In this study, CO
2
exchange over sugarcane and wheat growing season was quantified by continuous measurement of CO
2
fluxes using eddy covariance (EC) system from January 2014 to June 2015. We also ...elaborated on the response of CO
2
fluxes to environmental variables. The results show that the ecosystem has seasonal and diurnal dynamics of CO
2
with a distinctive U-shaped curve in both growing seasons with maximal CO
2
absorption reaching up to −8.94 g C m
−2
day
−1
and −6.08 g C m
−2
day
−1
over sugarcane and wheat crop, respectively. The ecosystem as a whole acted as a carbon sink during the active growing season while it exhibits a carbon source prior to sowing and post-harvesting of crops. The cumulative net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (
R
eco
) were −923.04, 3316.65, and 2433.18 g C m
−2
over the sugarcane growing season while the values were −192.30, 621.47, and 488.34 g C m
−2
over the wheat growing season. The sesbania (green manure) appeared to be a carbon source once it is incorporated into soil. The response of day-time NEE to photosynthetically active radiation (PAR) under two vapor pressure deficit (VPD) sections (0–20 h Pa and 20–40 h Pa) seems more effective over sugarcane (
R
2
= 0.41–0.61) as compared to the wheat crop (
R
2
= 0.25–0.40). A decrease in net CO
2
uptake was observed under higher VPD conditions. Similarly, night-time NEE was exponentially related to temperature at different soil moisture conditions and showed higher response to optimum soil moisture conditions for sugarcane (
R
2
= 0.87, 0.33 ≤ SWC < 0.42 m
3
m
−3
) and wheat (
R
2
= 0.75, 0.31 ≤ SWC < 0.37 m
3
m
−3
) crop seasons. The response of daily averaged NEE to environmental variables through path analysis indicates that PAR was the dominant predictor with the direct path coefficient of −0.65 and −0.74 over sugarcane and wheat growing season, respectively. Satellite-based GPP products from Moderate Resolution Imaging Spectroradiometer (GPP
MOD
) and Vegetation Photosynthetic model (GPP
VPM
) were also compared with the GPP obtained from EC (GPP
EC
) technique. The seasonal dynamics of GPP
EC
and GPP
VPM
agreed well with each other. This study covers the broad aspects ranging from micro-meteorology to remote sensing over C4-C3 cropping system
Partitioning of net available energy into latent heat flux (
Q
le
), sensible heat flux (
Q
H
) is considered critical to hydrological cycle and water management. In this study, the eddy covariance ...technique was utilized to determine the sources of variation in net radiation and implications for corrections of the surface energy fluxes over a wheat crop along Indo-Gangetic Plains over the period 1 January 2015 to 30 April 2015. We found that the variation in net radiation was dependent on the diurnal variation in four radiation components (upwelling shortwave radiation, downwelling shortwave radiation, upwelling longwave radiation, and downwelling longwave radiation). Furthermore, the relationship of surface energy fluxes with different meteorological, soil, satellite-based parameters was determined using Pearson’s correlation analysis (significant at 0.05 level).
Q
le
was the main consumer of the available energy during the active growth period and was dominantly controlled by available net radiation. Out of all the factors under study, soil water content at a depth of 7.5 cm least affected
Q
le
with an insignificant correlation of about 0.23 on daily basis, while leaf area index was correlated with a significant correlation of 0.48. Sun-induced fluorescence dataset was also included as a global proxy for vegetation vigor and gross primary productivity (GPP) and has a positive and negative correlation with
Q
le
(0.26,
p
< 0.05) and
Q
H
(− 0.32,
p
< 0.05), respectively.
