Assessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm,India

Evapotranspiration (ET) is a vital process in land surface atmosphere research. In this study, Surface Energy Balance Algorithm for Land (SEBAL) for the assessment of ET (for 23 December 2010, 8 January 2011, 24 January 2011, 9 February 2011, 25 February 2011, 29 March 2011 and 14 April 2011) from LANDSAT7-ETM+ and validation with Lysimeter data set is illustrated. It is based on the evaporative fraction concept, and it has been applied to LANDSAT7-ETM + (30 m resolution) data acquired over the Indian Agricultural Research Institute’s agricultural farm land. The ET from SEBAL was compared with Lysimeter ET using four statistical tests (root-mean-square error (RMSE), relative root-mean-square error (R-RMSE), mean absolute error (MAE), and normalized root-mean square error (NRMSE)), and each test showed a good correlation between the predicted and observed ET values. Results from this study revealed that the RMSE of crop-growing period was 0.51 mm d^?1 for ET_SEBAL, i.e. ET_SEBAL having good accuracy with respect to observed ET_Lysimeter. Results were also validated using R-RMSE test, which also proved that ET_SEBAL data are having good accuracy with respect to observed ET_Lysimeter as R-RMSE of crop-growing period is 0.19 mm d^?1. MAE (0.19), NRMSE (0.21) and r² (0.91) tests indicated that model prediction is significant, and model can be effectively used for the estimation of ET from SEBAL as input of remote sensing data sets. Finally, the SEBAL has been useful for remote agricultural land where ground-based data (Lysimeter data) are not available for daily ET (ET_24 h) estimation. The temporal study of the ET_24 h values analysed has revealed that the highest ET_24 h values are owing to the higher development (high greenness) of crops, whereas the lower values are related to the lower development (low greenness) or null crop.     

Yield Estimation Model and Water Productivity of Wheat Crop (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS

Crop yield estimation has an important role on economy development and its accuracy and speed influence yield price and helps in deciding the excess or deficit production conditions. The water productivity evaluates the irrigation command through water use efficiency (WUE). Remote sensing (RS) and geographical information system (GIS) techniques were used for crop yield and water productivity estimation of wheat crop (Triticum aestivum) grown in Tarafeni South Main Canal (TSMC) irrigation command of West Bengal State in India. One IRS P6 image and four wide field sensor (WiFS) images for different months of winter season were used to determine the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) for area under wheat crop. The temporally and spatially distributed spectral growth profile and AREASUM of NDVI (A_NDVI) and SAVI (A_SAVI) with time after sowing of wheat crop were developed and correlated with actual crop yield of wheat (Y_act). The developed relationships between A_SAVI and Y_act resulted high correlation in comparison to that of A_NDVI. Using the developed model the RS based wheat yield (Y_RS) predicted from A_SAVI varied on entire TSMC irrigation command from 22.67 to 33.13 q ha⁻¹ respectively, which gave an average yield of 26.50 q ha⁻¹. The RS generated yield based water use efficiency (WUE_YRS) for water supplied from canal of TSMC irrigation command was found to be 6.69 kg ha⁻¹ mm⁻¹.     

Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate

In the last two decades, a number of single-source surface energy balance (SEB) models have been proposed for mapping evapotranspiration (ET); however, there is no clear guidance on which models are preferable under different conditions. In this paper, we tested five models-Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Index (S-SEBI), Surface Energy Balance System (SEBS), and operational Simplified Surface Energy Balance (SSEBop)—to identify the single-source SEB models most appropriate for use in the humid southeastern United States. ET predictions from these models were compared with measured ET at four sites (marsh, grass, and citrus surfaces) for 149 cloud-free Landsat image acquisition days between 2000 and 2010. The overall model evaluation statistics showed that SEBS generally outperformed the other models in terms of estimating daily ET from different land covers (e.g., the root mean squared error (RMSE) was 0.74 mm day⁻¹). SSEBop was consistently the worst performing model and overestimated ET at all sites (RMSE = 1.67 mm day⁻¹), while the other models typically fell in between SSEBop and SEBS. However, for short grass conditions, SEBAL, METRIC, and S-SEBI appear to work much better than SEBS. Overall, our study suggests that SEBS may be the best SEB model in humid regions, although it may require modifications to work better over short vegetation.     

