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The land surface is an essential component of the Earth's System that interacts with the atmosphere via mass, momentum, and energy exchange. Croplands are one of the most common types of land use. Therefore, a comprehensive understanding of land-atmosphere interactions requires understanding the biogeochemical and biogeophysical processes and interactions in agroecosystems.Earth System Models (ESMs) can simulate the complex physical, chemical, and biological processes within and between the earth's land, atmosphere, ocean, and other spheres. Croplands have not received adequate attention in ESMs and were previously represented as grasslands. Land components in ESMs, such as the Community Land Model version 5 (CLM5) in the Community Earth System Model (CESM), have recently begun to include specific crops. The addition of crops to land models improved the simulation of energy, carbon, and water fluxes from land. CLM5 can represent a wide range of crops all over the world. However, there are significant errors in crop representation for the Indian region, including cropping areas, cropping season, irrigation, and crop characteristics. CLM5's estimated annual yield of wheat and rice has significant biases compared to UN-FAO estimates due to differences in growing seasons. Furthermore, observational data on the phenology of spring wheat and rice are scarce in the Indian region. As a result, crop growth model simulations in the Indian region suffer from poor calibration and validation.India is the world's second-largest producer of wheat and rice. Rice and wheat croplands cover more than 70 million ha combined. The current study aims to improve CLM5's representation of spring wheat and rice crops. This is accomplished by incorporating a crop planting window based on observations, wheat and rice cultivated area and irrigated cropland maps from district-level data. To further improve the crop models, we digitized historical crop phenology data and used them for model calibration and validation.Correcting the spring wheat and rice growing seasons in CLM5 over India has greatly improved crop phenology, yield, and irrigation pattern. As a result, the energy, carbon, and water fluxes are better estimated than the default CLM5 model. If the improved CLM5 is incorporated into the CESM, this can also improve the simulation of atmospheric phenomena.

Methane (CH4) and nitrous oxide (N2O) emissions coupled with climate change are issues which are of great concern for modern rice cultivation. It is very difficult and costly affair to quantify the CH4 and N2O emissions at regional and national scales due to large scale spatial and temporal variability in soil and crop management practices. In this study, the denitrification and decomposition (DNDC) model was used for simulating the CH4 and N2O gas emissions from rice fields in Eastern India. For simulating the gaseous emission, the maps of pH, bulk density, soil organic carbon and clay content were prepared using geostatistics and ordinary kriging with study area divided into 1178 grids with an area of 32 ha for each grid. The maps along with other datasets used for running the DNDC model were compiled as model input parameter. The model was then applied for simulation of methane and nitrous oxide emissions from rice fields with various management practices. Simulated CH4 emission in the study area ranged from 9.38 to 110.63 kg C ha−1 and N2O emissions ranged from 0.01 to 1.82 kg N ha−1 as simulated by the DNDC for the crop growing season. Simulated CH4 emission ranged from 0.07 to 1.15 kgC/ha/day, whereas N2O reached upto 8.96 g N ha−1 day−1. The study suggested that the DNDC model can be used for estimating the CH4 and N2O emissions by capturing the information about the different crop management practices from rice fields.

One of the important variables in the retrieval of Land Surface Temperature (LST) is surface emissivity. The main aim of this study is to test how surface emissivity estimated using different metho...

Multi-spectral and diurnal meteorological data from optical-thermal observations of Indian Geostationary satellites were used to estimate country-scale evapotranspiration (ET). In this study radiat...

There is enormous scope and prospective of crop yield prediction at finer scale for both farm-level crop management as well as for crop insurance settlement at gram panchayat (GP) level in India. Now with the advent of satellite sensors like the MSI from Sentilnel-2 with fine spatial resolution, the possibility of generating frequent information on crop condition at field scale is increasing. This study demonstrated the combined use of high-resolution data from Sentinel-2 (10 m and 20 m); moderate-resolution data from MODIS (500 m) and coarser-resolution radiation data from INSAT-3D (4 km) for estimating yield of three major crops of India at GP and taluka level using a semi-physical model based on crop-specific radiation use efficiency. The novelty of this study lies in the data fusion approach using parameters from multiple spatial resolution of Geostationary and Lower Earth Orbiting satellites within the basic semi-physical model framework. The methodology has been demonstrated in Cuttack district of Odisha for rice; Rajkot district of Gujarat for cotton; and Indore district of MP and Fatehabad district of Haryana for wheat. We validated our result at GP, taluka and district level. At GP level, the root mean square error (RMSE) was found to be 16.5% for rice and 5.8% for wheat in Indore district. At taluka level, the RMSE was found to be 15%, 5.7%, 4.4% and 7.4% for rice, wheat in Indore district, wheat in Fatehabad district and cotton, respectively. The study concluded that high resolution remote sensing data would be of immense use for finer scale yield estimation, which can be aggregated at GP and taluka level with satisfactory accuracy (p = 95%).

