical manipulations, the analytical method of geographic information system (GIS) was applied to carry out the partial least squares regression (PLSR) analysis with the measured value of soil salinity, thus quantitatively inverting the soil salinity in Songliao Plain. The research result indicated that the reflectance of HSI hyperspectral imaging can increase the related coefficient R2 of salt content in saline alkali soil significantly after being processed with reciprocal (1/R) and first-order differentiation (R), reaching 0.818 and 0.851 respectively with a root mean square error (RMSE) of 0.77 and 0.694 respectively, quantitatively and quickly gaining the soil salinity in Songliao Plain as well as salinization prevention.

scholarly journals A study on interannual change features of soil salinity of cotton field with drip irrigation under mulch in Southern Xinjiang

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244404
Author(s):  
Yu Zhang ◽  
Yongjun Zhu ◽  
Baolin Yao
Keyword(s):  
Soil Moisture ◽  
Soil Salinity ◽  
Drip Irrigation ◽  
Rapid Development ◽  
Salt Content ◽  
Cotton Field ◽  
Salt Leaching ◽  
Growing Period ◽  
Time Migration ◽  
Southern Xinjiang

The drip irrigation under mulch has become one of significant supporting technologies for cotton industry development in Xinjiang, and has shown the good economic and ecological benefits. With the rapid development of society and economy in Southern Xinjiang, the conventional mode of large-quota winter and spring irrigation, salt leaching and alkali decreasing is difficult to support sustainable development of land and water resources in Southern Xinjiang. This study tries to adjust soil moisture and salt content regulation mode of massive water salt leaching and drip irrigation under mulch in the non-growing period of cotton field in Southern Xinjiang, explores interannual soil salinity change features of drip irrigation cotton field without winter and spring irrigation, and provides experimental basis for drip irrigation technology under mulch which can reduce and exempt cotton irrigation in winter and spring. According to ET0, the dual-factor complete combination experiment involving 3 irrigating water quotas (I1, I2, I3) and 2 irrigation times (T12, T16) was designed, and 6 treatments were involved in total(I1T12,I2T12,I3T12,I1T16,I2T16 and I3T16). The investigation results of four-year (2012–2015) field positioning experiment showed that, under the condition of “germination under drip irrigation” without winter and spring irrigation, increasing irrigation quota and irrigation times could lower 0-100cm soil salinity accumulation, but the soil salinity accumulation degree was 40-100cm, and less than 0-30cm. In the seedling stage, bud stage, blossom and boll-forming stage, and boll opening stage, the average salinity of 0-100cm soil increased by 39.81%, 31.91%, 26.85% and 29.47%, respectively. Increasing irrigation quota and irrigation times could ease interannual soil salinity accumulation degree of cotton field with drip irrigation under mulch, without winter and spring irrigation. 0-100cm soil salinity before sowing was related to the irrigation quota of cotton in the growing stage of the last year. The larger the irrigation quota was, the smaller the soil salinity before sowing would be. The accumulation amount of soil salinity at the end of growing stage under different treatments was lower than that before sowing. The drip irrigation of cotton under mulch in the growing stage could effectively regulate soil salinity distribution and space-time migration process in the growing stage of cotton. Compared with the beginning of 2012, 0-100cm average soil salinity under 3 irrigation quotas (I1, I2, I3) was 33.66%, 5.60% and 1.24%, respectively. Salt accumulating rates under 12 irrigations and 16 irrigations were 20.66% and 6.33%, respectively. The soil had the risk of salinization when the “germination under drip irrigation” without winter and spring irrigation was used. Such results can provide the reference for prevention and treatment of soil moisture and salt content of cotton field with drip irrigation under mulch in the arid region.

