Rice Equivalent Crop Yield Assessment Using MODIS Sensors' Based ...

IEEE SENSORS JOURNAL, VOL. 14, NO. 10, OCTOBER 2014

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Rice Equivalent Crop Yield Assessment Using MODIS Sensors’ Based MOD13A1-NDVI Data Vandana Tomar, Vinay Prasad Mandal, Pragati Srivastava, Shashikanta Patairiya, Kartar Singh, Natesan Ravisankar, Natraj Subash, and Pavan Kumar, Member, IEEE Abstract— This paper analyzes the site-specific infield fertilizer treatments, its application rate discrepancies and crop yield assessment using rice equivalent productivity in terms of their economic potential using MOD13A1-normalized difference vegetation index (NDVI) (a moderate-resolution imaging spectroradiomete derived 16 day composite normalized difference vegetation index product, with spatial resolution of 500 m). Soil quality and final crop yield response to nitrogen (N), phosphorus (P), and potassium (K) fertilizers were taken from selected experimental Agri-plots in the part of Kuru region in North India, to calculate site-specific rice equivalent yield (REY) in the crop year of 2005–2006. A 3 × 3 spatial window average pixel reflectance of the NDVI layer at the regional level was used to assess its relationship with contemporaneous cropping systems, such as rice-wheat, rice-sugarcane, and rice-onion in the study area. A robust linear regressive relationship of R2 = 0.69, has been found between site-specific vegetation index values and calculated REY. Inverse distance weighted spatial interpolation method was used to analyze the spatial variability of three major fertilizer nutrients (NPK) response in the study area. The potassium nutrient availability showed high levels of spatial autocorrelation, suggesting that proper fertilizer application ratio with genuine irrigation practices may be used for underpinning of the high crop yield variety acreages. In order to strengthen the crop productivity, we have suggested the diversified triple-based cropping systems with satellite mounted sensor derived NDVI products as a holistic and feasible monitoring approach. Index Terms— MODIS, REY, NDVI, fertilizers.

I. I NTRODUCTION

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NDIA is acclaimed as a commendatory landscape for tenant farmers. Embedded use of mineral fertilizers disturbed the equilibrium between localized production and rising demand by population for more agricultural products [1], [2]. The crop yield is an indispensable element in rural development Manuscript received May 1, 2014; revised June 1, 2014; accepted June 1, 2014. Date of publication June 5, 2014; date of current version August 29, 2014. The associate editor coordinating the review of this paper and approving it for publication was Prof. Aime Lay-Ekuakille. V. Tomar is with Haryana Institute of Public Administration, Gurgaon 122001, India (e-mail: [email protected]). V. P. Mandal, N. Ravisankar, and N. Subash are with the Department of Croping System, Project Directorate for Farming Systems Research, Meerut 250110, India (e-mail: [email protected]; [email protected]; [email protected]). P. Srivastava, S. Patairiya, and P. Kumar are with the Department of Remote Sensing, Banasthali University, Tonk 304022, India (e-mail: [email protected]; [email protected]; [email protected]). K. Singh is with the Department of Remote Sensing, Birla Institute of Technology, Ranchi 835215, India (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSEN.2014.2329185

