Owing to its severe effect on productivity of rain-fed crops and indirect employment as well per capita income, agricultural drought has become a prime concern worldwide. The occurrence is mainly climatic phenomenon which cannot be eliminated. However, effects can reduced if actual spatio-temporal information related crop status available the decision makers. present study attempts assess efficiency remote sensing GIS techniques for monitoring extent drought. In study, NOAA-AVHRR NDVI data...

High‐resolution soil moisture holds the key to improving weather forecast, drought monitoring and hydrological modelling. Therefore, present study investigates potential of temperature/vegetation dryness index (TVDI) from MODIS assess status in sub‐humid parts India (western Uttar Pradesh). The TVDI was calculated by parameterizing normalized difference vegetation index–surface temperature space 8 day reflectance surface products. Correlation regression analysis carried out relate against...

Abstract In the present study, prediction of agricultural drought has been addressed through yield using a model based on NDVI-SPI. It observed that meteorological index SPI with different timescale is correlated NDVI at lag. Also current fortnight previous lags. Based correlation coefficients, Multiple Regression Model was developed to predict NDVI. The found highly in semi-arid and transitional zones. between crop highest first August. RMSE predicted year be about 17.07 kg/ha which 6.02...

Plant functional types (PFTs) have been widely used to represent the vegetation characteristics and their interlinkage with surrounding environment in various earth system models. The present study aims generate a PFT map for Northwest Himalayan (NWH) foothills of India using seasonality parameters, topographic conditions, climatic information from satellite data products Random Forest (RF) algorithm Google Earth Engine (GEE) platform. was extracted by carrying out harmonic analysis...

Climate change has become a serious concern worldwide owing to its multifaceted impact upon the physical as well socio‐economic environment (IPCC, 2013). Vulnerability climate is much higher in developing countries like India, where economy mainly agro‐based and productivity from agricultural sector dependent summer monsoon rainfall. Hence, assessing quantitative relationship between vegetation patterns climatic influence an increasingly important study conducted on regional global scales....

Forest plays a significant role in regulating the carbon budget and mitigating climate change long term. However, lack of spatially explicit accurate information on exchange components from diverse forest ecosystem types India limits budgeting regional scale. Remote-sensing-driven models are well-established tools for estimating gross primary productivity (GPP) over large areas but they seldom found erroneous if implemented without proper calibration biome-specific parameters. The present...

Soil moisture is one of the key state variable to regulate water and energy budgets at soil-plant-atmosphere continuum. This study demonstrates utility Vegetation Temperature Condition Index (VTCI), developed from interpretation NDVI–Ts space using MODIS, estimate soil status a spatial resolution 1-km in Rajasthan province. The findings reveal that VTCI computed for whole image (Method-1) was found be more representative average profile condition than other methods. Statistical regression...

The Moderate Resolution Imaging Spectroradiometer (MODIS) has provided an improved capability for moderate resolution land surface monitoring and studying temperature variations. Surface is a key variable in the energy balance. To investigate temporal variation of relation to different vegetation types, MODIS data from 2000–04 were used, especially reproductive phase crops (September–October). types used this study agriculture desert areas, rainfed agriculture, irrigated forest. We found...

Abstract Adequate knowledge about consumptive use of water (actual evapotranspiration, ET) by wheat on a regional scale is prerequisite in the intensively irrigated region western Uttar Pradesh, India. Satellite observations supplemented with routine meteorological data provide unique capability to estimate actual evapotranspiration over large areas needed for irrigation management and balance. The surface energy balance algorithm land (SEBAL) was applied time series MODIS level 3...

Net Primary Productivity (NPP) is a significant biophysical vegetation variable to understand the spatio-temporal distribution of carbon and source-sink nature ecosystem. This study was carried out in forest plantation area aimed (i) estimate patterns NPP during 2009 2010 using Carnegie-Ames-Stanford Approach [CASA] model (ii) effects climate variables on generalized linear modelling (GLM) approach. The total annual varied from 157.21 1030.89 gC m−2 yr−1 for year 154.36 1124.85 g C 2010....

Estimation and monitoring of crop evapotranspiration (ETc) or consumptive water use over large-area holds the key to irrigation management plans regional drought preparedness. The objective this study was estimate ETc by applying simplified-surface energy balance index (S-SEBI) model Landsat-8 data for 2014–2015 period in parts North India. An average estimated 2.72 2.47 mm day−1 with 0.22, 0.18 standard deviation 0.11, 0.07 error Kharif Rabi crops, respectively. On validation part, a close...

This paper presents a new approach to estimate spatial Sun-Induced Fluorescence (SIF) using the empirical relationship between simulated Canopy Chlorophyll Concentration (CCC) and SIF. PROSAIL model [PROpriétésSPECTrales (PROSPECT) Scattering by Arbitrarily Inclined Leaves (SAIL) models] was used simulate CCC. CCC maps were generated through an Automated Radiative Transfer Model Operator (ARTMO) Sentinel-2 Multi-Spectral Imager (MSI) imagery. The Soil Observation, Photochemistry, Energy...

Recently evolved satellite-based sun-induced fluorescence (SIF) spectroscopy is considered as a direct measure of photosynthetic activity vegetation.We have used monthly averages SIF retrievals for three agricultural year cycles, i.e.May to April each the years, viz.2007-08,