Spatial Variability and Precision Nutrient Management in Sugarcane

Investigations were carried out to develop precision nutrient management techniques for sugarcane. The study area (800 ha) comprised of Bijapur, Bilgi and Jamakhandi talukas that lie between 16° 34′–28° 10′ N latitudes and 75° 33′–75° 37′ E longitudes and located around Nandi Sahakari Sakkare Karkhane (NSSK) Niyamit, Galagali. The soils are medium to deep black with pH and EC ranging from 7.32 to 8.36 and 0.17 to 1.13 dS/m, respectively. The soils are low to medium in available nitrogen, medium in available phosphorus and high in available potassium content. Crop condition assessment was made through analysis of LISS-III satellite images using Erdas Imagine software. Fertigation with 300 kg N and 195 kg K per ha at fortnightly interval and soil application of 32 kg P per ha as basal, recorded higher sugarcane yield (167 Mg ha^?1) as compared to 124 Mg ha^?1 obtained with soil application of 250 kg N, 32 kg P and 156 kg K per ha and flood irrigation as per the package recommended by the University(POP). Fertigation of N and K at weekly interval recorded highest NDVI value (0.354) and soil application of nutrients as per POP resulted in the lowest NDVI of 0.219.     

农田水分生产率估算方法及应用

水分生产率是评价农业灌溉用水管理水平和节水效果的一个重要指标.本文利用遥感数据定量估算了流域平原区的蒸散量和干物质量,采用经验的收获指数计算了海河流域2003-2008多年平均水分生产率.通过在水资源三级分区的统计结果分析表明：遥感估算的水分生产率可以反映单方水产出的空间差异,海河流域平原平均水分生产率为0.99kg/m3,变化范围为0.02-2.13kg/m3,总体水平较低,南北差异较大。通过ET,产量和水分生产率关系的分析表明水分生产率与产量线性相关(R＝0.97),提高产量将一直是流域提高水分生产率的重要研究方向。水分生产率与产量随ET变化均为先线性增加后减小的特点,在两季作物区当年耗水量在335-575mm范围变化时,水分是影响产量和水分产出效益的关键因子,要着重提高中低产区域的水分生产率; 当耗水量超过575mm,水分不是唯一的限制因子,以提高作物的收获指数为重点。     

Assessing the performance of a large-scale irrigation system by estimations of actual evapotranspiration obtained by Landsat satellite images resampled with cubic convolution

Remote sensing techniques allow monitoring the Earth surface and acquiring worthwhile information that can be used efficiently in agro-hydrological systems. Satellite images associated to computational models represent reliable resources to estimate actual evapotranspiration fluxes, ET_a, based on surface energy balance. The knowledge of ET_a and its spatial distribution is crucial for a broad range of applications at different scales, from fields to large irrigation districts. In single plots and/or in irrigation districts, linking water volumes delivered to the plots with the estimations of remote sensed ET_a can have a great potential to develop new cost-effective indicators of irrigation performance, as well as to increase water use efficiency. With the aim to assess the irrigation system performance and the opportunities to save irrigation water resources at the “SAT Llano Verde” district in Albacete, Castilla-La Mancha (Spain), the Surface Energy Balance Algorithm for Land (SEBAL) was applied on cloud-free Landsat 5 Thematic Mapper (TM) images, processed by cubic convolution resampling method, for three irrigation seasons (May to September 2006, 2007 and 2008). The model allowed quantifying instantaneous, daily, monthly and seasonal ET_a over the irrigation district. The comparison between monthly irrigation volumes distributed by each hydrant and the corresponding spatially averaged ET_a, obtained by assuming an overall efficiency of irrigation network equal to 85%, allowed the assessment of the irrigation system performance for the area served by each hydrant, as well as for the whole irrigation district. It was observed that in all the investigated years, irrigation volumes applied monthly by farmers resulted generally higher than the corresponding evapotranspiration fluxes retrieved by SEBAL, with the exception of May, in which abundant rainfall occurred. When considering the entire irrigation seasons, it was demonstrated that a considerable amount of water could have been saved in the district, respectively equal to 26.2, 28.0 and 16.4% of the total water consumption evaluated in the three years.     

Assessing the supplementary irrigation for improving crop productivity in water stress region using spatial hydrological model

Water stress during crop cultivation due to inconsistent rainfall is a common phenomenon in maize growing area of Shanmuganadi watershed, located in the semi-arid region of southern peninsular India. The objective is to estimate the supplementary irrigation required to improve the crop productivity during water stress period. Spatial hydrological model, Soil and Water Assessment Tool, has been applied to simulate the watershed hydrology and crop growth for rabi season (October–February) considering the rainfed and irrigated scenarios. The average water stress days of rainfed maize was 60 days with yield of 1.6 t/ha. Irrigated maize with supplementary irrigation of 93–126 mm was resulted in improved yield of 3.8 t/ha with 28 water stress days. The results also suggest that supplemental irrigation can be obtained from groundwater reserves and by adopting early sowing strategy can provide opportunities for improving water productivity in rainfed farming.     

