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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.

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 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

Filling in the void between forest ecology and remote sensing through monitoring biodiversity variables is of great interest. In this study, we utilized imaging spectroscopy data from the ISRO–NASA Airborne Visible InfraRed Imaging Spectrometer—Next Generation (AVIRIS-NG) India campaign to investigate how the measurements of biodiversity attributes of forests over wide areas can be augmented by synchronous field- and spectral-metrics. Three sites, Shoolpaneshwar Wildlife Sanctuary (SWS), Vansda National Park (VNP), and Mudumalai Tiger Reserve (MTR), spread over a climatic gradient (rainfall and temperature), were selected for this study. Abundant species maps of three sites were produced using a support vector machine (SVM) classifier with a 76–80% overall accuracy. These maps are a valuable input for forest resource management. Convex hull volume (CHV) is computed from the first three principal components of AVIRIS-NG spectra and used as a spectral diversity metric. It was observed that CHV increased with species numbers showing a positive correlation between species and spectral diversity. Additionally, it was observed that the abundant species show higher spectral diversity over species with lesser spread, provisionally revealing their functional diversity. This could be one of the many reasons for their expansive reach through adaptation to local conditions. Higher rainfall at MTR was shown to have a positive impact on species and spectral diversity as compared to SWS and VNP. Redundancy analysis explained 13–24% of the variance in abundant species distribution because of climatic gradient. Trends in spectral CHVs observed across the three sites of this study indicate that species assemblages may have strong local controls, and the patterns of co-occurrence are largely aligned along climatic gradient. Observed changes in species distribution and diversity metrics over climatic gradient can help in assessing these forests’ responses to the projected dynamics of rainfall and temperature in the future.

A field experiment was conducted in 2015 to study the land surface energy fluxes from tropical lowland rice paddy in eastern India with an objective to determine the mass, momentum, and energy exchange rates between rice paddies and the atmosphere. All the land surface energy fluxes were measured by eddy covariance (EC) system (make Campbell Scientific) in dry season (DS, 1–125 Julian days), dry fallow (DF, 126–181 Julian days), wet season (WS, 182–324 Julian days), and wet fallow (WF, 325–365 Julian days). The rice was cultivated in dry season (January–May) and wet season (July–November) in low wet lands and the ground is kept fallow during the remainder of the year. Results showed that albedo varied from 0.09 to 0.24 and showed positive value from morning 6:00 h until evening 18:00 h. Mean soil temperature (Tg) was highest in DF, while the skin temperature (Ts) was highest in WS. Average Bowen ratio (B) ranged from 0.21 to 0.64 and large variation in B was observed during the fallow periods as compared to the cropping seasons. The magnitude of aerodynamic, canopy, and climatological resistances increased with the progress of cropping season and their magnitudes decreased during the end of both cropping seasons and found minimum during the fallow periods. At a constant vapor pressure deficit (VPD) at 0.16, 0.18, 0.15, and 0.43 kPa, latent heat flux (LE) initially increased, but later it tended to level off with an increase in VPD. The actual evapotranspiration (ETa) during both the cropping seasons was higher than the fallow period. This study can be used as a source of default values for many land surface energy fluxes which are required in various meteorological or air-quality models for rice paddies. A larger imbalance of energy was observed during the wet season as the energy is stored and perhaps advected in the fresh water.