Crop mapping and acreage estimation are the simplest yet the most critical issues in agriculture. Remote sensing technology has been extensively used in the past few decades for executing these ...tasks. The objective of this study is to map sugarcane fields at a catchment level and segregate the plant and ratoon fields using the freely available Sentinel-1 and Sentinel-2 data. The study is carried out at the Kisan Sahkar Chini Mill catchment in the Saharanpur district of Uttar Pradesh. The objective was achieved by a two-step process where firstly the sugarcane fields are identified using Random Forest and SVM classifiers over temporal optical and microwave images. The most accurate result is used as a crop mask to separate the plant and ratoon fields. This was achieved by attempting a phenology based classification and spectral based classification. The results revealed that temporal Sentinel-2 data are highly competent in classifying sugarcane at farm level and segregating the plant and ratoon fields. The sugarcane crop mask was created with a kappa coefficient of 0.95 using the SVM classifier, and the plant and ratoon fields were discriminated using the Random Forest classifier with a kappa coefficient of 0.81. The sugarcane crop area was estimated to be approximately 535 acres of plant crop and 560 acres of the ratoon crop while the mill estimate was 520 acres and 540 acres, respectively. The results showed that Sentinel-2 has promising capabilities and is a convenient asset in delineating small-sized farms and classifying sugarcane and its crop types.
In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India ...exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing “crop management”, “soil management”, and “water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management.
Human life has come to a standstill when the countrywide lockdown was imposed to curb the infection of the highly contagious COVID-19 pandemic. Mass transportation and industrial activities ...were prohibited, which has proven advantageous for the environmental quality. This study was conducted to investigate the impact of lockdown on the environmental parameters measured over agricultural ecosystems in the Himalayan foothills Saharanpur Flux Site (SFS) and Palampur Flux Site (PFS), India. The UV–Aerosol Index obtained from the Sentinel-5 Precursor satellite falls drastically after imposing lockdown particularly in both the agricultural regions. It reached up to −3.082 and −3.522 during the first lockdown in SFS and PFS, respectively. Furthermore, due to the lesser availability of aerosols into the atmosphere, the availability of direct radiant energy increased, thus led to warming the earth’s surface. Improvement in direct radiant energy and normalized difference vegetation index positively increases the gross primary productivity in SFS and PFS.
The study explores the usefulness of infra-red CO
2
gas analyzer in capturing diurnal and seasonal variations in atmospheric CO
2
over three ecosystems, viz., mixed forest plantation (HFS), deciduous ...Sal forest (BFS), and sugarcane–wheat cropland (SFS). Half-hourly datasets of CO
2
and meteorological parameters collected in the respective study sites were analyzed to depict the diurnal, seasonal, and annual variation in atmospheric CO
2.
The influence of meteorological variables on atmospheric CO
2
concentration was depicted through biplot technique based on principal component analysis. Minimum and maximum CO
2
values observed in late-afternoon and pre-dawn period, respectively. Among all the sites under study, SFS exhibited the highest range 438.55 ppm (pre-dawn maximum) – 345.99 ppm (late-afternoon minimum) of CO
2
concentration. Minimum CO
2
concentration was observed in monsoon and post-monsoon seasons for HFS/BFS and SFS, respectively. Also, maximum “morning down-drop” was observed in monsoon and post-monsoon season in HFS/BFS and SFS, respectively which can be ascribed to rapid consumption of CO
2
concentration due to higher biomass. Highest CO
2
concentration was observed in SFS owing to the more anthropogenic emission in terms of biomass burning data obtained through long-term Fire Energetics and Emission Research version 1.0. The peak in normalized difference vegetation Index derived from MODIS satellite across three sites coincides with months of the lowest atmospheric CO
2
due to strong photosynthetic activity.
Crop yield prediction at regional levels is an essential task for the decision-makers for rapid decision making. Pre-harvest prediction of a crop yield can prevent a disastrous situation and help ...decision-makers to apply more reliable and accurate strategies regarding food security. With the advent in digital world, various advanced techniques are employed for crop yield prediction. Remote Sensing (RS) data with its capability to provide the synoptic view of the Earth’s surface, has numerous returns in the area of crop monitoring and yield prediction. This study provides as a review for the advanced techniques for crop yield prediction in India with RS data as a base. The advanced techniques like RS based statistical yield modelling, machine learning based yield modelling, semi-physical yield modelling are described in the current study. The assessment of the studies related to integration of RS data in crop simulation model is also described in a section. All the techniques involved in the current study show significant improvements in crop yield prediction, enabling the development of new agricultural applications in India.
