Remote Sensing (Apr 2022)

Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties

  • Roomesh Kumar Jena,
  • Siladitya Bandyopadhyay,
  • Upendra Kumar Pradhan,
  • Pravash Chandra Moharana,
  • Nirmal Kumar,
  • Gulshan Kumar Sharma,
  • Partha Deb Roy,
  • Dibakar Ghosh,
  • Prasenjit Ray,
  • Shelton Padua,
  • Sundaram Ramachandran,
  • Bachaspati Das,
  • Surendra Kumar Singh,
  • Sanjay Kumar Ray,
  • Amnah Mohammed Alsuhaibani,
  • Ahmed Gaber,
  • Akbar Hossain

DOI
https://doi.org/10.3390/rs14092101
Journal volume & issue
Vol. 14, no. 9
p. 2101

Abstract

Read online

Defining nutrient management zones (MZs) is crucial for the implementation of site-specific management. The determination of MZs is based on several factors, including crop, soil, climate, and terrain characteristics. This study aims to delineate MZs by means of geostatistical and fuzzy clustering algorithms considering remotely sensed and laboratory data and, subsequently, to compare the zone maps in the north-eastern Himalayan region of India. For this study, 896 grid-wise representative soil samples (0–25 cm depth) were collected from the study area (1615 km2). The soils were analysed for soil reaction (pH), soil organic carbon and available macro (N, P and K) and micronutrients (Fe, Mn, Zn and Cu). The predicted soil maps were developed using regression kriging, where 28 digital elevation model-derived terrain attributes and two vegetation derivatives were used as environmental covariates. The coefficient of determination (R2) and root mean square error were used to evaluate the model’s performance. The predicted soil parameters were accurate, and regression kriging identified the highest variability for the majority of the soil variables. Further, to define the management zones, the geographically weighted principal component analysis and possibilistic fuzzy c-means clustering method were employed, based on which the optimum clusters were identified by employing fuzzy performance index and normalized classification entropy. The management zones were constructed considering the total pixel points of 30 m spatial resolution (17, 86,985 data points). The area was divided into four distinct zones, which could be differently managed. MZ 1 covers the maximum (43.3%), followed by MZ 2 (29.4%), MZ 3 (27.0%) and MZ 4 (0.3%). The MZs map thus would not only serve as a guide for judicious location-specific nutrient management, but would also help the policymakers to bring sustainable changes in the north-eastern Himalayan region of India.

Keywords