and an indicator of national food security [3]. However, the pivotal implementation of Precision Agriculture (PA) concept at farm level to obtain high crop yield and high profitability necessarily depends on the Site-Specific Crop Management (SSCM) strategies. The PA-SSCM acknowledges the in-situ field variability by optimizing nutrient inputs on the Agri-plot basis. It depends on an authoritative spatial database of crop related circumstances and their elucidations related to fertilizer application rates towards negatively affected crop areas. Remote Sensing, as a satellite navigation based tool, could be a substitute to the conventional farming systems for synoptic monitoring of crop acreage. Therefore, a number of sensors prominently dedicated to precision farming have been developed to enhance the profitability of agriculture sector. Some of the prominent examples of the ground based sensors are MiniVeg-N Laser System, GreenSeeker®Sensor, Crop-Circle, Yara N-Sensor and Crop-Meter used for determining crop nutrient requisites based on their spectral properties. In spite of the above stated sensors some of the Active Canopy Sensor Equipment’s (ACSE) are also have been used to evaluate the crop status in the Agri-fields such as the CC-210 and GS-506. Remote Sensing appliances are used as a technology for studying cropping patterns by collecting pre and post-season imagery data [4]. Multi-temporal remote sensing signatures counsel valuable spatial information to determine the crop condition in both crop preparation and harvesting periods. The most prevalent utilization of remote sensing technique in the field of precision agriculture is for monitoring of seasonal variations in crop condition [5]. Multitudinous satellitemounted sensors have been developed to assess the spatiotemporal variations of soil and crop conditions for different geographical locations. It has been found that Synthetic Aperture Radar (SAR), ALOS PALSAR-1 and 2 (Single & Dual beam), ENVISAT ASAR (wide swath), RISAT-1, MODIS, Terra SAR-X (Strip Map & Scan SAR), Sentinel-1A, 1B and Sentinel-3, Proba V, Cosmo-SkyMed (wide region), LANDSAT-TM, ETM+, SPOT, are satellite-based sensors have the appropriate characteristics (spectral and temporal) to properly monitor the crop acreage [6]. RADARSAT-1 and 2 are dedicated to provide useful data related to weed infestation, crop and soil parameters. SAR data can also be very useful for digital elevation model (DEM) generation, which is important in the characterization of watershed delineation [7]. The NDVI and red-NIR ratio have been widely used for monitoring of the crop biophysical conditions and multi-temporal normalized difference vegetation index (NDVI) derived from

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Agri-fields have been selected for the experiment and their owners were interviewed. It was ensured that the experimental plots were not confounded with other crop rotations and fertilizer amendments. The farmers were interviewed for recording their current crop management practices and RiceWheat production levels during the crop year of 2005-06. Soil samples were collected at 0 to 15 cm depth from experimental plots with the consideration of post wheat harvesting and before commencement of the experiment. Soil samples were examined to determine the availability of N, P, K, organic carbon (OCsoil%) pH and electrical conductivity (ECsoil ). The vanadomolybdate yellow color method and flame photometry have been used to determine the P and K content respectively. The extensive laboratory based analysis of the soil samples have been performed using the following equations: (S − B) ×0.00028×106 (1) Wt of soil sample 10(B − S) × 0.003 × 100 (2) OCsoil% = Wt of soil sample Q × V × 2.24×106 Pavailable = (3) A × S×106 C × 25×106 ×2.24 (4) Kavailable = 5 × 106 where; S = Sample reading; B = Blank reading; Q = Quantity of P in (µg ) on X-axis against a sample reading; V = Volume of extracting reagent used (mL); A = Volume of aliquot used for colour development (mL); S = Weight of soil sample taken (g); C = Concentration of K in the sample obtained on X-axis, against the reading. For straightforward appraisal of the individual crop productivity in terms of market profitability, the economic product of the individual crop was converted to REY based on the local market prices as following:    (5) REYcropx = Yieldx Pricex Pricer Nsoil =

Fig. 1.

Location of the study area.

the low resolution NOAA-AVHRR satellite data for rice yield prediction [8], [9]. By using the soil sampling, farmer interaction and Remote Sensing & Geographical Information System approach, this study attempts to investigate the final crop yield in terms of REY of the particular crop acreage with the following main objectives: (i) To evaluate the effects of three major fertilizer nutrients N, P, K on the final crop productivity and estimation of the REY on the basis of market price influences (ii) to assess the relationship between REY and Moderate-resolution Imaging Spectroradiometer (MODIS) sensor based normalized difference vegetation index MOD13A1-NDVI product. II. M ATERIALS AND M ETHODS

Where Yieldx is the yield of individual crop(x) in tons harvest product ha−1 , Pricex is the price of crop (x) and Pricer is the market price of rice.

