Geospatial Technologies for Crops and Soils

Natural resource monitoring and assessment is a vital step to formulate a sustainable development plan. The introduction of various modern geospatial techniques and tools like Remote Sensing (RS), Geographic Information System (GIS), Global Positioning System (GPS), and information technology (IT) have provided powerful approaches of surveying, identifying, classifying, mapping, monitoring, and characterization of the composition, extent, and distribution of various natural resources. Geospatial techniques deal with the acquirement, storage, processing, production, presentation, and dissemination of geoinformation. The information obtained from RS, GPS, and through conventional methods could be used effectively to create database in GIS platform for various spatial and temporal analysis related to sustainable management of land resource and formulate environment-friendly action plans. Major applications of geospatial technologies related to crops and soils are crop inventory and monitoring, crop production estimates and forecasting, crop growth simulation modeling, crop yield estimation, precision agriculture, soil mapping, land degradation assessment, soil erosion assessment, soil quality assessment, digital soil mapping, digital terrain modeling, soil-landscape modeling, land use/land cover mapping, agricultural land use planning, etc., which have a far-reaching impact on mapping, monitoring, and management of crop and land resources on sustainable basis. Geospatial approaches have made inroads across different sectors both in private and public domain in various countries across the world. Selected tools can help to restore the soil health, stop exploitation of the natural resources, reduce energy consumption, carbon and water footprints, and improve the productivity and sustainability under changing climate. Geospatial technologies for crops and soils a novel tool for the food, nutritional, environmental, and economic security for the future generations under limited natural resources. This book will be helpful for the producers, researchers, teachers, and policymakers to deal with the future alarming issues.

The right time application of the right amount of input is a prerequisite to optimizing profitability and sustainability with a lesser impact on environmental degradation. Such can be achieved through precision farming (PF). It can offer a great potential to minimize the yield gap by optimizing food production using best management practices. It can also help to maintain the consumption of natural resources at an ecologically benign and environmentally sustainable level. PF is a holistic approach to enhance crop productivity with the aid of satellite-based technology and information technology (IT) to assess and manage the spatial and temporal variability of resources and inputs such as seeds, fertilizers, chemicals, etc. within the field. Application of remote sensing (RS) and geographic information system (GIS) shows a great promise to precision agriculture (PA) because of its role in monitoring spatial variability overtime at high resolution. This chapter highlights various applications of RS and GIS techniques in PA or smart agriculture.

Consistent and near-real-time crop growth monitoring over a large scale is a very crucial step for digital agriculture. An efficient tool for accurate retrieval of different biophysical parameters is the basic requirement for crop growth monitoring. Quantitative estimation of various crop biochemical and biophysical variables with reliable accuracy is very useful for different applications related to agriculture, ecology, and climate. This chapter briefly describes different methods and models for the retrieval of various crop biophysical parameters using remote sensing (RS) approaches. Leaf area index (LAI) is a vital attribute in many land-surface vegetation and climate models which have many important applications. Leaf chlorophyll and leaf water content are key parameters in many ecological processes, such as photosynthesis, respiration, transpiration, and they also provide stress information. The fraction of absorbed photosynthetically active radiation (fAPAR) by crop vegetation is used as an essential climate variable (ECVs) and critical input in many land-surface, crop growth and climate, ecological, water, and carbon cycle models. This chapter highlights various retrieval methods of crop biophysical parameters, including empirical, semiempirical, hybrid, physically based models with various inversion algorithms like look-up table, neural network, genetic algorithms, Bayesian networks, support vectors, etc.

Process-based crop growth simulation models (CGSMs) have been proven as a potential tool for analysing crop behaviour and yield prediction in various spatial and temporal scales. Since the early 1960s, the crop growth models (CGMs) have been used broadly: (1) as a tool for the policymakers to make an informed decision for sustainable land management; (2) as a research tool supporting the interdisciplinary studies covering agronomy, plant physiology, agrometeorology, plant breeding, soil science, climate change, market intelligence, etc.; and (3) as a support tool for education and technology transfer. These models are developed as point-based models to simulate the crop growth and development for a homogeneous unit as a function of crop genotype, management practices, soil physico-chemical properties, and weather variables. The point-based applications of this model are best suited to the need of field experimentation, predicting, and analysing the crop behaviour under different environmental scenarios. But this approach is associated with limited applications at a regional scale under a heterogeneous real-world situation. In this context, satellite remote sensing (RS) techniques could supplement the crop growth modelling particularly by generating “the missing spatial information” for the unit of simulation. Though these two technologies developed independently, today, both of them can be used synergistically under various spatial and temporal scales for overall agriculture development under different socio-economic and climate change scenarios. The present chapter will provide a brief introduction of the CGSM, its scope, and development across the time epochs. It would further elaborate on the framework, methodology, and issues to run the CGSMs at the spatial domain. The role of remote sensing technique to retrieve crop biophysical parameters and its assimilation into CGSMs are also discussed along with future scope and challenges.