Remote sensing-based large-area crop disease discrimination plays a vital role in agricultural management to assess severity level of diseases to avoid economical losses to farming community. Over the period, various biotic stresses have emerged through disease and pest infestation. Among these, blast is one of the most disastrous disease of wheat. It is also called as leaf blast, collar rot node blast, spike blast or rotten neck blast depending on the portion of wheat infected. Wheat blast is caused by the fungal pathogen Magnaporthe oryzae and first discovered in Brazil, Bolivia and Paraguay in 1985. In 2017, wheat blast was emerged in Murshidabad district of West Bengal in India. This posed a serious threat to Indian food security. In this study blast disease over wheat crop is studied using ground based hyperspectral measurements at one nm interval and Resourcesat-2 AWiFS data for year 2017. Supervised classification with maximum likelihood classifier was used to generate wheat crop mask using AWiFS data. The ground based hyperspectral bands are further used derive four RS-2 AWiFS broad spectral bands and showed significant difference in healthy and infested plants in vegetative and advance vegetative stages in green, red, NIR and SWIR spectral bands. The 2-D scatter between vegetative indices such as NDVI and LSWI showed well-marked discrimination between healthy and infested wheat crop in all sites. These results are used to derive thresholds for NDVI and LSWI and translated in RS-2 AWiFS derived indices to generate disease severity at spatial scale over Murshidabad district of West Bengal. The distributed wheat severity map showed that 0.59 percent and 99.41 percent of total wheat area is healthy and blast infested respectively. This study suggests that wheat blast can be discriminated using RS-2, AWiFS broad bands at vegetative and advance vegetative stage with support of ground measured hyperspectral data and damage area can be mapped at spatial scale before final harvest.

The aim of this work is to compare three remote sensing based models: two contextual and one physically-based single-pixel model for the estimation of daytime integrated latent heat flux without the use of any ground measurements over Indian ecosystems. Satellite datasets from the MODIS sensors aboard the Terra and the Aqua satellites were used. The latent heat flux estimated from the remote sensing models was compared with that estimated from Bowen ratio energy balance towers at five sites in India. The root mean square error (RMSE) of the latent heat flux estimated from the contextual and the physically-based models was found to be in the order of 40 and 70 W m−2, respectively. The relatively inferior performance of the more complex physically-based model in comparison with the contextual models was found to be largely due to inaccurate parameterizations estimated only from remote sensing datasets without any ground data.

Under the present study estimation of high resolution soil moisture (SM) under Pan India mode using simple water balance method and from satellite data has been explored. It aims at the simple calculation of soil moisture followed by verification with ground truth data of SM on spatial and temporal scale (WC) as climatic input. The model has been verified for winter (January-February), pre-monsoon (March-May), monsoon (June-September) and post-monsoon (October-December) seasons of year 2013. The comparison of model estimates with the in-situ data from 17 ground stations (for 396 paired datasets) over different seasons produced a better correlation coefficient varying from 0.46 to 0.60. The spatial comparison of SM estimated from model and satellite SM for the monsoon season shows a greater degree of coherence over most parts of India. Model derived weekly gridded SM combined with higher resolution satellite SM could use simple formulation and minimum inputs in conjunction with geographic information system (GIS). The SM is calculated on weekly basis and using gridded rainfall, potential evapotranspiration (PET) and field capacity (FC) and wilting point be used for better accuracy of the proposed block level agrometadvisory services.

Remote sensing data from Indian geostationary satellites (Kalapana-1, INSAT 3A) were used for the first time for early warning of agricultural drought and forewarning of crop vigour. An Early warning indicator (EWI) was developed from operational product of rainfall and reference evapotranspiration from observations of Kalpana-1 very high resolution radiometer (VHRR). The effectiveness of EWI was evaluated for the two drought years (2009 and 2012). The positive correlation (r = 0.66–0.68 for 2009 and r = 0.64–0.70 for 2012) between the EWI in the month of June–July and standardized precipitation index-1 (SPI-1) averaged over administrative unit (called district) indicates that EWI can be used successfully for drought early warning. Lag-response behaviour between EWI and crop vigour in terms of normalized difference vegetation index (NDVI) and LAI (leaf area index) over cropland was studied. Systematic patterns emerged for 30 days lag period between negative EWI and NDVI at both grid-scale (0.25°) and at district level. Linear relations were found between 10-day EWI and NDVI or LAI at 30 days lag during June–July period. Linear models were developed to forewarn crop vigour which was validated with realized NDVI from INSAT 3A charge-coupled device (CCD) observations within 95% accuracy. The EWI is recommended as potential indicator for early-season agricultural drought assessment and can be used for sub-district scale with finer scale rainfall and evaporation products from advanced next-generation geostationary meteorological satellites.

