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Original Articles

Inversion of soil salinity according to different salinization grades using multi-source remote sensing

ORCID Icon, , & ORCID Icon
Pages 1274-1293 | Received 19 Mar 2020, Accepted 20 May 2020, Published online: 24 Jun 2020

References

  • Akar A, Gökalp E, Akar Ö, Yılmaz V. 2017. Improving classification accuracy of spectrally similar land covers in the rangeland and plateauareas with a combination of WorldView-2 and UAV images. Geocarto Int. 32(9):990–1003.
  • Akar Ö. 2018. The Rotation Forest algorithm and object-based classification method for land use mapping through UAV images. Geocarto Int. 33(5):538–553.
  • Ardak K, Tashpolat T, Zhang F, Lei L, Zhang D. 2015. Prediction model of saline of soil and its validation based on thermal infrared emissivity spectrum. Trans Chin Soc Agric Eng. 31(17):115–120.
  • Casanova-Gascón J, Martín-Ramos P, Martí-Dalmau C, Badía-Villas D. 2018. Nutrients assimilation and chlorophyll contents for different grapevine varieties in calcareous soils in the Somontano DO (Spain). Beverages. 4(4):90.
  • Chen Z. 2012. Relationship model among water content, bulk density and reflectivity of different soil. Trans CSAE. 28(4):76–81.
  • Chen SB, Chen JY, Zhang ZT, Bian J, Wang YF, Shi SL. 2018. Retrieving soil water content of winter wheat during heading period by multi-spectral remote sensing of Unmanned Aerial Vehicle (UAV). Water Saving Irrig. 43(5):39–43.
  • Chen JY, Wang XT, Zhang ZT, Han J, Yao ZH, Wei GF. 2019a. Soil salinization monitoring method based on UAV and satellite remote sensing. Trans Chin Soc Agric Mach. 50(12):1–19.
  • Chen HY, Zhao GX, Chen JC, Wang RY, Gao MX. 2015. Remote sensing inversion of saline soil salinity based on modified vegetation index in estuary area of Yellow River. Trans Chinese Soc Agric Eng. 31(5):107–114.
  • Chen HY, Zhao GX, Li YH, Wang DY, Ma Y. 2019b. Monitoring the seasonal dynamics of soil salinization in the Yellow River Delta of China using Landsat data. Nat Hazards Earth Syst Sci. 19(7):1499–1508.
  • Dai XJ, Peng J, Zhang YL, Luo HP, Xiang HY. 2016. Prediction on soil salt content based on spectral classification. Acta Pedol Sin. 53(04):909–918.
  • Dehaan RL, Taylor GR. 2002. Field -derived spectra of salinized soils and vegetation as indicators of irrigation -induced soil salinization. Remote Sens Environ. 80(3):406–418.
  • Dong Y, Wan Y, Liu F, Zhuge Y. 2019. Effects of exogenous SA supplied with different approaches on growth, chlorophyll content and antioxidant enzymes of peanut growing on calcareous soil. J. Plant Nutr. 42(16):1869–1883.
  • Du M, Noguchi N, Itoh A. 2016. Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle. Int J Agric Biol Eng. 49(16):5–13.
  • Duan XQ, Zheng LR, Sun JH, Liu WB, Wang WQ, An HL. 2019. Co-culturing on dry filter paper significantly increased the efficiency of Agrobacterium-mediated transformations of maize immature embryos. Physiol Mol Biol Plants. 25(2):549–560.
  • Hasan U, Sawut M, Nurmamat I, Sawut R, Wang JZ. 2017. Inversion model of soil salt content based on World View-2 image. Trans CSAE. 33(24):200–206.
  • Hassan-Esfahani L, Torres-Rua A, Jensen A, McKee M. 2015. Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sens. 7(3):2627–2646.
  • Hoffmann H, Jensen R, Thomsen A, Nieto H, Rasmussen J, Friborg T. 2016. Crop water stress maps for entire growing seasons from visible and thermal UAV imagery. Biogeosciences. 13(24):6545–6563.
  • Hu J, Peng J, Zhou Y, Xu DY, Zhao RY, Jiang QS, Fu TT, Wang F, Shi Z. 2019. Quantitative estimation of soil salinity using UAV-borne hyperspectral and satellite multispectral images. Remote Sens. 11(7):736.