Agricultural water demand (AWD) and irrigation water demand (IWD) were assessed (2009–2018) over India using geostationary and polar orbiting satellites. A novel concept of satellite based composit...

The present study focused on forest types classification, and major plant species assemblages in Mudumalai forests region utilizing Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG). The phytosociological analysis exhibited a total of 657 individuals (1095 individuals ha-1) of 24 tree species belonging to 22 genera, and 18 families. The highest tree density was contributed by Tectona grandis (132 individuals ha-1 and 12.05% of total tree density) followed by Anogeissus latifolia (105 individuals ha-1 and 9.59% of total tree density). The support vector machine study showed the dominance of Southern Tropical Semi-Evergreen forests (31%) followed by Southern Tropical Moist Deciduous forests (26.7%), and Southern Tropical Dry Deciduous forests (24.8%) with very high accuracy (92.37%). The comparative analysis of the existing forest types with Champion and Seth’s (1968) classification of forests exhibited a change of 30% in forest types in terms of their structure, composition, and extent over a period of 50 years. The spectral angle mapper-based study emphasized the defining role of elevation, rainfall, and temperature in species distribution, and physiognomy with dominance of A. latifolia (∼19.22%). The study implies the high spectral fidelity of airborne images for forest type mapping, and plant species discrimination in tropical forests.

Feed and fodder comprises about 65% of the cost of milk production of a dairy industry. It is a crucial input for enhancing the milk production. To address the issue of fodder availability at first, its assessment is required. Thus, we have implemented remote sensing technique for fodder crop assessment at state level to create a baseline for fodder crop availability for dairy managers to plan for its procurement during deficit and for better management purposes during its excess. We have devised a technique for remote sensing-based fodder crop assessment based on spectral pattern of growth, i.e. normalised difference vegetation index profile and land surface wetness index profile of series of IRS LISS-III satellite data taken during the crop growth cycle for a hybrid method of crop classification. Second objective to address the issue of mitigating the deficit of fodder crops, we have demonstrated the satellite derived intersection of probable high soil wetness area and available current fallows during a crop growing season which can be utilised for growing fodder crops. For macro-level planning in a state for developing new fodder-growing areas, we have demonstrated the availability of soil wetness factor from SMAP data. Fallow land available between two cropping seasons can be identified through remote sensing for growing short duration fast growing fodder crops. This project has been a demonstration project for AMUL in Gujarat to implement it subsequently at national level.

Present study was carried out to develop multiple linear regression (MLR) model of surface CH4 flux emission from monthly atmospheric clearness index (8 km), day-night land surface temperature (LST) at 1 km and surface soil moisture (25 km) from Kalpana-1, MODIS TERRA and GCOM-W1 satellites, respectively. All these products were aggregated to GOSAT level-4A product resolution. 2° × 2° grids representing homogeneous agro-ecosystems were used to draw data samples. Initial results showed that methane flux (from GOSAT) produced significant coefficient of determination (R2 = 0.84) with tri-variate (LST, surface soil moisture and atmospheric transmissivity) as compared to bi-variate (LST-soil moisture, LST-atmospheric transmissivity, soil moisture-atmospheric transmissivity) MLR models. These have been utilised for predicting surface methane flux for monthly scale. Validation of predicted methane flux with actual GOSAT methane flux was carried out and RMSE of 4.2–15.9% was obtained using variance-based bias correction. All these scaling models may be utilised to predict CH4 flux at regional level using high-resolution LST from thermal remote sensing and soil moisture from Synthetic Aperture Radar.

Accurate estimation of crop evapotranspiration (ET) is a key factor in crop water scheduling. The objective of this study was to estimate ET from the high-resolution satellite remote sensing data with integration of in situ observation. The surface energy balance model, Mapping Evapotranspiration with Internalized Calibration (METRIC) was utilised in this study for its simplicity, advantages, and effectiveness. It is a one-source model, which calculates the net radiation, soil heat flux, and sensible heat flux at every pixel level, and estimates the latent heat flux as the residual term in that energy budget equation. Intermediate steps like calculation of NDVI, surface temperature, and albedo served as important input parameters for ET estimate. Landat-8 satellite images were used to compute the ET in paddy field near CRRI, Cuttack, Odisha state in eastern India. Results indicated that the METRIC algorithm provided reasonably good ET over the study area with marginal overestimation in comparison to field observation by eddy covariance data. The satellite-based ET estimates represented in spatial scale has potential in improving irrigation scheduling and precise water resource management at local scales.

Synergistic use of Optical and Microwave remotely sensed data for cotton condition based on biophysical traits, indices from ground measurements and satellite derived reflectance were assessed. Reg...