The Indian and French Space Agencies, ISRO and CNES, have conceptualized a space-borne Thermal Infrared Reflectance (TIR) mission, TRISHNA (Thermal infRared Imaging Satellite for High-resolution Natural Resource Assessment). The primary design drivers of TRISHNA are the monitoring of (i) terrestrial water stress and use, and of (ii) coastal and continental water. A suit of four TIR bands and six optical bands is planned. The TIR bands will be centred at 8.6 μm, 9.1 μm, 10.3 μm and 11.5 μm to provide noon-night global observations at 57m nadir resolution over land and coastal regions. The field of view (FOV) is ±34° and the orbit of 761 km altitude was designed to allow 3 sub-cycle acquisitions during the 8-day cycle. The optical bands correspond to blue, green, red, and NIR plus two SWIR bands at 1.38 μm and 1.61 μm. The green, red, NIR and the 1.61 μm SWIR bands will have better radiometry quality than those of AWiFS. ISRO and CNES will develop optical and TIR payloads, respectively. Assessing evapotranspiration and furthermore Gross and Net Primary Productivity (GPP and NPP) will in turn assist in quantifying water use in rainfed and irrigated agriculture, water stress and water use efficiency, with expected applications to agricultural drought and early warning, crop yield prediction, water allocation, implementation of water rights, crop insurance business and agro-advisories to farmers. The other scientific objectives of TRISHNA are also briefly described. TRISHNA instrument will fly aboard a ISRO spacecraft scheduled to be launched from 2024 for a minimum period of 5 years’ mission lifetime.

International audience; The monitoring of the water cycle at the Earth surface which tightly interacts with the climate change processes as well as a number of practical applications (agriculture, soil and water quality assessment, irrigation and water resource management, etc...) requires surface temperature measurements at local scale. Such is the goal of the Indian-French high spatio-temporal TRISHNA mission (Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment). The scientific objectives of the mission and research work conducted to consolidate the mission specifications are presented. Progress in modelling of surface fluxes is then discussed. The main specifications of the mission such as the revisit, the spatial resolution, the overpass time, the spectral bands and the orbit are analyzed and justified. The resulting baseline of the mission is given.

Models are means to capture, condense and organize knowledge. These are expressions, which represent relationship between various components of a system. A well-tested weather-based model can be an effective scientific tool for forewarning insect-pests and diseases in advance so that timely plant protection measures could be taken up. Various types of techniques have been developed for the purpose. The simplest technique forms the class of thumb rules, which are based on experience. Though these do not have much scientific background but are extensively used to provide quick forewarning of the menace. Another tool in practice is regression model that represents relationship between two or more variables so that one variable can be predicted from the other (s). Linear and non-linear regression models have been widely used in studying relationship of insect-pests and diseases with time and weather variables (as such or in some transformed forms). With the advent of computers more sophisticated techniques such as simulation modelling and machine learning approach such as decision tree induction algorithms, genetic algorithms, neural networks, rough sets, etc. have been explored. A number of simulation models have been developed all over the world for quantifying effects of various factors including weather on agriculture. These may provide a good forecast but require detailed data base, which may not be available. Machine learning approach has recently received some attention. As opposed to traditional model-based methods, machine learning approach is self adaptive methods in that there are a few a priori assumptions about the models for problem(s) under study. This technique learns more from examples and captures subtle functional relationships among the data even if the underlying relationships are unknown or hard to describe. This modelling approach with ability to learn from experience is very useful for many practical problems provided enough data are available. Remotely sensed data can provide useful information relating to area under the crop and also the condition thereof. It has certain advantages over land use statistics due to multi-spectral, synoptic and repetitive coverage. An attempt has been made for accurate estimation of area affected by insect-pests and diseases in crops along with accurate assessment of damage due to the same are possible for providing compensation to farmers. In this study, an Integrated Decision Support System (IDSS) for Crop Protection Services is also discussed.

