Integration of Sentinel 1 and 2 Observations for Mapping Early and Late Sowing of Soybean and Cotton Crop Using Deep Learning

Mohite, Jayantrao; Sawant, Suryakant; Pandit, Ankur; Pappula, Srinivasu

doi:10.1109/igarss39084.2020.9323482

Cited by 1 publication

(2 citation statements)

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“…Specifically for this last goal, beans are a crop that can contribute to reducing world hunger, lowering the total of 2795 million people who will suffer from hunger by the year 2050 [4]. According to [6][7][8][9], to achieve these five goals, considering the effects of climate change, the prediction of agricultural crop yield through multiple linear regressions (MLR) and essential climate variables is an efficient tool. There are 55 essential climate variables [10], of which certain variables stand out for being easy to obtain and important in agriculture: (1) average surface soil moisture (ASM), (2) cumulative effective precipitation (CEP), and air temperature [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…There are 55 essential climate variables [10], of which certain variables stand out for being easy to obtain and important in agriculture: (1) average surface soil moisture (ASM), (2) cumulative effective precipitation (CEP), and air temperature [11,12]. According to [13,14], from the air temperature it is possible to calculate (3) average maximum temperature (AMT), (4) maximum maximorum temperature (MMT), (5) average minimum temperature (AmT), (6) minimum minimorum temperature (mmT), (7) average mean temperature (AMeT), (8) maximorum mean temperature (MMeT), (9) degree days, and (10) cumulative reference evapotranspiration (CET), these nine essential climate variables being the main ones that affect crop yields. According to [15], the crops most sensitive to extreme essential climate variables conditions in Latin America are corn, wheat, and bean.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Yield of Irrigated and Rainfed Bean in Central and Southern Sinaloa State, Mexico, Based on Essential Climate Variables

Llanes Cárdenas,

Estrella Gastélum,

Parra Galaviz

et al. 2024

Atmosphere

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The goal was to model irrigated (IBY) and rainfed (RBY) bean yields in central (Culiacán) and southern (Rosario) Sinaloa state as a function of the essential climate variables soil moisture, temperature, reference evapotranspiration, and precipitation. For Sinaloa, for the period 1982–2013 (October–March), the following were calculated: (a) temperatures, (b) average degree days for the bean, (c) cumulative reference evapotranspiration, and (d) cumulative effective precipitation. For essential climate variables, (e) daily soil moisture obtained from the European Space Agency and (f) IBY and RBY from the Agrifood and Fisheries Information Service were used. Multiple linear regressions were significant for predicting IBY–RBY (dependent variables) as a function of essential climate variables (independent variables). The four models obtained were significantly predictive: IBY–Culiacán (Pearson correlation (PC) = 0.590 > Pearson critical correlation (CPC) = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|), and RBY–Rosario (PC = 0.532 > CPC = |0.349|). Due to the lack of irrigation depth data, many studies only focus on modeling RBY; this study is the first in Sinaloa to predict IBY and RBY based on essential climate variables, contributing to the production of sustainable food.

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