Satellite-based preharvest estimates of agricultural output are an essential requirement of agriculture management and policy. Optical remote sensing is limited by the cloudy and obscure weather conditions during monsoon season. Microwave signal can penetrate cloud, haze, and fog making it suitable for mapping and monitoring of crops in all weather conditions. The interaction of the microwave with the crop canopy is greatly influenced by the senor characteristics such as wavelength, angle of incidence, and polarization and also the target properties such as surface roughness, crop geometry, and soil and vegetation water content. Synthetic aperture radar (SAR) data has been successfully used for mapping of flooded rice crop. Limited success has also been received for wheat, corn, and soybean crops with reasonable accuracies. Crop biophysical parameters such as leaf area index (LAI) and crop biomass can also be retrieved with limited uncertainty using different wavelength, polarization, and incident angle of SAR data. Synergistic use of SAR and optical data showed promising results in the assessment of crop parameters and condition at regional level. This chapter provides a brief introduction to microwave remote sensing (MRS) and its interaction with crop canopy at different wavelength and polarization followed by few case studies showcasing successful utilization of SAR date for agricultural crop monitoring.

The ever-increasing global population demands a steep increase in food grain production. To cope up with this demand and maintain a steady supply, proper crop monitoring and production forecasting systems are some of the major requirements. Advance estimation of crop yield is useful for different stakeholders to plan standard agronomical practices, procurement, determine storage availability, transportation, price fixation, and marketing of agricultural products. This estimation can be done by statistical analysis using traditional ground-based study or by using remotely sensed data. The developments in the field of satellite and sensor technologies in the last few decades have established the second method as the most trusted and efficient tool to forecast crop production. Its time and cost-effectiveness with precise estimation capacity ascertain its competence. This chapter presents an exhaustive discussion on the role of these methods (particularly satellite remote sensing) in crop yield estimation. Analysis and transformation of space data to process different vegetation indices and their use in crop production estimation have been detailed. These vegetation indices are generally used as an explanatory variable in different traditional and advanced statistical models. Further, recent advancements in modeling techniques have introduced applications like machine learning, artificial intelligence, pattern recognition, mobile computing, etc., and thus opened a new dimension in production forecasting processes. This chapter also tried to focus on these rapidly evolving sectors and their possible contribution to the crop yield estimation.

Light energy absorbed by plant chlorophyll pigments is principally utilized for photosynthesis. The surplus energy is dissipated as heat or re-emitted as chlorophyll fluorescence (CF). The CF is wavelength specific and directly linked to the efficiency of the photosystems I and II. Hence, it is one of the few direct assessments of vegetation condition, growth processes, and productivity. The active CF retrievals are computationally simple but lack scalability; hence, passive measurement in terms of solar-induced chlorophyll fluorescence (SIF) from ground-based, airborne, and space-borne instruments is popular for regional or global monitoring of vegetation condition. The retrieval of SIF from upwelling radiance from vegetation canopy, though complex, is one of the promising developments in the field of remote sensing. Significant research works have been done on the instrumentation, measurement, retrieval, and application of CF for crop/vegetation monitoring and assessment. The present book chapter reviews the basic concepts of chlorophyll fluorescence, its measurement, major SIF retrieval techniques and its applications along with future challenges.