A. Study Site Description The state of Haryana, INDIA, lies between 27° 39 -30° 55 N and 74° 27 -77° 36 E (Fig. 1) and notably, this region is the part of very fertile Indo-Gangetic Plain (IGP) also called as “Rice-Wheat Bowl” because, it comprises Asia’s largest ricewheat cropping system with a continental climatic effect. The quaternary sediments of the aeolian origin cover the Haryana state. The study area encompasses the Sub-Himalayan system of rocks, predominantly concomitant to Shiwalik group. The “Yamuna” and “Ghagghar” are the major river system in this geographical area. B. Field Based Investigations and Soil Sampling The comprehensive stratified random soil sampling and questionnaire have been performed to know the past fertilizer/soil treatment information over the 25 locations of 6 districts of Haryana state. Only Rice and Wheat cultivated

C. Satellite Image Processing and Vegetation Index The MOD13A1 (16-day composite NDVI image product with spatial resolution of 500 m, acquired on June 10th , 2005 was used in this work. The satellite image has been pre-processed to rectify the geometrical errors [10], [14]. Presumably, the Bi-directional Reflectance Distribution Function (BRDF) of the target was considered minimal in this study. The pixel match-up analysis with respect to ground sample location was conducted using a 3 × 3 moving window method to minimize the inherent noises in geometrical differences in the satellite image. The average pixel reflectance values of NDVI product have been used to assess the site-specific crop productivity. A rigorous statistical analysis has been performed to explore the relationship between REY and vegetation index. Once the index pixel values were extracted, the spatial variation of the yield response to available NPK nutrients was evaluated.

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TABLE I T OTAL F ERTILIZER U SE , AVAILABILITY AND O THER L AB M EASURED S OIL Q UALITY PARAMETERS W ITH T HEIR R ESPECTIVE R ICE E QUIVALENT Y IELD (REY)

Fig. 3. Spatial co-linearity between REY and MODIS derived NDVI product.

Fig. 2.

NDVI map derived from MOD13A1 product of MODIS sensor.

III. R ESULTS AND D ISCUSSION A. Soil Fertility and Available N, P, K Budget Rice-Wheat (R-W) rotation was found as dominant Rice Based Cropping System (RBCS), which occupies 96% of the total cropping area. The Rice-Sugarcane (R-S) and Rice-Onion (R-O) were found as minor RBCS in the state of Haryana. Rotation based cropping system alters the soil quality and vice versa. For the experimental plots the Inherent Soil Indicators (ISI’s) e.g. pH , EC and OC values were found in the range of 7.5 to 8.2, 0.20 to 1.1 and 0.48 to 0.66 respectively (Table I). during the experiment it has been observed that the ISI’s were not very influential in affecting the final crop yield. The long term rigorous analysis related to Dynamic

Soil Indicators (DSI’s) were also required to evaluate the productive integration of the soil factors with respect to overall crop yield in this geographical location. In the district of Sonipat, it has been found that N fertilizer treatment rate in R-W cropping system (Csystem ) was high that introduces the acidification process of the soil in the area. ECsoil decreased in R-S and R-O Csystems , irrespective of whether rice and wheat were grown (Table I). The lowest OCsoil % was observed in the district of Ambala, this declining trend was particularly concerned with sustainable growth of all the cropping systems. The excessive tillage practices, removal of crop residues and continuous farming were the main stimulating causes of the declined OCsoil % in this area. The escalation in OCsoil% was found in the R-W cropping system of “Karnal” and “Sirsa” Districts of the Haryana State. The high availability of K nutrient in rice crop was recorded in “Sirsa” (237 kgha−1), whereas the same was lowest in “Ambala” (139 kgha−1). The overall K (Used in kgha−1) varied in the experimental plots from 0 to 165 kgha−1

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Fig. 4.

