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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.

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.

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.

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.