Morphological parameters like cotton height, branches, Leaf Area Index and biomass are mainly affected by the vegetation water content (VWC). Periodical assessment of the VWC and crop parameters is required for timely management of the crop for maximizing yield. The study aimed at using both optical and microwave remotely sensed data to assess cotton crop condition based on the above mentioned traits. Vegetation indices (VI) derived from ground based measurements (5 narrow band and 2 broad band VIs) as well as satellite derived reflectance (2 broad band VIs) were assessed. Regression models were derived for estimating LAI, biomass and plant water content using the ground based indices and applied to the satellite derived spectral index (from LISS-III) map to estimate the respective parameters. HH and HV polarization from RISAT-1 were used to derive Radar Vegetation Index (RVI). The coefficient of determination of the model for estimating LAI, biomass and vegetation water content of cotton with optical vegetation index as input parameter were found to be 0.42, 0.51 and 0.52, respectively. The correlation between RVI and plant height, date of planting in terms of the age of the crop and vegetation water content were found to range between 0.4 to 0.6. The fresh biomass from RVI showed spatial variability from 100 gm-2 to 4000 gm-2 while the dry biomass map derived from NDVI showed spatial variability of 50 to 950 g m-2 for the study area. Plant water content in the district varied from 65 to 85%. The correlation between optical vegetation index and RVI was not significant. Hence a multiple linear regression model using both optical index (NDVI and LSWI) and SAR index (RVI) was developed to assess the LAI, biomass and plant water content. The model showed a R2 of 0.5 for LAI estimation but not significant for biomass and water content. This study show cased the use of combined optical and microwave (C band) remote sensing for cotton condition assessment.

HighlightsIn-season agricultural area tracking at regular interval from geostationary satellite.Modelling of temporal profile of vegetation index spread across two consecutive agriculture seasons to track crop area.The crop area estimates and their frequent updates in an agricultural growing season are essential to formulate policies of country’s food security. A new methodology has been developed with high temporal vegetation index data at 1000 m spatial resolution from Indian geostationary satellite (INSAT 3A) to track progress of country-scale rabi (post-rainy) crop area in six agriculturally dominant states of India. The 10-day (dekad) maximum normalized difference vegetation index (NDVI) composite products at 0700 GMT (Greenwich Mean Time) were generated and used in the study. A cubic function was fitted to NDVI temporal profile on the training data-sets of 2009–2010. Model parameters were standardized over 40 agroclimatic subzones, which were used to estimate rabi crop area at 10-day interval in the...

Estimation of latent heat flux at the agricultural field scale is required for proper water management. The current generation thermal sensors except Landsat-8 provide data on the order of 1000 m. The aim of this study is to test three approaches based on contextual models using only remote sensing datasets for the disaggregation of latent heat flux over India. The first two approaches are, respectively, based on the estimation of the evaporative fraction (EF) and solar radiation ratio at coarser resolution and disaggregating them to yield the latent heat flux at a finer resolution. The third approach is based on disaggregation of the thermal data and estimating a finer resolution latent heat flux. The three approaches were tested using MODIS datasets and the validation was done using the Bowen Ratio energy balance observations at five sites across India. From the validation, it was observed that the first two approaches performed similarly and better than the third approach at all five sites. The third approach, based on the disaggregation of the thermal data, yielded larger errors. In addition to better performance, the second approach based on the disaggregation of solar radiation ratio was simpler and required lesser data processing than the other approaches. In addition, the first two approaches captured the spatial pattern of latent heat flux without introducing any artefacts in the final output.

The regional impacts of future climate changes are principally driven by changes in energy fluxes. In this study, measurements on micrometeorological and biophysical variables along with surface energy exchange were made over a coniferous subtropical chir pine (Pinus roxburghii) plantation ecosystem at Forest Research Institute, Doon valley, India. The energy balance components were analyzed for two years to understand the variability of surface energy fluxes, their drivers, and closure pattern. The period covered two growth cycles of pine in the years 2010 and 2011 without and with understory growth. Net short wave and long wave radiative fluxes substantially varied with cloud dynamics, season, rainfall induced surface wetness, and green growth. The study clearly brought out the intimate link of albedo dynamics in chir pine system with dynamics of leaf area index (LAI), soil moisture, and changes in understory background. Rainfall was found to have tight linear coupling with latent heat fluxes. Latent heat flux during monsoon period was found to be higher in higher rainfall year (2010) than in lower rainfall year (2011). Higher or lower pre-monsoon sensible heat fluxes were succeeded by noticeably higher or lower monsoon rainfall respectively. Proportion of latent heat flux to net radiation typically followed the growth curve of green vegetation fraction, but with time lag. The analysis of energy balance closure (EBC) showed that the residual energy varied largely within ±30% of net available energy and the non-closure periods were marked by higher rainspells or forced clearance of understory growths.