Spatial variability of Nitrogen (N) under rice based cropping systems.

TABLE II R ELATION B ETWEEN REY U NDER R ICE BASED C ROPPING S YSTEM

(Table I). These experimental results confirmed the highly decreasing trends in K (Use) in fertilizer practices over the state of Haryana and it can be compared with other longterm studies in Rice–Rice and R-W Csystems of the Asia. The variations in P availability were mainly due to alternating periods of aerobic–anaerobic processes and their influence on the Ca-phosphates and Fe in soil [15]. The seed materials and irrigation water contributed as major sources of P nutrients in the soil. It is observed that the Plant Available Nitrogen (PAN) in the soil directly affects the potential crop yield, because in the critical phases of the plant growth only the N fertilizer application necessarily influences the vegetative growth. After the careful investigation it has been manifested that the western part of the Haryana, comprised with the highest load of N fertilizer as compared to other agricultural lands of the state. But, the N nutrient availability cannot be predicted accurately because the N fertilizer treatment depends on several critical factors such as weather dependency can severely influence the N component ratios in the soil. In this study, the quantity of N nutrient varies from 100-198 kgha−1 and 141-280 kgha−1 for the “Kharif” and “Rabi” seasons respectively. B. REY and Effects of Available N, P, K Nutrients The final crop yield and its quality have been determined in terms of REY using the Eq. 5. The crop yield in a particular

area depends on several factors and they can vary within an experimental plot. When these environmental determinants vary spatially, the final crop yield can be arduous to estimate. In this experiment the total experimental crop acreage has been divided into three major REY groups on the basis of their crop productivity, fertilizer application rate and soil quality parameters (Table II and Fig. 7). Nevertheless, the other factors such as market prices and fertilizer applicability of other mineral chemicals may drastically influence the overall crop production capacity in the area. It has been observed that the P application rate of 13 kgha−1 positively affects the yield of rice crop by 0.87 tha−1 . Raising P level from 13 to 26 (kgha−1) produced 0.09 to 0.63 tha−1 additional rice grain at different locations of the experimental plots. Increasing P rates also produced an additional wheat grain yield of 0.44 to 0.74 tha−1 , which was 0.5 tha−1 or greater at 5 locations. The averaged application of 33, 66 and 99 kg K produced grain yield response of 0.53, 0.86 and 0.96 tha−1 respectively. The optimization studies revealed that the N content ranges from 168-188 kgha−1 whereas the quantity of Nitrogen (Use) varies from 225-325 kgha−1 in the investigated crop acreage. Increased ECsoil can negatively influence the final crop productivity [16]. In case of “Sirsa” district the REY was calculated as minimum of 6 tha−1 , which was passively affected by the increased ECsoil . After careful investigation of the spatially interpolated maps of fertilizer nutrient availability and crop yield, it has been stated that the northern part of Haryana shows the minimal ranges for N nutrient and the upper middle region extracted as the zone of higher use N content (Figs. 4–6). C. Crop Yield (REY) and Vegetation Index MODIS Terra Sensor derived NDVI product evinces that the vegetation index can be represented as a scalar quantity that accurately estimates the crop productivity on the basis of its spectral signatures. The temporal phonological changes in site specific cultivated crops can be easily monitored using the vegetation indices. In this study, through a combination

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Fig. 5.

Fig. 6.

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Spatial variability Potassium (K) under rice based cropping systems.