Predicting Wheat Grain and Biomass Yield Using Canopy Reflectance of Booting Stage

Field experiment was conducted in a sandy loam soil of Indian Agricultural Research Institute, New Delhi during the year 2011–13 to see the effect of irrigation, mulch and nitrogen on canopy spectral reflectance indices and their use in predicting the grain and biomass yield of wheat. The canopy reflectances were measured using a hand held ASD FieldSpec Spectroradiometer at booting stage of wheat. Four spectral reflectance indices (SRIs) viz. RNDVI (Red Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio) and WI (Water Index) were computed using the spectral reflectance data. Out of these four indices, RNDVI, GNDVI and SR were significantly and positively related with the grain and biomass yield of wheat whereas WI was significantly and negatively related with the grain and biomass yield of wheat. Calibration with the second year data showed that among the SRIs, WI could account for respectively, 85 % and 86 % variation in grain and biomass yield of wheat with least RMSE (395 kg ha^?1 (15 %) for grain yield and 1609 kg ha^?1 (20 %) for biomass yield) and highest d index (0.95 for grain yield and 0.91 for biomass yield). Therefore it can be concluded that WI measured at booting stage can be successfully used for prediction of grain and biomass yield of wheat.     

基于遥感技术的祖厉河流域LUCC与蒸散发研究

本文以陇中黄土高原的祖厉河流域为研究区,基于Landsat系列遥感影像,利用主成分变换方法,与地面温度和植被指数相结合,进行土地利用/土地覆盖提取研究,在此基础上,探讨祖厉河流域土地利用/土地覆盖变化(LUCC)及其驱动因素。利用基于TM影像的SEBAL(Surface Energy Balance Algorithm for Land)模型,从祖厉河流域三期影像提取了区域蒸散发通量。首先进行了NDVI、比辐射率和地面温度等地表参数的计算,然后反演了地表的净辐射、土壤热通量和感热通量,根据能量守恒最终获得日蒸散发量。结合气象观测数据反演作物系数,最后计算月蒸散发量。对同期的土地利用/土地覆盖数据和陆面蒸散发量进行对比分析,探讨了土地利用类型的变化对流域内能量和水分时空分异的影响。     

Comparing the spectral settings of the new generation broad and narrow band sensors in estimating biomass of native grasses grown under different management practices

The challenge of assessing and monitoring the influence of rangeland management practices on grassland productivity has been hampered in southern Africa, due to the lack of cheap earth observation facilities. This study, therefore, sought to evaluate the capability of the newly launched Sentinel 2 multispectral imager (MSI) data, in relation to Hyperspectral infrared imager (HyspIRI) data in estimating grass biomass subjected to different management practices, namely, burning, mowing and fertilizer application. Using sparse partial least squares regression (SPLSR), results showed that HyspIRI data exhibited slightly higher grass biomass estimation accuracies (RMSE = 6.65 g/m², R² = 0.69) than Sentinel 2 MSI (RMSE = 6.79 g/m², R² = 0.58) across all rangeland management practices. Student t-test results then showed that Sentinel 2 MSI exhibited a comparable performance to HyspIRI in estimating the biomass of grasslands under burning, mowing and fertilizer application. In comparing the RMSEs derived using wave bands and vegetation indices of HyspIRI and Sentinel, no statistically significant differences were exhibited (α = 0.05). Sentinel (Bands 5, 6 and 7) and HyspIRI (Bands 730 nm, 740 nm, 750 nm, 710 nm), as well as their derived vegetation indices, yielded the highest predictive accuracies. These findings illustrate that the accuracy of Sentinel 2 MSI data in estimating grass biomass is acceptable when compared with HyspIRI. The findings of this work provide an insight into the prospects of large-scale grass biomass modeling and prediction, using cheap and readily available multispectral data.     

Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM

Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R² = 0.65, whereas VWSI showed overestimated values with mean RD 4%.     

基于遥感的蒸散与土地利用及主要地表参数的关系研究——以乌裕尔河下游为例

以TM/ETM+影像为数据源,利用自动分类与目视解译相结合的方法提取了研究区1992～2001年共5个时相的土地利用信息;采用SEBAL模型估算影像过境当天的日蒸散量;最后对蒸散与土地利用变化、植被覆盖度及地表温度等地表参数之间的关系进行了相关性分析。结果表明:水体、湿地具有较高的日蒸散量,草地、旱地、林地次之,盐碱地、居民地最低。说明蒸散与地表温度、植被覆盖度等密切相关,土地利用变化是引起上述变化的主要驱动力之一。     

