17
schemes, advanced technologies have been developed to find out (i) crop forecast accuracy at district level, (ii) kharif crop inventory using microwave data, (iii) field data analysis using high resolution data, and (iv) linking crop growth models with remote sensing for improved yield estimation (Dadhwal 1999). In India, this is the only option left to realize farmers’ goal to increase crop production by eliminating cost of production as well as environmental impacts.
18
Wang et al. (2007) demonstrated that the rice yield target responses to K have generally been lesser than that of the designed N and P (Liu et al., 2013). However, there has been a close association between the amount of N fertilizer applied to rice and the yield level. It has been reported that the yield obtained was approximately 20 kg more per kilogram of N applied (Chen et al, 2014). However, this will lead to inefficient nitrogen application and environment pollution. Hence, to protect the environment from excessive application of N fertilizer, the method of applying recommended N is most important to minimize N losses and achieve better nitrogen use efficiency in paddy crop. Many farmers do not use N fertilizer judiciously, whose application varies from upland to wetland rice, resulting in lower production even if high levels of N fertilizer was applied. The season of planting also influences the N requirement of rice (Bhuyan et al., 2012; Tian et al., 2017). Moreover, the rate of N management recommendations must change from single small scale farms to large scale farms. Usually, farmers grow high yielding rice varieties without following best soil-water- nutrient-crop management practices that significantly decreases the rice production and ultimately the economic returns. The use of computer and advanced satellite technology, in the management of N through SSNM, has performed very well resulting in more rice yield per unit fertilizer of N than the existing farmers' practices, without the help of special equipment and large farming operations. The advanced remote sensing technologies have positively amplified the status of rice agriculture system.
Remote Sensing and GIS applications for Agriculture
Real time assessment in agricultural applications is very crucial and use of remote sensing (RS) tools can provide a better solution to achieve this by overcoming the difficulties. Site specific crop management is being followed smartly to enhance crop production by implementing spatial referencing, crop and climate monitoring, crop area mapping, nutrient management, decision support system and preventive measures (Auernhammer, 2001;
Campbell, 1996). GPS is being used to get the spatial variability of crop fields in crop production. Visible and near-infrared (NIR) regions of electromagnetic spectrum have performed well in monitoring photosynthetic radiation, nitrogen stress, soil moisture, crop health, crop growth, crop diseases, crop area, crop age and crop yield (Asner, 1998; Bausch,
TH-2211_126104014
19
1993; Clevers, 1989; Gallo et al., 1985). It is confirmed that RS sensor or the device gathers information from the radiation, transmission, absorption and reflection properties of leaves that can be used as indicators for plant parameters assessment. Chlorophyll reflects certain wavelengths of light within the visible light spectrum and absorbs the rest. It absorbs the blue and red lights and reflects green light thus making the plant appear green. The near infrared (NIR) wavelengths are reflected from the leaf’s internal mesophyll structure (Curran, 1985).
The NIR radiation enters through the topmost layer (Palisade tissue) of the leaf. Then after touching the mesophyll and the internal cavities of leaf, it is partly reflected upwards and partly transmitted downwards (Figure 2.2). The reflectance of visible spectrum is mainly a function of leaf pigments, whereas NIR reflectance is a function of leaf area index (LAI), cell turgor, leaf thickness, leaf internal air and water content (Campbell, 1996). Thus, healthy crops will show high NIR reflectance value and low reflectance in visible region.
Furthermore, RS has been widely used in PA to investigate field crop growth status and its variability. These include the use of ground-based remote sensors, satellite imagery and hand
Figure 2.2 : The visible spectrum for monitoring plant health (Campbell, 1996) Green
Upper epidermal
Palisade mesophyll
Spongy mesophyll Intercellular air space
Lower epidermal Stomata
Red Green Blue Blue Red
TH-2211_126104014
20
held instruments. RS methods are categorized into passive and active remote sensing methods and similarly RS techniques are differentiated into multiple classes like panchromatic, multispectral and hyperspectral methods based on different standards like spatial, spectral and radiometric resolution. These RS classes have exhibited satisfactory results in various dimensions like detection of leaf pigment (Chappelle et al., 1992; Zhao et al., 2005), leaf water content (Hunt, 1991), plant nitrogen (Hansen, 2003; Serrano et al., 2000), potassium (Serrano et al., 2000) and phosphorous content (Osborne et al., 2002), deficiencies, pests, weed stress (Franz, 1991; Tian et al., 1999), salinity stress (Wiegand et al., 1994; Wiegand et al., 1996), LAI (Carlson, 1997), plant biomass production (Koetz et al., 2005), crop age estimation (Fang et al., 1998; Okamoto and Fukuhara, 1996; Tennakoon et al., 1992) and crop yield estimation (Dadhwal and Ray, 2000; Prasad et al., 2007). Previous studies have explored the ability of remote sensing platforms against various agricultural practices and management systems in different parts of the world. Rapid development of optical remote sensing system methods have offered a number of practical means for agricultural applications.