Using proximal sensor data for soil salinity management and mapping
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摘要
Over the past five decades, increased pressure caused by the rapidly growing population has resulted in a reclamation of agricultural and urban buffer zones along China's coastline. However, information about the spatio–temporal variation of soil salinity in these reclaimed regions is limited. As such, obtaining this information is crucial for mapping the variation in saline areas and to identify suitable salinity management strategies. In this study, we employed EM38 data to conduct digital soil mapping of spatio–temporal variation and map these variations of different site-specific zones. The results indicated that the distribution of soil salinity was heterogeneous in the middle of, and that the leaching of salts was significant at the edges of, the study field. Afterwards, fuzzy-k means algorithm was used to divide the site-specific management zones within the time series apparent soil electrical conductivity(ECa) data and the spatial correlations of variation. We concluded that two management zones are optimal to guide precision management. Zone A had an average salinity level of about 165 mS m–1, in which salt-tolerant crops, such as cotton and barley can grow normally, while crops such as soybean and cowpeas may be planted using leaching and increasing the mulching film methods to reduce the accumulation of salt in surface soil. In Zone B, there was a low salinity level with a mean of 89 mS m–1 for ECa, which allows for rice, wheat, and a wide range of vegetables to be grown normally. In such situations, measures such as an optimized combination of irrigation and drainage, as well as soil amendment can be taken to adjust and control the salt content. Particularly, flattening the land with a large-scale machine was used to improve the ability of micro-topography to influence salt migration; rice and other dry, land crops were planted in rotation in combination with utilizing salt-leaching multiple times to speed up desalinization.
Over the past five decades, increased pressure caused by the rapidly growing population has resulted in a reclamation of agricultural and urban buffer zones along China's coastline. However, information about the spatio–temporal variation of soil salinity in these reclaimed regions is limited. As such, obtaining this information is crucial for mapping the variation in saline areas and to identify suitable salinity management strategies. In this study, we employed EM38 data to conduct digital soil mapping of spatio–temporal variation and map these variations of different site-specific zones. The results indicated that the distribution of soil salinity was heterogeneous in the middle of, and that the leaching of salts was significant at the edges of, the study field. Afterwards, fuzzy-k means algorithm was used to divide the site-specific management zones within the time series apparent soil electrical conductivity(ECa) data and the spatial correlations of variation. We concluded that two management zones are optimal to guide precision management. Zone A had an average salinity level of about 165 mS m–1, in which salt-tolerant crops, such as cotton and barley can grow normally, while crops such as soybean and cowpeas may be planted using leaching and increasing the mulching film methods to reduce the accumulation of salt in surface soil. In Zone B, there was a low salinity level with a mean of 89 mS m–1 for ECa, which allows for rice, wheat, and a wide range of vegetables to be grown normally. In such situations, measures such as an optimized combination of irrigation and drainage, as well as soil amendment can be taken to adjust and control the salt content. Particularly, flattening the land with a large-scale machine was used to improve the ability of micro-topography to influence salt migration; rice and other dry, land crops were planted in rotation in combination with utilizing salt-leaching multiple times to speed up desalinization.
Over the past five decades, increased pressure caused by the rapidly growing population has resulted in a reclamation of agricultural and urban buffer zones along China's coastline. However, information about the spatio–temporal variation of soil salinity in these reclaimed regions is limited. As such, obtaining this information is crucial for mapping the variation in saline areas and to identify suitable salinity management strategies. In this study, we employed EM38 data to conduct digital soil mapping of spatio–temporal variation and map these variations of different site-specific zones. The results indicated that the distribution of soil salinity was heterogeneous in the middle of, and that the leaching of salts was significant at the edges of, the study field. Afterwards, fuzzy-k means algorithm was used to divide the site-specific management zones within the time series apparent soil electrical conductivity(ECa) data and the spatial correlations of variation. We concluded that two management zones are optimal to guide precision management. Zone A had an average salinity level of about 165 mS m–1, in which salt-tolerant crops, such as cotton and barley can grow normally, while crops such as soybean and cowpeas may be planted using leaching and increasing the mulching film methods to reduce the accumulation of salt in surface soil. In Zone B, there was a low salinity level with a mean of 89 mS m–1 for ECa, which allows for rice, wheat, and a wide range of vegetables to be grown normally. In such situations, measures such as an optimized combination of irrigation and drainage, as well as soil amendment can be taken to adjust and control the salt content. Particularly, flattening the land with a large-scale machine was used to improve the ability of micro-topography to influence salt migration; rice and other dry, land crops were planted in rotation in combination with utilizing salt-leaching multiple times to speed up desalinization.