Spatial variability of Phosphorus (P) under rice based cropping systems.

of MOD13A1-NDVI, application rate discrepancies of nutrient fertilizers and soil quality parameters; a Geoinformatics based appraisal has been suggested. It is shown that using vegetation index (NDVI), farmers can fairly assess their farm crop yield. In this experiment the average pixel reflectance values have been correlated with final calculated REY, which was also based on the other market parameters and variable nutrient rates (Fig. 3). We have examined the relationship between final rice equivalent yield and NDVI, based on satellite image reflectance values and integrated results of fertilizer applications in the experimental plots that exhibited the crop health pattern in terms of its canopy greenness. The positive robust linear relationship with R2 = 0.69 was found between REY and vegetation index Thus, NDVI can serve as a reliable estimator of crop productivity in this area (Fig. 2). It has been also found that the crop rotation and variable rate fertilizer treatments directly affect the final crop productivity regardless of irrigation systems. Our experiment

results suggested that both satellite sensor based vegetation index and in-field inventories can be used to develop crop yield prediction models for the assessment of site-specific nutrient fertilization discrepancies in rice based and other cropping systems. The extensive practical efforts have been made for pivotal implementation of NDVI as a good estimator of the crop yield in this study area. On the basis of monitoring data of the each crop phase it has been stated that the final REY was strongly influenced by the in-situ environmental conditions. Therefore, we utilized the 16 day composite MODIS-NDVI product that responds to the crop dynamics very effectively. Although, in this study the highest pixel reflectance value of 0.6235 was observed for NDVI product but, the pixel reflectance value of 0.429 was correspond to the REY=14 tha−1 which was maximum estimated REY for the crop yield assessment period. The prime rationale to select the NDVI profile values for the assessment of the economical productivity of the cultivated crop was only its potential to

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fields, but Remote Sensing & Geographical Information System provides the economical solution in a very feasible manner. There is a need for detailed investigation related to soil characteristics at farm level which have a very prominent effect on nutrient availability and irrigation water holding capacity. The characteristics of the crushed soil can be effectively measured using Radar reflectivity because the polarimeter and interferometric Synthetic Aperture Radar (SAR’s) has the centimetric level capability to detect the changes in soil characteristics using its backscattering properties [17]. The quality of soil and subsoil has a major impact on the final crop productivity. To evaluate the soil morphologic modifications, substratum effects, erosion susceptibility and climatic danger, the advanced satellite based monitoring systems like ERS-2/SAR data can be used [18]. The relationship between the major nutrients, final crop yield and vegetation index should be evaluated to establish an accurate efficiency level of fertilizer use in this area. This study did not consider the residual effects; it should be considered to increase the accuracy level in the yield estimation process. ACKNOWLEDGMENT

Fig. 7.

Spatial variability of rice equivalent yield.

properly indicate the vegetation health in terms of crop green biomass and capacity to reduce the effects due to the surface topography and changing illumination conditions. IV. C ONCLUSION The Indo Gangetic Plain (IGP) is one of the most extensively cultivated areas in the Asia, and the fertilizer treatment practices are very common in this zone. This study revealed that the farmers of the Haryana state basically use the Nitrogen fertilizer treatment in a more extensive way in comparison to Phosphorus, Potassium and other micro-nutrients. An increase in P use efficiency consequential to inclusion of K in a fertilizer planning and vice-versa implies that the necessity of both P and K application in adequate amount to achieve volumetric efficiencies. After the evaluation of the field based inventories, it can be suggested that diversified triple based cropping systems can be a very effective alternative cropping systems to enhance the rate of crop productivity and net return income in this area. This study demonstrated the relationship between MODIS derived NDVI product and REY which exhibited a fine co-linearity under variable fertilizer application rates. Although, the complexity of agriculture ecosystem interrupts the accuracy level of yield estimation, but the MOD13A1-NDVI has been established as a holistic indicator of the crop status. In the present scenario ample number of ground based sensors are available in the market for the assessment of the fertilizer application rates in the cropping