  • Ivushkin K, Bartholomeus H, Bregt AK, Pulatov A, Franceschini MHD, Kramer H, Loo EN, Roman VJ, Finkers R. 2019. UAV based soil salinity assessment of cropland. Geoderma. 338:502–512.
  • Jin XL, Du J, Liu HJ, Wang ZM, Song KS. 2016. Remote estimation of soil organic matter content in the Sanjiang plain, Northest China: the optimal band algorithm versus the GRA-ANN model. Agric Forest Meteorol. 218-219(12):250–260.
  • Li J, Zhao GX, Chang CY, Liu HT. 2014. Land salinization information extraction method based on HSI hyperspectral and TM imagery. Spectrosc Spectr Anal. 34(2):520–525.
  • Li M, Zhou SW, Zhang H, Bi XL. 2016. Estimating soil salinity in different landscapes of the Yellow River Delta through Landsat OLI/TIRS and ETM + Data. J Coast Conserv. 20(4):271–279.
  • Liu Y, Pan XZ, Wang CK, Li YL, Shi RJ, Zhou R, Xie XL. 2013. Predicting soil salinity based on spectral symmetry under wet soil condition. Spectrosc Spectr Anal. 33(10):2771–2776.
  • Morellos A, Pantazi X-E, Moshou D, Alexandridis T, Whetton R, Tziotzios G, Wiebensohn J, Bill R, Mouazen AM. 2016. Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosystems Eng. 152(12):104–116.
  • Nguyen K, Liou Y, Tran H, Hoang PP, Nguyen TH. 2020. Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam. Prog Earth Planet Sci. 7:1.
  • Park S-H, Lee B-R, Lee J-H, Kim T-H. 2016. S nutrition alleviates salt stress by maintaining the assemblage of photosynthetic organelles in Kentucky bluegrass (Poa pratensis L.). Plant Growth Regul. 79(3):367–375.
  • Patel AK, Ghosh JK, Sayyad SU. 2020. Fractional abundances study of macronutrients in soil using hyperspectral remote sensing. Geocarto Int. 1–20.
  • Rosa OC, Baup F, Fabre S, Fieuzal R, Briotte X. 2015. Improvement of soil moisture retrieval from hyperspectral VNIR-SWIR data using clay content information: from laboratory to field experiments. Remote Sens. 7(3):3184–3205.
  • Scudiero E, Skaggs TH, Corwin DL. 2015. Regional-scale soil salinity assessment using Landsat ETM + canopy reflectance. Remote Sens Environ. 169:335–343.
  • Shi XP, Ren JJ, Yu Q, Zhou SM, Ren QP, Kong LJ, Wang XL. 2018a. Overexpression of SDH confers tolerance to salt and osmotic stress, but decreases ABA sensitivity in Arabidopsis. Plant Biol (Stuttg)). 20(2):327–337.
  • Shi Y, Wang RJ, Wang YB. 2018b. Soil organic carbon prediction based on convolutional neural networks and near infrared spectroscopy. Comput Appl Software. 35(10):147–152.
  • Sidike A, Zhao SH, Wen YM. 2014. Estimating soil salinity in Pingluo county of China using QuickBird data and soil reflectance spectra. Int J Appl Earth Observ Geoinform. 26:156–175.
  • Srivastava R, Sethi M, Yadav RK, Bundela DS, Singh M, Chattaraj S, Singh SK, Nasre RA, Bishnoi SR, Dhale S, et al. 2017. Visible-near infrared reflectance spectroscopy for rapid characterization of salt-affected soil in the Indo-Gangetic plains of Haryana. J Indian Soc Remote Sens. 45(2):307–315.
  • Su HJ, Du Q. 2012. Hyperspectral band clustering and band selection for urban land cover classification. Geocarto Int. 27(5):395–411.
  • Wang ZR. 2016. Spatial and temporal variability of soil moisture and salinity, affecting factors and forecasting model in the typical area of the Yellow River Delta. Tai'an: Shandong Agricultural University.
  • Wang H, Gao YL, Yu M, Du YP, Sun YJ, Zhai H. 2018a. Effect of root irrigation of acetic acid and wine on photoinhibition of grape under seawater stress. Sci Agric Sin. 51(21):4210–4218.