The present work discusses the development of a nonlinear data-fitting technique based on genetic algorithm (GA) for the prediction of routine weather parameters using observations from Agro-Met Stations (AMS). The algorithm produces the equations that best describe the temporal evolutions of daily minimum and maximum near-surface (at 2.5-meter height) air temperature and relative humidity and daily averaged wind speed (at 10-meter height) at selected AMS locations. These enable the forecasts of these weather parameters, which could have possible use in crop forecast models. The forecast equations developed in the present study use only the past observations of the above-mentioned parameters. This approach, unlike other prediction methods, provides explicit analytical forecast equation for each parameter. The predictions up to 3 days in advance have been validated using independent datasets, unknown to the training algorithm, with impressive results. The power of the algorithm has also been demonstrated by its superiority over persistence forecast used as a benchmark.

The skill of weather forecasts at high spatial resolution depends on accurate representation of land surface states at appropriate spatial and temporal scales that modulate flux partitioning in the numerical weather prediction models. In this study, the Weather Research and Forecasting (WRF) model is customized to assess the impact of land surface albedo (LSA) derived from Kalpana-1 Very High Resolution Radiometer (K1VHRR) in comparison to default monthly climatological albedo from the United States Geological Survey (USGS). A two-stage upscaling of ground-measured albedo from Agro-Met Stations is performed to derive K1VHRR LSA. This combines multispectral reflectance at intermediate scales from the Advanced Wide Field Sensor on board Resourcesat-2 at Low Earth Orbiting platform and the planetary (Earth-atmosphere system) albedo from Kalpana-1 visible band at Geostationary Earth Orbiting platform. Two separate experiments, with real-time K1VHRR LSA and USGS climatological LSA (CNT), are performed to evaluate the impact of real-time K1VHRR LSA on daily WRF model forecasts during July 2009. Additional experiments are performed to assess the impact of real-time and climatological K1VHRR albedo against USGS climatological albedo based experiment. Results show that real-time K1VHRR albedo improves the surface temperature, specific humidity, and wind speed forecasts as compared to CNT experiments. The impact of climatological and real-time K1VHRR LSA is small compared to the advantage of using K1VHRR over USGS. Moreover, real-time K1VHRR albedo has additional benefits to improve the representation of seasonal variability. Results show that the real-time K1VHRR LSA has slight positive impact on rainfall forecast.

The non-availability of high-spatial-resolution thermal data from satellites on a consistent basis led to the development of different models for sharpening coarse-spatial-resolution thermal data. Thermal sharpening models that are based on the relationship between land-surface temperature (LST) and a vegetation index (VI) such as the normalized difference vegetation index (NDVI) or fraction vegetation cover (FVC) have gained much attention due to their simplicity, physical basis, and operational capability. However, there are hardly any studies in the literature examining comprehensively various VIs apart from NDVI and FVC, which may be better suited for thermal sharpening over agricultural and natural landscapes. The aim of this study is to compare the relative performance of five different VIs, namely NDVI, FVC, the normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI), for thermal sharpening using the DisTrad thermal sharpening model over agricultural and natural landscapes in India. Multi-temporal LST data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors obtained over two different agro-climatic grids in India were disaggregated from 960 m to 120 m spatial resolution. The sharpened LST was compared with the reference LST estimated from the Landsat data at 120 m spatial resolution. In addition to this, MODIS LST was disaggregated from 960 m to 480 m and compared with ground measurements at five sites in India. It was found that NDVI and FVC performed better only under wet conditions, whereas under drier conditions, the performance of NDWI was superior to other indices and produced accurate results. SAVI and MSAVI always produced poorer results compared with NDVI/FVC and NDWI for wet and dry cases, respectively.