We boundless pleasure and heartfelt thanks to Space Applications Centre (SAC), Ahmedabad, India for ancillary data. They also thank the Image Processing Lab, Department of Remote Sensing, Banasthali Vidyapith, Rajasthan, India, for providing all necessary support. R EFERENCES [1] M. P. Sharma, M. Yadav, K. Yadav, R. Prawasi, P. Kumar, and R. S. Hooda, “Cropping system analysis using remote sensing and GIS: A block level study of kurukshetra district,” J. Agricult. Biol. Sci., vol. 6, no. 10, pp. 45–61, Oct. 2011. [2] W. I. Woods, N. P. S. Falcão, and W. G. Teixeira, “Biochar trials aim to enrich soil for smallholders,” Nature, vol. 443, p. 144, Sep. 2006. [3] W. G. M. Bastiaanssen and A. Samia, “A new crop yield forecasting model based on satellite measurements applied across the indus basin, pakistan agriculture,” Agricult. Ecosyst. Environ., vol. 94, no. 3, pp. 321–340, Mar. 2003. [4] K. R. Manjunath, “Remote sensing and GIS applications for crop systems analysis,” Invited Lecture Delivered During NNRMS Training Programme Geoinformatics Sustainable Development. Hisar, India: Haryana Remote Sensing and Applications Centre, 2006. [5] M. S. Moran, A. Vidal, D. Troufleau, Y. Inoue, and T. Mitchell, “Combining multi-frequency microwave and optical data for farm management,” Remote Sens. Environ., vol. 61, no. 1, pp. 96–109, Jul. 1997. [6] P. Bisht, P. Kumar, M. Yadav, J. S. Ravat, M. P. Sharma, and R. S. Hooda, “Spatial dynamics for relative contribution of cropping pattern analysis on environment by integrating remote sensing and GIS,” Int. J. Plant Prod., vol. 8, no. 1, pp. 1–17, 2014. [7] P. W. Vachon, D. Geudtner, A. L. Gray, and R. Touzi, “ERS-1 Synthetic aperture radar repeat-pass interferometry studies: Implications for radarsat,” Can. J. Remote Sens., vol. 21, no. 4, pp. 441–454, 1995. [8] J. Huang, X. Wang, X. Li, H. Tian, and Z. Pan, “Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA’sAVHRR,” PLoS ONE, vol. 8, no. 8, p. e70816, Aug. 2013. [9] J. Lofton, B. S. Tubana, Y. Kanke, J. Teboh, H. Viator, and M. Dalen, “Estimating sugarcane yield potential using an in-season determination of normalized difference vegetative index,” Sensors, vol. 12, no. 6, pp. 7529–7547, 2012. [10] P. Kumar, L. K. Sharma, P. C. Pandey, S. Sinha, and M. S. Nathawat, “Geospatial strategy for forest biomass estimation of tropical forest of sariska wildlife reserve (India),” IEEE J. Sel. Topics Appl. Earth Observat. Remote Sens., vol. 6, no. 2, pp. 917–923, Apr. 2013.

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[11] M. Rani, P. Kumar, M. Yadav, and R. S. Hooda, “Wetland assessment and monitoring using image processing technique: A case study of Ranchi, India,” J. Geograph. Inf. Syst., vol. 3, no. 4, pp. 345–350, Oct. 2011. [12] P. Kumar, B. K. Singh, and M. Rani, “An efficient hybrid classification approach for land use/land cover analysis in a semi-desert area using ETM+ and LISS-III sensor,” IEEE Sensors J., vol. 13, no. 6, pp. 2161–2165, Jan. 2013. [13] P. Kumar, D. Kumar, V. P. Mandal, P. C. Pandey, M. Rani, and V. Tomar, “Settlement risk zone recognition using high resolution satellite data in jharia coal field,” Life Sci. J., vol. 9, no. 1, pp. 1–6, 2012. [14] V. Tomar, P. Kumar, M. Rani, G. Gupta, and J. Singh, “A satellite-based biodiversity dynamics capability in tropical forest,” Electron. J. Geotech. Eng., vol. 18 pp. 1171–1180, Sep. 2013. [15] A. Dobermann, K. F. Bronson, and C. S. Khind, “Optimal phosphorus management strategies for wheat-rice cropping on a loamy sand,” Soil Sci. Soc. Amer. J., vol. 64, no. 4, pp. 1413–1422, 2000. [16] K. Singh, P. Kumar, and B. K. Singh, “An Associative relational impact of water quality on crop yield: A comprehensive index analysis using LISS-III sensor,” IEEE Sensors J., vol. 13, no. 12, pp. 4912–4917, Dec. 2013. [17] G. Andria et al., “Accuracy assessment in photo interpretation of remote sensing ERS-2/SAR images,” in Proc. 17th IEEE IMTC, Baltimore, MD, USA, May 2000, pp. 392–394. [18] A. L. Ekuakille, F. Tralli, and M. Tropeano, “Land modification measurements using ERS-2 satellite images,” in Proc. 16th IMEKO World Congr., Vienna, Austria, Sep. 25–28, 2000.