  • Wang LJ, Guo Y, He J, Wang LM, Zhang XW, Liu T. 2018b. Classification method by fusion of decision tree and SVM based on Sentinel-2A image. Trans Chin Soc Agric Mach. 49(09):146–153.
  • Wang GD, Liu Q, Shang XT, Chen C, Xu N, Guan J, Meng QW. 2018c. Overexpression of transcription factor SlNAC35 enhances the chilling tolerance of transgenic tomato. Biol Plant. 62(3):479–488.
  • Wang HF, Zhang ZT, Fu QP, Chen SB, Bian J, Cui T. 2018d. Inversion of soil moisture content based on multispectral remote sensing data of low-altitude UAV. Water Saving Irrig. 43(1):90–94.
  • Wei Y, Ding JL, Wang F. 2017. Optimal scale analysis of soil salinity prediction in oasis irrigated area of arid land based on Landsat OLI. Sci Agric Sin. 50(15):2969–2982.
  • Weng YL, Gong P. 2006. A review on remote sensing technique for salt-affected soils. Sci Geogr Sinica. 26(3):369–375.
  • Weng YL, Gong P, Zhu ZL. 2008. Reflectance spectroscopy for the assessment of soil salt content in soils of the Yellow River Delta of China. Int J Remote Sens. 29(19):5511–5531.
  • Whitney K, Scudiero E, El-Askary HM, Skaggs TH, Allali M, Corwin DL. 2018. Validating the use of MODIS time series for salinity assessment over agricultural soils in California, USA. Ecol Indicators. 93:889–898.
  • Wu CS, Huang C, Liu GH, Liu QS. 2016. Spatial prediction of soil salinity in the Yellow River Delta based on geographically. Resources Sci. 38(04):704–713.
  • Xu HQ. 2013. Spatiotemporal dynamics of the bare soil cover in the Hetian basinal area of County Changting, China, during the past 35 years. Acta Ecol Sin. 33(10):2946–2953.
  • Xu C, Zeng W, Huang J, Wu J, van Leeuwen W. 2016. Prediction of soil moisture content and soil salt concentration from hyperspectral laboratory and field data. Remote Sens. 8(1):42–62.
  • Yao Y, Ding JL, Zhang F, Wang G, Jiang HN. 2013. Monitoring of soil salinization in Northern Tarim Basin, Xinjiang of China in dry and wet seasons based on remote sensing. Chin J Appl Ecol. 24(11):3213–3234.
  • Yu H, Liu MY, Du BJ, Wang ZM, Hu LJ, Zhang B. 2018. Mapping soil salinity/sodicity by using Landsat OLI imagery and PLSR algorithm over semiarid West Jilin Province, China. Sensors. 18(4):1048–1065.
  • Zhang ZL, Han ZD, Liu LJ, Li XD, Hao FP, Dong Z. 2018. Parameters optimization for gripping and delivering device of corn harvester for reaping both corn stalk and spike. Trans Chin Soc Agric Mach. 49(3):114–121.
  • Zhang TT, Qi JG, Gao Y, Ouyang ZT, Zeng SL, Zhao B. 2015. Detecting soil salinity with MODIS time series VI data. Ecol Indicators. 52:480–489.
  • Zhang TX, Su JY, Liu CJ, Chen WH. 2019a. Potential bands of Sentinel-2A satellite for classification problems in precision agriculture. Int J Autom Comput. 16(1):16–26.
  • Zhang ZT, Wei GF, Yao ZH, Tan CX, Wang XT, Han J. 2019b. Soil salt inversion model based on UAV multispectral remote sensing. Trans Chin Soc Agric Mach. 50(12):151–160.
  • Zhang Y, Zhang LY, Zhang HH, Song CY, Lin GH, Han WT. 2019c. Crop coefficient estimation method of maize by UAV remote sensing and soil moisture monitoring. Trans Chin Soc Agric Eng. 35(01):83–89.
  • Zhang SM, Zhao GX. 2019. A harmonious satellite-unmanned aerial vehicle-ground measurement inversion method for monitoring salinity in coastal saline soil. Remote Sens. 11(14):1700.
  • Zhang SM, Zhao GX, Lang K, Su BW, Chen XN, Xi X, Zhang HB. 2019d. Integrated satellite, unmanned aerial vehicle (UAV) and ground inversion of the SPAD of winter wheat in the reviving stage. Sensors. 19(7):1485.
  • Zhao H, Chen X. 2015. Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. IEEE Int Geosci Remote Sens Symp. 99(11):1666–1668.

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