Sensing of moisture stress effects by infra-red thermometry in sorghum,maize and pearl millet

Field experiment was carried out on sandy loam soil with sorghum (cv. S-136), maize (cv. Ageti-76) and pearl millet (cv. PHB-14) during the summer season (may–July) of 1980 at Haryana Agricultural University Farm, Hisar. After one uniform irrigation at crop establishment, the crops were subjected to four irrigation treatments, viz. irrigation at ID/CPE (ID=irrigation depth of 7cm; CPE=cumulative pan evaporation) of 1.0, 0.6, 0.3 & 0.15. Changes in soil water potential (ψ soil), leaf water potential (ψ L), stomatal conductance (KL), canopy temperature (Tc), transpirational cooling (Canopy temperature minus air temperature, Tc-Ta), evapotranspiration (ET) and dry matter yields were recorded in different treatments. An increase in moisture stress resulted in a decrease in ψ soil, ψ L, KL, transpirational cooling, ET but increase in Tc. Tc-Ta showed significant curvilinear association with ψ soil and linear relationship with ψ L, KL, Tc, ET and dry matter yield of summer cereals. It is suggested that the mid day values of Tc-Ta as observed with an infra-red thermometer could effectively be used to sense the moisture stress effects in summer cereals.     

Application of Remotely-Sensed Data to Estimate a Water Budget for Data-Scarce Endorheic Basins: A Case Study of Lake Urmia basin,Iran

Estimating the water budgets of large basins is a challenge because of the lack of data and information. It becomes more complicated in endorheic basins that consist of separate land and water phases. The application of remotely-sensed data is one solution in this regard. The present study addresses this issue and develops a modeling framework to evaluate a water budget based on remotely-sensed data for endorheic basins. To explore the methodology, Lake Urmia basin was selected as a case study. The lake water level has declined steeply since 1995 and stakeholders have agreed to allocate 3100 MCM of water per year to the lake. This makes it necessary to monitor river inflow into the lake to fulfill the agreement. Gauging stations have been employed around the lake, but they could not account for shortages such as water uptake below the stations. To do this, separate water budgets for the water body and the land were required. More specifically, it was necessary to estimate actual evapotranspiration (ET _a ) from freshwater (E _f ) and saltwater (E _s ) estimated using the SEBAL model. Different methods were applied to estimate soil moisture, groundwater exploitation, and surface-groundwater inflow into the lake. A comparison of the observed and estimated amounts showed good agreement. For instance, the coefficient of determination for the observed/reported and estimated ET _a and E _f were 0.83 and 0.84, respectively. The average annual inflow was estimated to be 2.2 BCM/year for 2002–2008 using the RS model, which is about 84 % of the total inflow from the last recording stations before the lake and shows influence of water exploitation after these stations. Future study should focus on increasing temporal and spatial resolution of the method     

Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series

In the context of growing populations and limited resources, the sustainable intensification of agricultural production is of great importance to achieve food security. As the need to support management at a range of spatial scales grows, decision-support tools appear increasingly important to enable the timely and regular assessment of agricultural production over large areas and identify priorities for improving crop production in low-productivity regions. Understanding productivity patterns requires the timely provision of gapless, spatial information about agricultural productivity. In this study, dense 30-m time series covering the 2004–2014 period were generated from Landsat and MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images over the irrigated cropped area of the Fergana Valley, Central Asia. A light-use efficiency model was combined with machine learning classifiers to assess the crop yield at the field level. The classification accuracy of land cover maps reached 91% on average. Crop yield and acreage estimates were in good agreement (R² = 0.812 and 0.871, respectively) with reported yields and acreages at the district level. Several indicators of cropland intensity and productivity were derived on a per-field basis and used to highlight homogeneous regions in terms of productivity by means of clustering. Results underlined that regions with lower water-use efficiency were not only located further away from irrigation canals and intake points, but also had limited access to markets and roads. The results underline that yield could be increased by roughly 1.0 and 1.4 t/ha for cotton and wheat, respectively, if the access to water would be optimized in some of the regions. The minimum calibration requirement of the method and the fusion of multi-sensor data are keys to cope with the constraints of operational crop monitoring and guarantee a sustained and timely delivery of the agricultural indicators to the user community. The results of this study can form the baseline to support regional land- and water-resource management.     

