Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India

Gumma, Murali Krishna; Kadiyala, M D M; Panjala, Pranay; Ray, S. S.; Akuraju, Venkata Radha; Dubey, S. K.; Smith, Ann C.; Das, Rajesh; Whitbread, Anthony

doi:10.1007/s12524-021-01341-6

Cited by 29 publications

(15 citation statements)

References 43 publications

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“…For assessing the accuracy of the yield estimates from the CASA model and the NPP-yield conversions, we used 30 CCE (crop cutting experiment) data points to verify the results. Each pixel around the CCE points was averaged with the eight corresponding pixels to attain the projected yield for the specified CCE point [67]. Figure 10 shows the assessed yield as a derivative of the estimated yield and the coefficient of determination R 2 = 0.5544, and the root mean square error (RMSE) was 3.361 Q/ha (Table 3).…”

Section: Wheat Yield Estimation Using the Casa Modelmentioning

confidence: 99%

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

et al. 2022

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Globally, estimating crop acreage and yield is one of the most critical issues that policy and decision makers need for assessing annual crop productivity and food supply. Nowadays, satellite remote sensing and geographic information system (GIS) can enable the estimation of these crop production parameters over large geographic areas. The present work aims to estimate the wheat (Triticum aestivum) acreage and yield of Maharajganj, Uttar Pradesh, India, using satellite-based data products and the Carnegie-Ames-Stanford Approach (CASA) model. Uttar Pradesh is the largest wheat-producing state in India, and this district is well known for its quality organic wheat. India is the leader in wheat grain export, and, hence, its monitoring of growth and yield is one of the top economic priorities of the country. For the calculation of wheat acreage, we performed supervised classification using the Random Forest (RF) and Support Vector Machine classifiers and compared their classification accuracy based on ground-truthing. We found that RF performed a significantly accurate acreage assessment (kappa coefficient 0.84) compared to SVM (0.68). The CASA model was then used to calculate the winter crop (Rabi, winter-sown, and summer harvested) wheat net primary productivity (NPP) in the study area for the 2020–2021 growth season using the RF-based acreage product. The model used for wheat NPP-yield conversion (CASA) showed 3100.27 to 5000.44 kg/ha over 148,866 ha of the total wheat area. The results showed that in the 2020–2021 growing season, all the districts of Uttar Pradesh had similar wheat growth trends. A total of 30 observational data points were used to verify the CASA model-based estimates of wheat yield. Field-based verification shows that the estimated yield correlates well with the observed yield (R2 = 0.554, RMSE = 3.36 Q/ha, MAE −0.56 t ha−1, and MRE = −4.61%). Such an accuracy for assessing regional wheat yield can prove to be one of the promising methods for calculating the whole region’s agricultural yield. The study concludes that RF classifier-based yield estimation has shown more accurate results and can meet the requirements of a regional-scale wheat grain yield estimation and, thus, can prove highly beneficial in policy and decision making.

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Section: Wheat Yield Estimation Using the Casa Modelmentioning

confidence: 99%

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

et al. 2022

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“…The challenge of crop discrimination and mapping in monsoon season overcome with a combined used of SAR and optical data. Gumma et al (2022) describe a multi-site study conducted in Kharif season covering rice, groundnut and maize crops and use of multi-date Sentinel-2 and Landsat-8 data. A large number of crops were mapped including rice, groundnut, cotton, maize, pigeon pea, millet based on the study area.…”

Section: Crop Yield Assessmentmentioning

confidence: 99%

Progress and Challenges in Earth Observation Data Applications for Agriculture at Field Scale in India and Small Farm Holdings Regions

Ray

Dadhwal

Navalgund

2022

J Indian Soc Remote Sens

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The paper traces the evolution of agricultural remote sensing in India through its four phases: initial exploratory and aerial data based (1969)(1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980)(1981)(1982), IRS Utilization Program led (1983( -1995( ), post-IRS-1C launch (1996( -2011 and since the establishment of a dedicated institution for EO-based crop forecasting and other agricultural applications (2012 onward). Published field-scale studies are discussed before introducing the submissions to the special issue of the journal, namely Advanced Geospatial Technologies for Agriculture with Special emphasis on field level Applications. A total of 17 articles grouped under crop discrimination and mapping, crop yield estimation, crop suitability analysis, irrigation management and others (UAV, fodder and field boundary extraction) and 15 of these describe work over India and remaining two have Iran as their study area. These studies are linked to other published work as well as challenges under major issues of data access, use of UAV, use of smartphones, data processing and crop simulation models. Taken together, these articles report substantial improvements in capabilities of field-scale monitoring for developing new class of agricultural applications in India as well as other regions in the world with small farm holdings.

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“…Advance estimations of crop productions are crucial for policymakers, as they enable them to prepare for crop procurement, distribution, determining price structure and strategizing import/export decisions [5]. For farmers, this helps to determine their optimum area allocation under different crops and ensure they can maximize their production and income [6]. Recent advancements in technology, data collection and computational efficiency have facilitated the design and implementation of big-data analytical approaches, which involve the use of historical crop data, satellite imagery, climate data and other relevant information to build a model that can forecast crop production at a given point in time.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

Gumma,

Nukala,

Panjala

et al. 2024

AgriEngineering

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This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.

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Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India

Cited by 29 publications

References 43 publications

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

Progress and Challenges in Earth Observation Data Applications for Agriculture at Field Scale in India and Small Farm Holdings Regions

Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

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