引文
Ali Aldabaa A A,Weindorf D C,Chakraborty S,Sharma A,Li B.2015.Combination of proximal and remote sensing methods for rapid soil salinity quantification.Geoderma,239-240,34-46.
Bezdek J C.1981.Pattern Recognition with Fuzzy Objective Function Algorithms.Plenum Press,New York.
Chung S O,Sudduth A K,Hummel J W.2006.Design and validation of an on-the-go soil strength profile sensor.Transactions of American Society of Agricultural and Biological Engineers,49,5-14.
Corwin D L,Lesch S M.2003.Application of soil electrical conductivity to precision agriculture:Theory,principles,and guidelines.Agronomy Journal,95,455-471.
Douaik A,Van Meirvenne M,T?th T.2005.Soil salinity mapping using spatio-temporal kriging and Bayesian maximum entropy with interval soft data.Geoderma,128,234-248.
Eigenberg R A,Doran J W,Nienaber J A,Ferguson R B,Woodbury B L.2002.Electrical conductivity monitoring of soil condition and available N with animal manure and a cover crop.Agriculture,Ecosystems and Environment,88,183-193.
Eldeiry A A,Garcia L A.2011.Using indicator kriging technique for soil salinity and yield management.Journal of Irrigation and Drainage Engineering,137,82-93.
Guo Y,Huang J Y,Shi Z,Li H Y.2015.Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.PLoS ONE,10,e0127996.
Guo Y,Shi Z,Li H Y,Triantafilis J.2013a.Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China.Soil Use and Management,29,445-456.
Guo Y,Shi Z,Zhou L Q,Jin X,Tian Y F,Teng H F.2013b.Integrating remote sensing and proximal sensors for detecting soil moisture and salinity variability in coastal area.Journal of Integrative Agriculture,12,101-108.
Gorsevski P V,Gessler P E,Jankowski P.2003.Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard.Journal of Geographical Systems,5,223-251.
Lesch S M,Corwin D L,Robinson D A.2005.Apparent soil electrical conductivity mapping as an agricultural management tool in arid zone soils.Computers and Electronics in Agriculture,46,351-378.
Li H Y,Webster R,Shi Z.2015.Mapping soil salinity in the Yangtze delta:REML and universal kriging(E-BLUP)revisited.Geoderma,237-238,71-77.
Li S,Shi Z,Chen S C,Ji W J,Zhou L Q,Yu W,Webster R.2015.In situ measurements of organic carbon in soil profiles using vis-NIR spectroscopy on the Qinghai-Tibet Plateau.Environmental Science&Technology,49,4980-4987.
Li Y,Shi Z,Wu C F,Li H Y,Li F.2008.Determination of potential management zones from soil electrical conductivity,yield and crop data.Journal of Zhejiang University(Science B),9,68-76.
Loreto A B,Morgan M T.1996.Development of an Automated System for Field Measurement of Soil Nitrate.American Society of Agricultural Engineers,Michigan.pp.96-1087.
Maliva R G.2016.Geostatistical methods and applications.In:Aquifer Characterization Techniques.Springer Hydrogeology.Springer,Cham.
McBratney A B,Moore A W.1985.Application of fuzzy-sets to climatic classification.Agricultural and Forest Meteorology,35,165-185.
McNeill J D.1980.Electromagnetic Terrain Conductivity Measurement at Low Induction Numbers:Technical Note TN-6.GEONICS Limited,Ontario,Canada.p.15.
Minasny B,McBratney A B.2002.FuzME version 3.0.[2010-03-20].http://www.usyd.edu.au/su/agric/acpa
Moral F J,Terr?J M,Marques da Silva J R.2010.Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques.Soil&Tillage Research,106,335-343.