The regular monitoring of soil physical, chemical, and biological properties is very essential, due to its role in soil ecosystem functions. A cost-effective alternative for soil monitoring corresponds to spectral sensing techniques. Soil spectral sensing techniques can support decision-making in agricultural systems at both time and spatial scales, maximizing food production while preserving an adequate soil condition. Due to the large number of ground, airborne, and orbital spectral sensors operating today, this technology has been increasingly assimilated by soil scientists. However, it is important to have an adequate comprehension about the technique principles and limitations. This chapter provides a wide perspective about the soil spectral sensing in the visible (vis: 350–700 nm), near-infrared (NIR: 700–1000 nm), and shortwave infrared (SWIR: 1000–2500 nm), considering reflectance data at different acquisition levels. Here, it is discussed how soil constituents interact with EMR and the resulting soil spectral behaviors. We describe the predictive potential of vis-NIR-SWIR data for quantitative assessment of soil and which soil attributes have been reliably estimated and the most commonly used vis-NIR-SWIR equipment, as well as their advantages and limitations. Finally, we discuss the current application in soil science and future perspectives.

Digital soil mapping (DSM) involves in development of a statistical or mathematical model to estimate soil class or properties at unsampled locations using information on spatial variation of soil properties and different covariates affecting soil formation process. There are three main approaches followed in DSM, and these are geostatistical approach, state-factor (clorpt) approach, and pedotransfer function (PTF) approach. In the geostatistical approach, spatial variation parameters (nugget, sill, and range) are identified from a spatial soil database using semivariogram followed by making unbiased estimate of soil properties at unsampled location through kriging. In the state-factor (clorpt) approach, the soil formation theory is the backbone. In this approach soil is considered to be influenced by five major factors: climate (cl), organism (o), relief (r), parent material (p), and time (t). Therefore, abundantly available information on these factors in different digital platforms are exploited to develop model to estimate soil properties at unsampled location. The PTF approach is used to develop digital soil maps of complex soil properties and difficult to measure soil properties. In this approach digital soil map of basic soil properties is first developed using the first two approaches, which are then combined to map of complex soil properties through PTF model. All these three approaches of DSM are discussed in detail along with assessment of its accuracy and uncertainty. Through the DSM approaches, available legacy soil data may be converted to digital products for its better accessibility and utility, e.g., through development of soil information system.

Soil moisture is required to understand the land surface processes, land-atmosphere interaction, drought forecasting, crop growth patterns, etc. It is a dynamic variable that changes significantly on different spatial and temporal scales even in a smaller area. Remote sensing (RS) techniques provide an alternative way to estimate the high spatial and temporal variability of soil moisture. This chapter includes the state of art and techniques used to retrieve soil moisture from satellite RS in different parts of the world. Several techniques have been developed to retrieve soil moisture either from optical/thermal/microwave sensors or fusion of these sensors, but microwave sensors either with a fine spatial resolution (and coarse temporal resolution) or with a coarse spatial resolution (and fine temporal resolution) seem promising than optical and thermal sensors depending on applications. However, microwave sensors have shown its high potential and capability for deriving global soil moisture information due to all weather capability and longer penetration depth. Many operational products of soil moisture have been developed using passive microwave sensors; however, coarse spatial resolution and penetration depth over vegetation-covered surfaces are the major factors that limit the utility of these soil moisture products for agricultural purposes. The major initiatives have been taken by various space agencies across the globe to develop the microwave sensors with L and/or S bands for its potential use in soil moisture besides other applications. Sentinel-1 synthetic aperture radar (SAR, active microwave sensor) data has opened up a new research area to develop high-spatial-resolution soil moisture products for agricultural applications. Several studies have shown the potentials of Sentinel-1 SAR data and high-resolution optical data along with operational products (like SMAP) to downscale the coarse soil moisture data to retrieve high spatial soil moisture at a regular interval over vegetation-covered surfaces. However, future sensors are required to estimate soil moisture from depth up to 0.7–1 m (microwave sensors with P band) over sparse vegetation areas and varying surface roughness using active and passive microwave sensors.

Unsustainable use of land resources leads to degradation of soil resulting decline in soil functions such as crop productivity, regulation of the hydrological cycle, water quality, and soil quality. Soil quality is influenced by inherent and anthropogenic factors. It is used to evaluate soil resource functions as how well soil performs for all its functions at present and how these functions will be preserved for future use. It cannot be measured directly, so we evaluate indicators. Indicators are measurable properties of soil. Indicators can be physical, chemical, and biological properties or characteristics of soils. Soil quality indices are usually used for the objective measurement of soil quality. These are useful tools for assessing the overall soil condition and response to management towards natural and anthropogenic factors. It helps to determine what conservation practices are needed to protect soil and water resources. The geospatial technique helps in providing spatial distribution of soils and representation of soil quality. Satellite remote sensing data and derived digital elevation models (DEMs) are used to map soils and landforms to evaluate soil quality. Soil quality assessment has been recognized as an important step towards understanding the long-term effects of various land management practices. It will help the land managers in preparing land use plans and management decisions for optimal use, hence assisting in sustainable land management. The chapter discusses various geospatial modelling methods in soil quality assessment.

