Interaction of climate, topography and soil properties with cropland and cropping pattern using remote sensing data and machine learning methods

Abbas, 2021, Strawberry fungal leaf scorch disease identification in real-time strawberry field using deep learning architectures, Plants, 10, 2643, 10.3390/plants10122643 Agam, 2007, A vegetation index based technique for spatial sharpening of thermal imagery, Remote Sens. Environ., 107, 545, 10.1016/j.rse.2006.10.006 Ahmad, 2019, Field Crops Research Climate warming and management impact on the change of phenology of the rice-wheat cropping system in Punjab, Pakistan. F. Crop. Res., 230, 46, 10.1016/j.fcr.2018.10.008 Ahmad, N., Ullah, S., Zhao, N., Mumtaz, F., Ali, Asad, Ali, Anwar, Tariq, A., Kareem, M., Imran, A.B., Khan, I.A., Shakir, M., 2023. Comparative Analysis of Remote Sensing and Geo-Statistical Techniques to Quantify Forest Biomass. Forests 14, 379. 10.3390/f14020379. Ahmadi, 2007, Geostatistical analysis of spatial and temporal variations of groundwater level, Environ. Monit. Assess., 129, 277, 10.1007/s10661-006-9361-z Ali, 2020, Estimation of soil carbon pools in the forests of Khyber Pakhtunkhwa Province, Pakistan. J. For. Res., 31, 2313 Amir, 2019, Land cover mapping and crop phenology of Potohar Region, Punjab, Pakistan. Pakistan J. Agric. Sci., 56, 187 Aronstein, 1993, Effect of a non-ionic surfactant added to the soil surface on the biodegradation of aromatic hydrocarbons within the soil, Appl. Microbiol. Biotechnol., 39, 386, 10.1007/BF00192098 AVCI, C., BUDAK, M., YAĞMUR, N., BALÇIK, F., 2021. Comparison Between Random Forest and Support Vector Machine Algorithms for LULC Classification. Int. J. Eng. Geosci. 8, 1–10. 10.26833/ijeg.987605. Belgiu, 2016, Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm. Remote Sens., 114, 24, 10.1016/j.isprsjprs.2016.01.011 Bhunia, 2018, Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC), J. Saudi Soc. Agric. Sci., 17, 114 Boryan, 2011, Monitoring US agriculture: The US department of agriculture, national agricultural statistics service, cropland data layer program, Geocarto Int., 26, 341, 10.1080/10106049.2011.562309 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Busetto, 2019, Analysing spatial–temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the PhenoRice algorithm, Int. J. Appl. Earth Obs. Geoinf., 75, 15 Congalton, R.G., Green, K., 2008. Assessing the Accuracy of Remotely Sensed Data, Assessing the Accuracy of Remotely Sensed Data. CRC Press. 10.1201/9780429052729. Couto, 2003, Assessing the accuracy of spatial simulation models, Ecol. Modell., 167, 181, 10.1016/S0304-3800(03)00176-5 da Silva Monteiro, 2022, Rainfall in the Urban Area and Its Impact on Climatology and Population Growth, Atmosphere (Basel), 13, 1610, 10.3390/atmos13101610 Delbart, 2005, Determination of phenological dates in boreal regions using Normalized Difference Water Index, Remote Sens. Environ., 97, 26, 10.1016/j.rse.2005.03.011 Eklundh, 2009, Mapping insect defoliation in Scots pine with MODIS time-series data, Remote Sens. Environ., 113, 1566, 10.1016/j.rse.2009.03.008 Firdaus, R., 2014. Doctoral Dissertation Assessing Land Use and Land Cover Change toward Sustainability in Humid Tropical Watersheds , Indonesia Assessing Land Use and Land Cover Change toward Sustainability in Humid Tropical Watersheds , Indonesia 0–1. Fu, 2022, Timely Plastic-Mulched Cropland Extraction Method from Complex Mixed Surfaces in Arid Regions, Remote Sens., 14, 4051, 10.3390/rs14164051 Gilabert, 2002, A generalized soil-adjusted vegetation index, Remote Sens. Environ., 82, 303, 10.1016/S0034-4257(02)00048-2 Gu, 