A study was conducted to understand the seasonal and annual energy balance behaviour of young and growing sub-tropical chir pine (Pinus roxburghii) plantation of eight years age in the Doon valley, India and its coupling with CO2 exchange. The seasonal cycle of dekadal daytime latent heat fluxes mostly followed net radiation cycle with two minima and range between 50–200 Wm-2 but differed from the latter during the period when soil wetness and cloudiness were not coupled. Dekadal evaporative fraction closely followed the seasonal dryness-wetness cycle thus minimizing the effect of wind on energy partitioning as compared to diurnal variation. Daytime latent heat fluxes were found to have linear relationship with canopy net assimilation rate (Y = 0.023X + 0.171, R2 = 0.80) though nonlinearity exists between canopy latent heat flux and hourly net CO2 assimilation rate . Night-time plant respiration was found to have linear relationship (Y = 0.088 + 1.736, R2 = 0.72) with night-time average vapour pressure deficit (VPD). Daily average soil respiration was found to be non-linearly correlated to average soil temperatures (Y = -0.034X2 + 1.676X – 5.382, R2 = 0.63) The coupled use of empirical models, seasonal energy fluxes and associated parameters would be useful to annual water and carbon accounting in subtropical pine ecosystem of India in the absence high-response eddy covariance tower.

Global dimming term was introduced in 1990s which means the decline in global radiation. This decline was reported in several studies across the world. In the present study time series analysis of global radiation (1960-2003), transmissivity (1960-2003) and bright sunshine hours (1973-2003) has been done over Nagpur during pre-monsoon (March to May) and monsoon seasons (June-September). A significant decrease in global radiation and transmissivity has been reported for both the seasons and it was higher during pre-monsoon compared to monsoon. Bright sunshine hour has also shown good agreement with the previous trend with a significant trend on pre-monsoon months only. Mann Kendall test was performed to confirm the significance of reported trend.

The time of forcing of spatial LAI to crop models at single or multiple stages is important to simulate crop biomass and yield in varying agro-climatic conditions and scales. The high temporal resolution (5-day) by Advanced Wide Field Sensor (AWiFS) on-board Resourcesat-1 Satellite IRS-P6 with 56 m spatial resolution and large swath (740 km) has substantially increased the availability of regional clear sky optical remote sensing data. The present study aimed at developing empirical vegetation index VI-LAI models for wheat using AWiFS optical data in four bands and in-situ measurements sampled over five different agro-climatic regions (ACRs) during 2005–2006 followed by validation during 2006–2007. While nonlinear relations exist for all the three normalized indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and Green NDVI, linear relation was the best fit for ratio vegetation index (RVI). Both NDVI and RVI models generally showed better correlation ranges (0.65–0.84 for NDVI and 0.37–0.76 for RVI) than other indices. The common NDVI-LAI model was found to produce lower root mean square errors (RMSE) between 0.5 and 1.1 from pooled model than those between 0.5 and 1.32 from regional models. The rate of substantial increase in errors from NDVI-LAI model (RMSE of modeled LAI: 0.85 to 1.28) as compared to RVI-LAI model (RMSE of modeled LAI: 1.12 to 1.17) at LAI greater than 3, than below 3 revealed the early saturation of NDVI than RVI. It is therefore recommended that LAI estimates can be used to force crop simulation model upto early vegetative stage based on NDVI and maximum vegetative to reproductive stages based on RVI.

Albedo determines radiation balance of land (soil-canopy complex) surface and influence boundary layer structure of the atmosphere. Accurate surface albedo determination is important for weather forecasting, climate projection and ecosystem modelling. Albedo-rainfall feedback relationship has not been studied so far using observations on spatial scale over Indian monsoon region due to lack of consistent, systematic and simultaneous long-term measurements of both. The present study used dekadal (ten-day) composite of satellite (e.g. NOAA) based Pathfinder AVHRR Land (PAL) datasets between 1981 and 2000 over India (68–100°E, 5–40°N) at 8 km spatial resolution. Land surface albedo was computed using linear transformation of red and near infrared (NIR) surface reflectances. The cloud effects were removed using a smoothening filter with harmonic analysis applied to time series data in each year. The monthly, annual and long term means were computed from dekadal reconstructed albedo. The mean per year and coefficient of variation (CV) of surface albedo over seventeen years, averaged over Indian land region, were found to show a significantly decreasing (0.15 to 0.14 and 60 to 40%, respectively) trend between 1981 and 2000. Among all the land use patterns, the inter-annual variation of albedo of Himalayan snow cover showed a significant and the steepest reducing trend (0.42 – 0.35) followed by open shurbland, grassland and cropland. No significant change was noticed over different forest types.. This could be due to increase in snow melting period and snow melt area. A strong inverse exponential relation (correlation coefficient r = 0.95, n = 100) was found between annual rainfall and annual albedo over seven rainfall zones. The decreasing trend in snow-albedo of accumulation period (September to March) follows the declining trend in measured south-west monsoon rainfall between 1988 (980 mm) to 1998 (880 mm) over India. This finding perhaps suggests the possible reversal of reported coupling of increased snowfall followed by lower monsoon rainfall.

