Remote Sensing, GIS and Crop Simulation Models – A Review

Abstract

A b s t r a c t A r t i c l e I n f oAgriculture continues to be the backbone of Third World economies. In India, more than two-thirds of population depends on agriculture. Agriculture provides the principal means of livelihood for over 58.4% of India's population. So the promotion of agriculture is an integral part of developmental programmes. The advances through information technology and space technology need to be extended to agriculture as well. Agriculture is always vulnerable, because of unfavorable weather and climatic conditions. So, it needs constant monitoring to improve crop productivity. The linkages among crop varieties, irrigation, soil characteristics, weather, etc., which are the key factors in agricultural productivity can be effectively made with the help of Remote Sensing and GIS tools. Crop Simulation Model (CSM) is a valuable tool to researchers to help them to understand the influence of climatic variables on crop productivity. Simulation models also provide global edge to the farmers and researchers since they are objective, fast and cost effective. The scope of applicability of these simulation models can be extended too much broader scales for regional planning and policy analysis by combining their capabilities with a Geographic Information System (GIS). The principle of this study appeal to review on Remote Sensing (RS), GIS and CSM, and on types of models and its limitations. Overview of CSM models in the current scenario, as well utilization of RS and GIS tools that model the impacts of agricultural interventions.

A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

“…Only [44] assimilated MODIS LAI using EnKF in WOFOST to improve wheat yield estimates from small-scale rainfed farming systems in Ethiopia. The lack of such studies can be attributed to crop models inadequately representing uncommon crops and alternative cropping methods (e.g., mixed cropping) usually used by small-scale farmers [6]. In addition, obtaining reliable and sufficient input data for calibration and validation of small-scale farming systems is difficult.…”

“…Process-based crop models (PBCMs) are among the essential numerical tools used to explore the effects of potential sustainable agricultural land management practices on crop growth and yield [4,5]. Such models use mathematical equations to capture the relationship between a crop's environmental conditions and mechanistic biophysical processes [6]. In addition, they focus on dynamically describing the physiological processes that drive plant growth, including photosynthesis, respiration, and evapotranspiration [7].…”

Implementation and application of APSIM for crop modelling in Ethiopia: A comprehensive review

“…Based on the simulation results, the optimized input is subsequently applied to the actual field. Crop simulation models (CSM), which are computer simulations based on mathematical equations, are used in agricultural production to depict how crops evolve [ [4] , [5] , [6] ]. These models incorporate climate data, such as temperature, humidity, solar radiation, and rainfall, as well as soil properties like organic matter, nutrient content, water content, and texture.…”

“…These models incorporate climate data, such as temperature, humidity, solar radiation, and rainfall, as well as soil properties like organic matter, nutrient content, water content, and texture. Additionally, management practices, such as planting dates, irrigation, fertilization, and pest control are considered [ 4 , 5 ]. Crop genetic characteristics and mathematical equations or simulation algorithms are also used as input to predict crop yield, assess the effects of climate change, and optimize management practices [ 4 , 7 ].…”

Spatializing the modelled impacts of future climate change – a case study on wheat in the Western Cape

“…A variety of crop models exist and have particular strengths and weaknesses, particularly in the context of spatial applications (Nagamani et al, 2017). Such models are increasingly used to test the impacts of expected climate change at a detailed physiological level.…”