Myers D B,Kitchen N R,Sudduth K A,Grunwald S,Miles R J,Sadler E J,Udawatta R P.2010.Combining proximal and penetrating soil electrical conductivity sensors for highresolution digital soil mapping.In:Viscarra Rossel R A,McBratney A B,Minasny B,eds.,Proximal Soil Sensing.Springer Science+Business Media B.V.,The Netherlands.pp.233-243.
Odeh I O A,McBratney A B,Chittleborough D J.1992.Soil pattern recognition with fuzzy c-means:Application to classification and soil-landform interrelationship.Soil Science Society of America Journal,56,505-516.
?amonil P,TimkováJ,Va?í?kováI.2016.Uncertainty in the detection of disturbance spatial patterns in temperate forests.Dendrochronologia,37,46-56.
Shi Z,Ji W J,Viscarra Rossel R A,Chen S C,Zhou Y.2015.Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis-NIR spectral library.European Journal of Soil Science,66,679-687.
Shi Z,Li Y,Wang R C,Makeschine F.2005.Assessment of temporal and spatial variability of soil salinity in a coastal saline field.Environmental Geology,48,171-178.
Sila A,Pokhariyal G,Shepherd K.2017.Evaluating regressionkriging for mid-infrared spectroscopy prediction of soil properties in western Kenya.Geoderma Regional,10,39-47.
Triantafilis J,Laslett G M,McBratney A B.2000.Calibrating an electromagnetic induction instrument to measure salinity in soil under irrigated cotton.Soil Science Society of America Journal,64,1009-1017.
Triantafilis J,Odeh I O A,Minasny B,McBratney A B.2003.Elucidation of physiographic and hydrogeological features of the lower Namoi valley using fuzzy k-means classification of EM34 data.Environmental Modelling&Software,18,667-680.
Viscarra Rossel R A,McBratney A B,Minasny B.2010.Proximal Soil Sensing.Springer Science+Business Media B.V.,The Netherlands.
Wang T J,Wedin D A,Franz T E,Hiller J.2015.Effect of vegetation on the temporal stability of soil moisture in grassstabilized semi-arid sand dunes.Journal of Hydrology,521,447-459.
Webster R,Oliver M A.2007.Geostatistics for Environmental Scientists.John Wiley&Sons,England.
Zhang G L,Liu F,Song X D.2017.Recent progress and future prospect of digital soil mapping:A review.Journal of Integrative Agriculture,16,2871-2885.
Bezdek J C.1981.Pattern Recognition with Fuzzy Objective Function Algorithms.Plenum Press,New York.
Chung S O,Sudduth A K,Hummel J W.2006.Design and validation of an on-the-go soil strength profile sensor.Transactions of American Society of Agricultural and Biological Engineers,49,5-14.
Corwin D L,Lesch S M.2003.Application of soil electrical conductivity to precision agriculture:Theory,principles,and guidelines.Agronomy Journal,95,455-471.
Douaik A,Van Meirvenne M,T?th T.2005.Soil salinity mapping using spatio-temporal kriging and Bayesian maximum entropy with interval soft data.Geoderma,128,234-248.
Eigenberg R A,Doran J W,Nienaber J A,Ferguson R B,Woodbury B L.2002.Electrical conductivity monitoring of soil condition and available N with animal manure and a cover crop.Agriculture,Ecosystems and Environment,88,183-193.
Eldeiry A A,Garcia L A.2011.Using indicator kriging technique for soil salinity and yield management.Journal of Irrigation and Drainage Engineering,137,82-93.
Guo Y,Huang J Y,Shi Z,Li H Y.2015.Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.PLoS ONE,10,e0127996.
Guo Y,Shi Z,Li H Y,Triantafilis J.2013a.Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China.Soil Use and Management,29,445-456.
Guo Y,Shi Z,Zhou L Q,Jin X,Tian Y F,Teng H F.2013b.Integrating remote sensing and proximal sensors for detecting soil moisture and salinity variability in coastal area.Journal of Integrative Agriculture,12,101-108.
Gorsevski P V,Gessler P E,Jankowski P.2003.Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard.Journal of Geographical Systems,5,223-251.