Quantification of assured solar energy potential is essential to select locations for solar photovoltaic, thermal power plants and to quantify solar power potential. The use of remote sensing observations from geostationary satellite sensors is ideal to capture space-time variability of surface insolation. The annual clear solar energy exposure over India was determined using three years' insolation data at 8 km spatial resolution from Kalpana-1 satellite. High density solar energy pockets were diagnosed in western, central and southern India including Gujarat, Rajasthan, Madhya Pradesh, Karnataka, Tamil Nadu and Chhattisgarh states with annual solar energy exposure ranging from 2500 to 3500 kW h m -2 yr -1 .

[1] Evaporative fraction (EF) is a measure of the amount of available energy at the earth surface that is partitioned into latent heat flux. The currently operational thermal sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) on satellite platforms provide data only at 1000 m, which constraints the spatial resolution of EF estimates. A simple model (disaggregation of evaporative fraction (DEFrac)) based on the observed relationship between EF and the normalized difference vegetation index is proposed to spatially disaggregate EF. The DEFrac model was tested with EF estimated from the triangle method using 113 clear sky data sets from the MODIS sensor aboard Terra and Aqua satellites. Validation was done using the data at four micrometeorological tower sites across varied agro-climatic zones possessing different land cover conditions in India using Bowen ratio energy balance method. The root-mean-square error (RMSE) of EF estimated at 1000 m resolution using the triangle method was 0.09 for all the four sites put together. The RMSE of DEFrac disaggregated EF was 0.09 for 250 m resolution. Two models of input disaggregation were also tried with thermal data sharpened using two thermal sharpening models DisTrad and TsHARP. The RMSE of disaggregated EF was 0.14 for both the input disaggregation models for 250 m resolution. Moreover, spatial analysis of disaggregation was performed using Landsat-7 (Enhanced Thematic Mapper) ETM+ data over four grids in India for contrasted seasons. It was observed that the DEFrac model performed better than the input disaggregation models under cropped conditions while they were marginally similar under non-cropped conditions.

The availability of Airborne Visible and Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data has enormous possibilities for quantification of Leaf Chlorophyll Content (LCC). The present study used the AVIRIS-NG campaign site of Western India for generation and validation of new chlorophyll indices by denoising the AVIRIS-NG data. For validation, concurrent to AVIRIS-NG flight overpass, field samplings were performed. The acquired AVIRIS-NG was subjected to Spectral Angle Mapper (SAM) classifier for discriminating the crop types. Three smoothing techniques i.e., Fast-Fourier Transform (FFT), Mean and Savitzky-Golay filters were evaluated for their denoising capability. Raw and filtered data was used for developing new chlorophyll indices by optimizing AVIRIS-NG bands using VIs based on parametric regression algorithms. In total, 20 chlorophyll indices and corresponding 20 models were developed for mapping LCC in the area. SAM identified 17 crop types in the area, while FFT found to be the best for filtering. Performance of these models when checked based on Pearson correlation coefficient ( ${r}$ ) and Centered Root Mean Square Difference (CRMSD), indicated that LCC-CCI10 based on normalized difference type index formed through Near Infrared band and blue band is the best estimator of LCC $({r}_{\textit {cal}}=0.73,{r}_{\textit {val}}=0.66,CRMSD=4.97)$ . The approach was also tested using AVIRIS-NG image of the year 2018, which also showed a promising correlation ( ${r} =0.704$ , CRSMD = 8.98, Bias = −0.5) between modeled and field LCC.