灌区蒸散发遥感反演及其时空变化特征分析——以漳河灌区为例

准确反演区域尺度的蒸散发对于水资源循环、气候变化、科学灌溉及干旱与洪涝监测等方面的研究具有重要的科学意义,本文以漳河灌区为典型案例,构建了遥感蒸散发的SEBS模型,验证了其精度,分析了其时空规律。结果表明：①2009—2018年钟祥、荆门、团林站点的（蒸散发） ET观测值与模拟值的平均相关系数分别为0.86、0.84和0.83,RMSE平均值分别为0.61、0.67和0.62 mm/d。年际间的ET观测值与模拟值变化幅度均小于10%,相关系数范围为0.7～0.9,RMSE范围为0.4～0.8 mm/d,标准差比率范围为0.8～1.5。②研究区2009—2018年ET的平均值为427.34 mm,整体呈上升趋势。③年尺度、季尺度和月尺度ET与气温和降水在时间序列上均有较好的一致性。多时间尺度ET均呈东北和西南部高、西部低的空间分布特征。研究结果能够较好地呈现ET的高时空异质特征和年际间空间差异,本文将为漳河灌区水资源开发管理和科学灌溉提供科学依据。     

Full year crop monitoring and separability assessment with fully-polarimetric L-band UAVSAR: A case study in the Sacramento Valley,California

Spatial and temporal information on plant and soil conditions is needed urgently for monitoring of crop productivity. Remote sensing has been considered as an effective means for crop growth monitoring due to its timely updating and complete coverage. In this paper, we explored the potential of L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data for crop monitoring and classification. The study site was located in the Sacramento Valley, in California where the cropping system is relatively diverse. Full season polarimetric signatures, as well as scattering mechanisms, for several crops, including almond, walnut, alfalfa, winter wheat, corn, sunflower, and tomato, were analyzed with linear polarizations (HH, HV, and VV) and polarimetric decomposition (Cloude–Pottier and Freeman–Durden) parameters, respectively. The separability amongst crop types was assessed across a full calendar year based on both linear polarizations and decomposition parameters. The unique structure-related polarimetric signature of each crop was provided by multitemporal UAVSAR data with a fine temporal resolution. Permanent tree crops (almond and walnut) and alfalfa demonstrated stable radar backscattering values across the growing season, whereas winter wheat and summer crops (corn, sunflower, and tomato) presented drastically different patterns, with rapid increase from the emergence stage to the peak biomass stage, followed by a significant decrease during the senescence stage. In general, the polarimetric signature was heterogeneous during June and October, while homogeneous during March-to-May and July-to-August. The scattering mechanisms depend heavily upon crop type and phenological stage. The primary scattering mechanism for tree crops was volume scattering (>40%), while surface scattering (>40%) dominated for alfalfa and winter wheat, although double-bounce scattering (>30%) was notable for alfalfa during March-to-September. Surface scattering was also dominant (>40%) for summer crops across the growing season except for sunflower and tomato during June and corn during July-to-October when volume scattering (>40%) was the primary scattering mechanism. Crops were better discriminated with decomposition parameters than with linear polarizations, and the greatest separability occurred during the peak biomass stage (July-August). All crop types were completely separable from the others when simultaneously using UAVSAR data spanning the whole growing season. The results demonstrate the feasibility of L-band SAR for crop monitoring and classification, without the need for optical data, and should serve as a guideline for future research.     

Basin scale rainfall-evapotranspiration dynamics in a tropical semiarid environment during dry and wet years

In semiarid regions the occurrence of alternating long drought and heavy rainfall periods directly impacts water availability, affecting human water supply, agriculture development and the provision of ecosystem services. Because of that, research on the water input and output fluxes at the basin scale is of paramount importance. In this sense, rainfall-evapotranspiration (ET) dynamics play a critical role in water, soil and vegetation interactions, in hydrometeorological modelling and in the energy fluxes dynamics of semiarid regions. Therefore, the objective of this study was to quantify daily ET during a wet year and a dry year in a watershed located in the Brazilian Semiarid, by using remote sensing data and formulations based on the Simplified Surface Energy Balance Index (S-SEBI) and the Simplified Surface Energy Balance (SSEB) algorithms. Land surface temperature, albedo and NDVI data from MODIS sensor and solar radiation data from weather stations located in the basin were used. Rainfall analysis indicated 2009 and 2012 as being representatives of anomalously wet and dry years respectively, which were selected for the quantification of ET. The proposed algorithm was adjusted and verified with data from a flux tower equipped with eddy covariance system. Daily remote sensing ET estimates showed good agreement with observed values (RMSE = 0.79 mm.d⁻¹) and the annual ET relative error was of 7.7% (35.4 mm.year⁻¹). Results showed that the native vegetation can delay its dormant state for five months during wet years. During the wet year, ET differences between land cover classes were less noticeable due to soil saturation and the urgency of vegetated surfaces to meet their physiological needs. In dry year, however, differences were more evident, with bare soil presenting lower ET rates and vegetation classes showing higher ET values.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.