Land degradation is a thoughtful threat involved in reducing area and productivity of 13.4 billion ha in the global cultivable land. The genesis and distribution of different types of land degradation processes depend on climate, topography, vegetative cover, parent material (salty or acidic), and groundwater (saline, sodic, or heavy metals/metalloid). Above all, human-induced degradation of land has been exaggerated recently. These changes in land degradation can be monitored and assessed through geospatial techniques such as remote sensing (RS) and geographic information system (GIS) with fine spatial and spectral resolution imageries. The advanced techniques such as microwave, hyperspectral, and proximal ground-based sensor data with multivariate statistical algorithms have increased the efficiency of classification and mapping of degraded lands. The values of different parameters extracted from thematic map of the terrain, surface, hydrology, and spectral ratio indices of multispectral, hyperspectral images are used as an input parameter for the generation of a digital soil map. The digital soil map with seasonal/temporal variation (possible with fine temporal resolution) conveys detailed information regarding the study of changes, characterization, causes, protection, and reclamation of the land degradation processes. The method of real-time monitoring and assessment of land degradation using RS/GIS techniques is cost-effective, fast, and accurate and indicates land/resource management quickly to secure the food, water, and environmental security. This chapter summarizes the comprehensive understanding of the extent, type, cause of land degradation processes, and indicators of land degradation as well as assessment and monitoring of such through advanced remote sensing techniques.

Groundwater irrigation plays an important role in sustainable agricultural development through protective shield during droughts and dry spells and intensifying and diversifying of the cropping system. The measuring, monitoring, and modeling of groundwater availability, condition, and distribution are the major step to formulate a sustainable groundwater management plan for agricultural use. The conventional methods to manage groundwater are tedious and costly. However, the modernization of geospatial techniques, namely, remote sensing (RS), geographic information system (GIS), Global Positioning System (GPS), etc., along with differential proximity sensing has enabled groundwater management both spatially and temporally. It can help in surveying, analyzing, detecting, differentiating, characterizing, mapping, monitoring, and modeling of the groundwater quantity, quality, distribution, extent, and association of groundwater resources. The major interventions of geospatial techniques in groundwater management are groundwater quality assessment, spatial zonation for irrigation, groundwater prospects mapping, dynamicity of groundwater storage, saltwater intrusion, etc. These applications have made a huge impact on groundwater management for crop and land resources on a sustainable basis. The multiparametric approach of geospatial techniques can minimize the time, labor, and money and thereby enable quick decision-making for efficient water resources management. However RS data has some inherent limitations of spatial, spectral, and temporal resolution, which sometimes makes it difficult to understand and asses the groundwater condition. Still, it is very important for the areas/regions especially developing nations where data scarcity in terms of quantity and quality is often an obstacle for solving real-world water problems. This chapter highlights the various approaches of groundwater management for irrigated agriculture using geospatial tools and techniques.

The number of city dwellers around the world is expected to increase about 2.5 billion between 2018 and 2050. This increment will lead to urban sprawl which is associated with destruction of agricultural lands, loss of fertile soils and reduction in food production. Already around 3–4% reduction of global crop production has been reported, in which Africa tops the list with 9% loss followed by Asia (5–6%). Hence, impact assessment of urban sprawl on agricultural land uses at both regional and global scale is required. The data from global satellite imageries and new geospatial technologies can play a crucial role in facilitating the impact assessments with precision and regularity. Remote Sensing (RS) and Geographic Information System (GIS) coupled with various modelling techniques have been proved to be an efficient tool for the analysis of land use/land cover (LULC). Such modelling approaches can be utilized to explore potential future impact of urban expansion on croplands and evaluate potential trade-offs between different land demands and thus are helpful for informed decision-making. This chapter emphasizes on the usage of RS and GIS to address the impact of urbanization on agricultural lands.