2007, A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States, Geophys. Res. Lett., 34, 1, 10.1029/2006GL029127 Guo, 2005, Support vector machines for predicting distribution of Sudden Oak Death in California, Ecol. Modell., 182, 75, 10.1016/j.ecolmodel.2004.07.012 Hentze, 2016, Evaluating crop area mapping from modis time-series as an assessment tool for Zimbabwe’s “fast track land reform programme”, PLoS One, 11, e0156630, 10.1371/journal.pone.0156630 Hu, 2021, Evaluation of Vegetation Indices and Phenological Metrics Using Time-Series MODIS Data for Monitoring Vegetation Change in Punjab, Pakistan. Water, 13, 2550 Hu, 2021, Evaluation of vegetation indices and phenological metrics using time-series modis data for monitoring vegetation change in Punjab, Pakistan, Water (Switzerland), 13, 1 Huang, 2002, An assessment of support vector machines for land cover classification, Int. J. Remote Sens., 23, 725, 10.1080/01431160110040323 Huete, 1988, A soil-adjusted vegetation index (SAVI), Remote Sens. Environ., 25, 295, 10.1016/0034-4257(88)90106-X Huete, 1985, Spectral response of a plant canopy with different soil backgrounds, Remote Sens. Environ., 17, 37, 10.1016/0034-4257(85)90111-7 Jinguo, 2004, Identification of forest vegetation using vegetation indices, Chinese J. Popul. Resour. Environ., 2, 12, 10.1080/10042857.2004.10677383 Jr, P.J.P., Hatfield, J.L., Barnes, E.M., 2003. Remote Sensing for Crop Management, USDA-ARS, [email protected]. Karnieli, 2006, Comments on the use of the Vegetation Health Index over Mongolia, Int. J. Remote Sens., 27, 2017, 10.1080/01431160500121727 Khare, 2019, Assessment of spatio-temporal patterns of black spruce bud phenology across Quebec based on MODIS-NDVI time series and field observations, Remote Sens., 11, 2745, 10.3390/rs11232745 Kumar, 2014, Using district-level occurrences in MaxEnt for predicting the invasion potential of an exotic insect pest in India, Comput. Electron. Agric., 103, 55, 10.1016/j.compag.2014.02.007 Le, 2019, Hybrid artificial intelligence approaches for predicting buckling damage of steel columns under axial compression, Materials (Basel)., 12, 1670, 10.3390/ma12101670 Li, 2014, Coordination of supply chain with a dominant retailer under demand disruptions, Math. Probl. Eng., 2014, 1 Lopez-Granados, 2002. Spatial variability of agricultural soil parameters in southern Spain. Plant Soil v. 246, 97-105–2002 v.246 no.1. Majeed, 2021, Monitoring of land use–Land cover change and potential causal factors of climate change in Jhelum district, Punjab, Pakistan, through GIS and multi-temporal satellite data, Land, 10, 1026, 10.3390/land10101026 Majeed, 2022, A Detailed Ecological Exploration of the Distribution Patterns of Wild Poaceae from the Jhelum District (Punjab), Pakistan, Sustainability, 14, 3786, 10.3390/su14073786 Major, 1990, A ratio vegetation index adjusted for soil brightness, Int. J. Remote Sens., 11, 727, 10.1080/01431169008955053 Meng, 2017, Estimating land surface temperature from Feng Yun-3C/MERSI data using a new land surface emissivity scheme, Remote Sens., 9, 9, 10.3390/rs9121247 Moulin, 1998, Combining agricultural crop models and satellite observations: From field to regional scales, Int. J. Remote Sens., 19, 1021, 10.1080/014311698215586 Olofsson, 2008, Towards operational remote sensing of forest carbon balance across Northern Europe, Biogeosciences, 5, 817, 10.5194/bg-5-817-2008 Palacios-Orueta, 2012, Derivation of phenological metrics by function fitting to time-series of Spectral Shape Indexes AS1 and AS2: Mapping cotton phenological stages using MODIS time series, Remote Sens. Environ., 126, 148, 10.1016/j.rse.2012.08.002 Pang, 