Vandana Tomar received the B.Tech degree in electronics from Maharshi Dayanand University, Rohtak, India, in 2011, and the M.Tech. degree in remote sensing from Banasthali University, Tonk, Rajasthan, India, in 2013. She is currently a Research Officer with the Haryana Institute of Public Administration, Gurgaon, India. She has academically excelled and has drawn others to her pace through enthusiasm, confidence, and determination. She has presented several papers in national and international conferences, and published articles in NESA newsletter.

Vinay Prasad Mandal received the M.A. degree in geography and the Advanced Diploma in remote sensing and GIS applications from Jamia Millia Islamia University, New Delhi, India. He is currently a Research Associate with the Project Directorate for Farming Systems Research, Meerut, India. His research work focuses on agro-climatic changes and remote sensing technology.

Pragati Srivastava received the B.Tech. degree in electronics from BMAS Engineering College, Agra, India, in 2011. She is currently pursuing the M.Tech. degree in remote sensing at Banasthali Vidhyapith, Tonk, India. Her research work focuses on sensor development, water resources management, urban planning, and geospatial technology.

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Shashikanta Patairiya received the B.Tech. degree in electronics from the Raj Kumar Goel Institute of Technology for Women, Ghaziabad, India, in 2012. She is currently pursuing the M.Tech. degree in remote sensing at Banasthali Vidhyapith, Tonk, India. Her research work focuses on forest resource management, natural resource development, and biooptical modeling using geospatial technology.

Kartar Singh received the M.Sc. degree in geoinformatics from the Birla Institute of Technology Mesra, Ranchi, India, in 2011, where he is currently a Ph.D. Research Scholar with the Department of Remote Sensing. His research work focuses on hyperspectral remote sensing, oceanography, biodiversity conservation, and satellite-based biooptical modeling.

Natesan Ravisankar is a Principal Scientist of Agronomy with the Project Directorate for Farming Systems Research, Meerut, India. His main research work included crop diversification, integrated farming systems for coastal and degraded lands, and water resource creation and utilization for multiple journals.

Natraj Subash is a Senior Scientist of Agro-Meteorology with the Project Directorate for Farming Systems Research, Meerut, India. He has a number of research papers in NAAS-rated national and international journals.

Pavan Kumar received the B.Sc. degree in botany and the M.Sc. degree in environmental science from Banaras Hindu University, Varanasi, India, in 2006 and 2008, respectively, and the M.Tech. degree in remote sensing from the Birla Institute of Technology Mesra, Ranchi, India, in 2010. He was a Lecturer with the Department of Remote Sensing and GIS, Jammu University, Jammu, India, in 2010. He was a Junior Research Fellow with the Haryana Space Applications Centre, Hisar, India, in 2010, where he was involved in crop forecasting and corron crop acreage estimation. He is currently an Assistant Professor with the Department of Remote Sensing, Banasthali Vidyapith, Tonk, India. His current research interests include forestry mapping, crop forecasting, climate change, and urban heat island.