A digital camera as an alternative tool for estimating soil salinity and soil surface roughness

Geoderma ◽  
2019 ◽  
Vol 341 ◽  
pp. 68-75 ◽  
Author(s):  
L. Xu ◽  
C.L. Zheng ◽  
Z.C. Wang ◽  
M.J. Nyongesah
Keyword(s):  
Surface Roughness ◽  
Soil Salinity ◽  
Digital Camera ◽  
Soil Surface ◽  
Soil Surface Roughness

scholarly journals Discrimination and Quantification of Moroccan Gasoline Adulteration with Diesel Using Fourier Transform Infrared Spectroscopy and Chemometric Tools

2019 ◽  
Vol 102 (3) ◽  
pp. 966-970 ◽  
Author(s):  
Issam Barra ◽  
Mohammed Alaoui Mansouri ◽  
Mohammed Bousrabat ◽  
Yahia Cherrah ◽  
Abdelaziz Bouklouze ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Mean Squared Error ◽  
External Validation ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Prediction Errors ◽  
Least Squares Regression ◽  
Squared Error ◽  
Plsr Model ◽  
Chemometric Tools

Abstract In this work, transform-infrared spectroscopy (FTIR) was associated with chemometric tools, especially principal component analysis (PCA) and partial least squares regression (PLSR), to discriminate and quantify gasoline adulteration with diesel. The method is composed of a total of 100 mixtures were prepared, and then FTIR fingerprints were recorded for all samples. PCA was used to verify that mixtures can be distinguished from pure products and to check that there are no outliers. As a result of using just PC1 and PC2, more than 98% of the general variability was explained. The PLSR model based on infrared spectra has shown its capabilities to be suitable for predicting gasoline adulteration in the concentration range of 0 to 98% (w/w), with a high significant coefficient of determination (R2 = 99.25%) and an acceptable calibration and prediction errors (root mean squared error of calibration = 0.63 and root mean square of external validation and/or prediction = 0.69).

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

scholarly journals How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

2021 ◽  
Vol 149 ◽  
Author(s):  
Junwen Tao ◽  
Yue Ma ◽  
Xuefei Zhuang ◽  
Qiang Lv ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Variable Selection ◽  
Mean Squared Error ◽  
Moving Average ◽  
Dynamic Bayesian Networks ◽  
Environmental Data ◽  
Control Measures ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Analysis Strategy ◽  
Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

scholarly journals Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat

2021 ◽  
Vol 13 (4) ◽  
pp. 739
Author(s):  
Jiale Jiang ◽  
Jie Zhu ◽  
Xue Wang ◽  
Tao Cheng ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Precision Agriculture ◽  
Field Experiments ◽  
Mean Squared Error ◽  
Growing Season ◽  
Leaf Nitrogen ◽  
Textural Analysis ◽  
Hyperspectral Images ◽  
Leaf Nitrogen Content ◽  
New Feature

Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification of different components of crop canopy, and the performance of spectral and textural indices from different components on estimating leaf nitrogen content (LNC) of wheat remains unexplored. This study aims to investigate a new feature extracted from near-ground hyperspectral imaging data to estimate precisely the LNC of wheat. In field experiments conducted over two years, we collected hyperspectral images at different rates of nitrogen and planting densities for several varieties of wheat throughout the growing season. We used traditional methods of classification (one unsupervised and one supervised method), spectral analysis (SA), textural analysis (TA), and integrated spectral and textural analysis (S-TA) to classify the images obtained as those of soil, panicles, sunlit leaves (SL), and shadowed leaves (SHL). The results show that the S-TA can provide a reasonable compromise between accuracy and efficiency (overall accuracy = 97.8%, Kappa coefficient = 0.971, and run time = 14 min), so the comparative results from S-TA were used to generate four target objects: the whole image (WI), all leaves (AL), SL, and SHL. Then, those objects were used to determine the relationships between the LNC and three types of indices: spectral indices (SIs), textural indices (TIs), and spectral and textural indices (STIs). All AL-derived indices achieved more stable relationships with the LNC than the WI-, SL-, and SHL-derived indices, and the AL-derived STI was the best index for estimating the LNC in terms of both calibration (Rc2 = 0.78, relative root mean-squared error (RRMSEc) = 13.5%) and validation (Rv2 = 0.83, RRMSEv = 10.9%). It suggests that extracting the spectral and textural features of all leaves from near-ground hyperspectral images can precisely estimate the LNC of wheat throughout the growing season. The workflow is promising for the LNC estimation of other crops and could be helpful for precision agriculture.

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