2022, Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia, Sensors, 22, 1, 10.3390/s22030717 Pettorelli, 2011, The Normalized Difference Vegetation Index (NDVI): Unforeseen successes in animal ecology, Clim. Res., 46, 15, 10.3354/cr00936 Poshtmasari, 2012, Comparison of Interpolation Methods for Estimating pH and EC in Agricultural Fields of Golestan Province, Int. J. Agric. Crop Sci., 4, 157 Pradhan, 2012, Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: A case study at Penang Island, Malaysia. Environ. Monit. Assess., 184, 715, 10.1007/s10661-011-1996-8 Quarmby, 1992, Inositol phospholipid metabolism may trigger flagellar excision in Chlamydomonas reinhardtii, J. Cell Biol., 116, 737, 10.1083/jcb.116.3.737 Rwanga, 2017, Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS, Int. J. Geosci., 08, 611, 10.4236/ijg.2017.84033 Shao, 2019, Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation, Remote Sens. Environ., 232, 10.1016/j.rse.2019.111338 Siddiqui, S., Safi, M.W.A., Tariq, A., Rehman, N.U., Haider, S.W., 2020. GIS Based Universal Soil Erosion Estimation in District Chakwal Punjab, Pakistan. Int. J. Econ. Environ. Geol. 11, 30–36. 10.46660/ijeeg.Vol11.Iss2.2020.443. Syed, 2022, Climate Impacts on the agricultural sector of Pakistan: Risks and solutions, Environ. Challenges, 6, 10.1016/j.envc.2021.100433 Tariq, 2022, Modeling spatio-temporal assessment of land use land cover of Lahore and its impact on land surface temperature using multi-spectral remote sensing data, Environ. Sci. Pollut. Res., 95 Tariq, 2023, Spatio-temporal assessment of land use land cover based on trajectories and cellular automata Markov modelling and its impact on land surface temperature of Lahore district Pakistan, Environ. Monit. Assess., 195, 114, 10.1007/s10661-022-10738-w Tariq, 2023, Spatio-temporal variation in surface water in Punjab, Pakistan from 1985 to 2020 using machine-learning methods with time-series remote sensing data and driving factors, Agric. Water Manag., 280, 10.1016/j.agwat.2023.108228 Tariq, 2020, Land surface temperature relation with normalized satellite indices for the estimation of spatio-temporal trends in temperature among various land use land cover classes of an arid Potohar region using Landsat data, Environ. Earth Sci., 79, 10.1007/s12665-019-8766-2 Tariq, 2022, Impact of spatio-temporal land surface temperature on cropping pattern and land use and land cover changes using satellite imagery, Hafizabad District, Punjab, Province of Pakistan. Arab. J. Geosci., 15, 1045, 10.1007/s12517-022-10238-8 Tariq, 2022, Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest, Geo-spatial Inf. Sci., 00, 1 Thanh Noi, 2017, Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery, Sensors (Basel)., 18, 18, 10.3390/s18010018 Tien Bui, 2017, A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area, Agric. For. Meteorol., 233, 32, 10.1016/j.agrformet.2016.11.002 Wahla, 2022, Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models, Geocarto Int., 37, 14963, 10.1080/10106049.2022.2093411 Wahla, 2023, Mapping and monitoring of spatio-temporal land use and land cover changes and relationship with normalized satellite indices and driving factors, Geol. Ecol. Landscapes, 00, 1 Wang, 2019, Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci. Total Environ., 666, 975, 10.1016/j.scitotenv.2019.02.263 White, 1997, A continental phenology model for monitoring vegetation responses to interannual climatic variability, Global Biogeochem. Cycles, 11, 217, 10.1029/97GB00330 Zhou, 2017, Mapping winter wheat with multi-temporal SAR and optical images in an urban agricultural region, Sensors (Switzerland), 17, 1210, 10.3390/s17061210