Airborne Visible InfraRed Imaging Spectrometer — Next Generation (AVIRIS-NG) data with high spectral and spatial resolutions are used for mapping metasediments in parts of Banswara district, Rajasthan, India. The AVIRIS—NG image spectra of major metasedimentary rocks were compared with their respective laboratory spectra to identify few diagnostic spectral features or absorption features of the rocks. These spectral features were translated from laboratory to image and consistently present in the image spectra of these rocks across the area. After ensuring the persistency of absorption features from sample to image pixels, three AVIRIS—NG based spectral indices is proposed to delineate calcareous (dolomite), siliceous (quartzite) and argillaceous (phyllite) metasedimentary rocks. The index image composite was compared with the reference lithological map of Geological Survey of India and also was validated in the field. The study demonstrates the efficiency of AVIRIS — NG data for mapping metasedimentary units from the Aravalli Supergroup that are known to host strata bound mineral deposits.

The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error 3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio–temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

The fragmented nature of arable landscapes and diverse cropping patterns often thwart the precise mapping of crop types. Recent advances in remote-sensing technologies and data mining approaches of...

The present study focuses on the soil moisture estimation using dual polarimetric RISAT-1, C-band SAR data in HH and HV polarization. The semi empirical approach derived by Dubois and the same modi...

Rice crop monitoring and yield prediction at country-scale can be effectively done using high-repeat active microwave remote sensors due to its all-weather observation capability of land surface. The Ku-band with a frequency of 13.515 GHz has the ability to interact with the surface layer and hence is useful for providing information on top portion of canopy and hence the grain and awns of rice crop. Also it has the capability to generate information over the whole region of South Asia in one day. Hence the present study was carried out to explore the super resolved Ku band back scattering coefficient from space borne scatterometer (SCATSAT-1) for rice productivity assessment over six Asian countries (India, Pakistan, Nepal, Bangladesh, Myanmar and Sri Lanka). The super resolved sigma-0 in both polarization (H and V) for the kharif rice season of 2017 (May to Mid-Nov) was used for this study. The temporal backscatter was used to generate rice planting date using polynomial fitting. Multiple regression models were developed using the daily SH/SV ratio and the farm-level fresh paddy yield collected through the Crop Cutting Experiment (CCE). The validation of the model was done for India at state level. For other countries national average reported yield was compared with the estimated yield. The rice planting date was found to vary from first week of June to last week of August in different parts of the six countries. Country average yield was found to vary from 3.45 t ha−1 in Sri Lanka to 4.32 t ha−1 in Myanmar. The absolute difference was lowest in India (8 %) followed by Sri Lanka (−11 %) and maximum in Nepal (35 %). In Indian states, the validation results showed a correlation coefficient of 0.95 at state level with a RMSE of 0.28 t ha−1 (11.4 % of mean reported yield). This study showed the possibility of using high frequency and high resolution Ku-band back scattering coefficient for rice grain yield estimation at continental scale such as Asia. The yield estimation can be further improved with the use of country-wise crop cutting data for model development and validation.

Growth and performance of various sectors of a country largely depends on the infrastructure developed by the country. It is true about the agricultural sector also. Along with the numbers, the location of these infrastructure play a vital role in reducing cost to the farmers while utilizing the existing resources like land, water, electricity and road network to their optimum level. India is the second largest producer of fruits, vegetable and tea. Against the total production of more than 500 MMT of horticultural & non-horticultural produces, the total cold storage capacity is estimated at approximately 31 MMT, which is not even 10% of the total production. In view of this, a site suitability study was conducted to develop an optimum plan to locate cold storages in the state of Uttar Pradesh, a leading potato growing state. Factors considered for evolving the plan includes the site of production, i.e. region where potato is grown, road network, existing cold storage, electricity and land use. When there are various factors behind any decision-making process, it becomes a complex process. It becomes difficult to decide which factor is to be given more importance and how other factors can be compounded with each other to arrive at a decision. Analytical Hierarchy process (AHP), helps us in determining the relative importance of various factors to arrive at a decision. In view of infrastructure planning, an illustrative map indicating spatial features is vital. As is widely established, remote sensing and GIS helps to obtain such a spatial map. Remote sensing and GIS was used to generate maps of the factors involved. AHP was used to develop weights of the factors in relation to each other to generate a map showing site suitability of places for developing cold storage facilities.