Lesch S M,Corwin D L,Robinson D A.2005.Apparent soil electrical conductivity mapping as an agricultural management tool in arid zone soils.Computers and Electronics in Agriculture,46,351-378.
Li H Y,Webster R,Shi Z.2015.Mapping soil salinity in the Yangtze delta:REML and universal kriging(E-BLUP)revisited.Geoderma,237-238,71-77.
Li S,Shi Z,Chen S C,Ji W J,Zhou L Q,Yu W,Webster R.2015.In situ measurements of organic carbon in soil profiles using vis-NIR spectroscopy on the Qinghai-Tibet Plateau.Environmental Science&Technology,49,4980-4987.
Li Y,Shi Z,Wu C F,Li H Y,Li F.2008.Determination of potential management zones from soil electrical conductivity,yield and crop data.Journal of Zhejiang University(Science B),9,68-76.
Loreto A B,Morgan M T.1996.Development of an Automated System for Field Measurement of Soil Nitrate.American Society of Agricultural Engineers,Michigan.pp.96-1087.
Maliva R G.2016.Geostatistical methods and applications.In:Aquifer Characterization Techniques.Springer Hydrogeology.Springer,Cham.
McBratney A B,Moore A W.1985.Application of fuzzy-sets to climatic classification.Agricultural and Forest Meteorology,35,165-185.
McNeill J D.1980.Electromagnetic Terrain Conductivity Measurement at Low Induction Numbers:Technical Note TN-6.GEONICS Limited,Ontario,Canada.p.15.
Minasny B,McBratney A B.2002.FuzME version 3.0.[2010-03-20].http://www.usyd.edu.au/su/agric/acpa
Moral F J,Terr?J M,Marques da Silva J R.2010.Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques.Soil&Tillage Research,106,335-343.
Myers D B,Kitchen N R,Sudduth K A,Grunwald S,Miles R J,Sadler E J,Udawatta R P.2010.Combining proximal and penetrating soil electrical conductivity sensors for highresolution digital soil mapping.In:Viscarra Rossel R A,McBratney A B,Minasny B,eds.,Proximal Soil Sensing.Springer Science+Business Media B.V.,The Netherlands.pp.233-243.
Odeh I O A,McBratney A B,Chittleborough D J.1992.Soil pattern recognition with fuzzy c-means:Application to classification and soil-landform interrelationship.Soil Science Society of America Journal,56,505-516.
?amonil P,TimkováJ,Va?í?kováI.2016.Uncertainty in the detection of disturbance spatial patterns in temperate forests.Dendrochronologia,37,46-56.
Shi Z,Ji W J,Viscarra Rossel R A,Chen S C,Zhou Y.2015.Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis-NIR spectral library.European Journal of Soil Science,66,679-687.
Shi Z,Li Y,Wang R C,Makeschine F.2005.Assessment of temporal and spatial variability of soil salinity in a coastal saline field.Environmental Geology,48,171-178.
Sila A,Pokhariyal G,Shepherd K.2017.Evaluating regressionkriging for mid-infrared spectroscopy prediction of soil properties in western Kenya.Geoderma Regional,10,39-47.
Triantafilis J,Laslett G M,McBratney A B.2000.Calibrating an electromagnetic induction instrument to measure salinity in soil under irrigated cotton.Soil Science Society of America Journal,64,1009-1017.
Triantafilis J,Odeh I O A,Minasny B,McBratney A B.2003.Elucidation of physiographic and hydrogeological features of the lower Namoi valley using fuzzy k-means classification of EM34 data.Environmental Modelling&Software,18,667-680.
Viscarra Rossel R A,McBratney A B,Minasny B.2010.Proximal Soil Sensing.Springer Science+Business Media B.V.,The Netherlands.
Wang T J,Wedin D A,Franz T E,Hiller J.2015.Effect of vegetation on the temporal stability of soil moisture in grassstabilized semi-arid sand dunes.Journal of Hydrology,521,447-459.
Webster R,Oliver M A.2007.Geostatistics for Environmental Scientists.John Wiley&Sons,England.
Zhang G L,Liu F,Song X D.2017.Recent progress and future prospect of digital soil mapping:A review.Journal of Integrative Agriculture,16,2871-2885.