Effective management of water resources is crucial for sustainable development in any region. When considering computer-aided analysis for resource management, geospatial technology, i.e., the use of ...remote sensing (RS) combined with Geographic Information Systems (GIS) proves to be highly valuable. Geospatial technology is more cost-effective and requires less labor compared to ground-based surveys, making it highly suitable for a wide range of agricultural applications. Effectively utilizing the timely, accurate, and objective data provided by RS technologies presents a crucial challenge in the field of water resource management. Satellite-based RS measurements offer consistent information on agricultural and hydrological conditions across extensive land areas. In this study, we carried out a detailed analysis focused on addressing agricultural water management issues in India through the application of RS and GIS technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we systematically reviewed published research articles, providing a comprehensive and detailed analysis. This study aims to explore the use of RS and GIS technologies in crucial agricultural water management practices with the goal of enhancing their effectiveness and efficiency. This study primarily examines the current use of geospatial technology in Indian agricultural water management and sustainability. We revealed that considerable research has primarily used multispectral Landsat series data. Cutting-edge technologies like Sentinel, Unmanned Aerial Vehicles (UAVs), and hyperspectral technology have not been fully investigated for the assessment and monitoring of water resources. Integrating RS and GIS allows for consistent agricultural monitoring, offering valuable recommendations for effective management.
Global climate change expected to exacerbate the temperature extremes and intensity of heat waves in recent decades. The terrestrial biosphere plays a crucial role in absorbing carbon from the ...atmosphere. Therefore, understanding how terrestrial ecosystems respond to extreme temperatures is essential for predicting land-surface feedbacks in a changing climate. In light of this, a study was conducted to assess the effects of 2022 heat wave March-May (MAM) on carbon and water vapour fluxes. This study utilized the measurements obtained from the eddy covariance tower mounted within the sugarcane agroecosystem. The study period (MAM) was characterized into three events: Heat wave event 1 (HE1), Heat wave event 2 (HE2), Non heat wave event (NHE). The variation in carbon and water vapour fluxes, along with meteorological variables, during these events in 2020 and 2022 was further analysed. Our findings indicate that the heat wave caused a decrease in net ecosystem exchange (NEE), leading to an increase in atmospheric CO2 concentration during HE1, HE2 compared to NHE. In HE1, maximum NEE in 2020 and 2022 was -19.15 µmol m-2 s-1 and -13.21 µmol m-2 s-1, respectively. Furthermore, the heat wave events led to a decrease in latent heat flux (LE) and sensible heat flux (H), with changes of up to 5% in LE and 57% in H compared to the same period in 2020. These results highlight the significant impact of the heatwave on both carbon and energy fluxes. Overall, the present study provides a valuable reference for further climate change analysis, specifically focusing on both carbon and energy fluxes within sugarcane ecosystem.
Accurate and quantitative regional estimates of the carbon budget require an integration of eddy covariance (EC) flux-tower observations and remote sensing in ecosystem models. In this study, a ...simple remote sensing driven light use efficiency (LUE) model was used to estimate the primary productivity for major cropping systems using multi-temporal satellite data over the Saharanpur district in India.
The model is based on radiation absorption and its conversion into biomass. The LUE model was implemented for major crop rotations derived from the time-series of Sentinel-2 and Landsat 8 with monthly satellite-based spatially explicit fields of photosynthetically active radiation (PAR), fraction of absorbed PAR (
fAPAR
) and down-regulated light use efficiency. Incident PAR and
fAPAR
were estimated on monthly basis from the ground-calibrated empirical equation using INSAT-3D insolation product and remote sensing–based vegetation indices, respectively. Spatial LUE maps created by down-regulating maximum LUE (EC tower-based) with water and temperature stressors derived from land surface water index (LSWI) and EC-based cardinal temperature, respectively. LUE-based modeled GPP over the sugarcane-wheat system was found higher than the rice-wheat system in Saharanpur district. This is because C4 crop (sugarcane) has very high photosynthetic efficiency compared to C3 crops (rice and wheat). Modeled GPP over the sugarcane-wheat system was found in good agreement with observed EC tower-based GPP (Index of Agreement = 0.93). Further regionally calibrated remote sensing–based LUE model well captures gross photosynthesis rates (GPP) over cropland ecosystem compared to globally modeled MODIS GPP product.
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