The present study deals with analyzing forest health, its parameters, and suitability of hyperspectral data for vegetation health-related studies. Sholayar reserve forest in Kerala has a huge reserve of equatorial moist evergreen forest and demands preservation in every respect. Due to increased human interferences coupled with possible climate change, its health is undergoing a stage of deterioration. Stress levels in the canopy were assessed using a number of stress-related pigments. Detailed study of vegetation response to canopy leaf pigments have been carried out in the study. Airborne Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) data provides immense possibilities to study a number of stress-related pigments like anthocyanin, carotenoid, lignin, chlorophyll-a, b etc. Dominant species in these forests are Holigarna arnottiana, Grevillea robusta, Grewia tiliifolia, Syzygium cumini, Alstonia Scholaris, Cinnamomum verum, Artocarpus heterophyllus, Bischofia javanica, Mangifera indica, Bombax ceiba, Anogeissus latifolia, Terminalia paniculata etc. Apart from luscious natural vegetation, plantation of teak (Tectona Grandis), rubber (Hevea brasiliensis), tea (Camellia sinensis), Coffee (Coffee Arabica), Palm-Oil tree (Elaeis guineensis) etc. also exists. Field data pertaining to one of the selected pigments was correlated with remotely sensed pigment estimates. Correlation of field measured chlorophyll concentration and EVI showed R2 = 0.421. Similarly, the anthocyanin index showed a correlation of R2 = 0.319. In the Sholayar Reserve Forest (493.0 km2) an area of 141.0 km2 was found to be in a healthy state. Whereas about 218.0 km2 of area exhibit moderately healthy condition and 77.0 km2 area was in the least healthy state.

Net surface radiation defines the availability of radiation energy on and near the surface to drive many physical and physiological processes such as latent heat, sensible heat fluxes, and evapotranspiration. One of the prime challenges of modeling radiation budget is estimation of net longwave radiation. Incoming or downwelling longwave radiation (LWin) flux is one of the two key components of net longwave radiation. Its estimation in cloudy conditions has always been a challenge due to lack of instrumentation and regular measurements at different spatial scales. In this study, two artificial neural network (ANN) multi-layer perceptron (MLP) models were developed for LWin flux estimation under cloudy-sky during daytime and nighttime using half-hourly flux measurements over different agro-climatic settings and several atmospheric parameters from measurements, satellite-based observations, and model outputs. A comparative evaluation was made between existing or newly developed multivariate linear regression (MVR) models and ANN-based models. The latter set of models were found to be superior to the best MVR model during both daytime and nighttime. The ANN models were found to have consistent performance across different sites and cloud types except less accuracy in sub-humid or humid climate and in deep convection cloud. The ANN models showed overall accuracies of 2.7% and 3.3% of measured mean and R2 of 0.86 and 0.85 for daytime and nighttime, respectively, when compared with independent data of in-situ measurements.

The COVID 19 pandemic led to lockdown and restrictions on anthropogenic activities not only in India but all over the world. This provided an opportunity to study positive effects on environment and subsequent impact on terrestrial ecosystems such as urban, peri-urban, forest and agriculture. A variety of studies presented so far mainly include improved air quality index, water quality, reduced pollutants etc. The present study focused on few novel parameters from both polar and geostationary satellites that are not studied in context to India, and also attempts deriving/quantifying benefits rather than merely indicating qualitative improvements. Due to lack of anthropogenic activities during complete lockdown-1 (21 days from 25 March 2020) in India nighttime cooling of land surface temperature (LST) of the order of 2 to 6 K was observed. Amongst 10 major cities, Bhopal showed highest nighttime cooling. The cooling effect in LST was evident in 80% of industrial units distinctly indicating cooling trend. Vegetation fires were analyzed in 10 fire-prone states of India. Compared to past fouryear average number of occurrences, only 45% fire occurrences occurred during lockdown, indicating strong effect of lockdown. The study also revealed that, there is increase in gross primary production in forest ecosystem to the tune of maximum 38%, during this period. Though delay in rabi crop harvest date by 1 to 2 weeks in majority of north Indian states was observed rise in rabi crop productivity of the order of maximum 34% was observed which is attributed to favorable environmental conditions for net carbon uptake. About 18% reduction in volumetric agricultural water demand was estimated in Indo-Gangetic region, parts of Gujarat and Rajasthan. Apart from controlling the spread of the disease, the lockdown restrictions were thus also able to show positive effects on the environment and ecosystem which might influence to rethink on strategies for sustainable development., Graphical abstract Image 1

Net surface radiation defines the availability of radiative energy on and near the surface to drive many physical, physiological and eco-hydrological processes such as latent heat, sensible heat fluxes and evapotranspiration. Incoming longwave radiation (LWin) is one of the key components of net longwave radiation. One of the prime challenges of modelling radiation budget is estimation of surface incoming longwave radiation. Estimation of incoming longwave radiation in cloudy and foggy conditions has always been a challenge due to the lack of instrumentation and regular measurements at different spatial and temporal scales. In this study, two neural network models (daytime and night-time) were developed for estimation of incoming longwave radiation under foggy sky using half-hourly LWin, and other meteorological parameters such Ta, RH etc. The model provided high correlation of 0.85 (daytime) and 0.86 (night-time) with Root Mean Square Error (RMSE) of 4.9% for both daytime and night-time.